diff --git a/.gitignore b/.gitignore index 1c17aa0..dbb2df9 100644 --- a/.gitignore +++ b/.gitignore @@ -71,6 +71,7 @@ target/ # Jupyter Notebook .ipynb_checkpoints +Untitled.ipynb # pyenv .python-version @@ -105,4 +106,14 @@ venv.bak/ data/* hyperparameter_tuning/.azureml/* -outputs/* \ No newline at end of file +outputs/* + +# AzureML +.azureml/ +*.pkl +config.json +*.log* +.DS_Store + +# VSCode +.vscode/ \ No newline at end of file diff --git a/0_data_setup.ipynb b/0_data_setup.ipynb index 60b3b0d..433248b 100644 --- a/0_data_setup.ipynb +++ b/0_data_setup.ipynb @@ -22,9 +22,12 @@ "outputs": [], "source": [ "import os\n", + "import pandas as pd\n", "import matplotlib.pyplot as plt\n", - "from common.utils import load_data\n", - "from common.extract_data import extract_data\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", "%matplotlib inline" ] }, @@ -34,36 +37,76 @@ "source": [ "Download the data. \n", "\n", - "(Note: The following code is designed to run on Azure Notebook. If you are using this notebook in a different environment, you will need to mofidy the code.)" + "(Note: The following code is designed to run on an Azure Notebook (or Unix-like environment). If you are using this notebook in a different environment, you will need to mofidy the code.)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nothing to do\n" + ] + } + ], "source": [ "data_dir = './data'\n", "\n", - "if not os.path.exists(os.path.join(data_dir, 'energy.csv')):\n", + "# If data not downloaded, do so\n", + "if not os.path.exists(os.path.join(data_dir, 'GEFCom2014.zip')):\n", + " os.makedirs(data_dir, exist_ok=True)\n", " # Download and move the zip file\n", " !wget https://www.dropbox.com/s/pqenrr2mcvl0hk9/GEFCom2014.zip\n", " !mv GEFCom2014.zip ./data\n", - " # If not done already, extract zipped data and save as csv\n", - " extract_data(data_dir)" + "else:\n", + " print(\"Nothing to do\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Load the data from csv into a Pandas dataframe" + "Extract zip file." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nothing to do\n" + ] + } + ], + "source": [ + "# If not done already, extract zipped data and save as compressed csv\n", + "from common.extract_data import extract_data\n", + "\n", + "if not os.path.exists(os.path.join(data_dir, 'energy.csv.gz')):\n", + " extract_data(data_dir)\n", + "else:\n", + " print(\"Nothing to do\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data from csv into a Pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { @@ -86,53 +129,120 @@ " \n", " \n", " \n", + " timestamp\n", " load\n", + " temp\n", " \n", " \n", " \n", " \n", - " 2012-01-01 00:00:00\n", + " 0\n", + " 2012-01-01 00:00:00\n", " 2698.0\n", + " 32.000000\n", " \n", " \n", - " 2012-01-01 01:00:00\n", + " 1\n", + " 2012-01-01 01:00:00\n", " 2558.0\n", + " 32.666667\n", " \n", " \n", - " 2012-01-01 02:00:00\n", + " 2\n", + " 2012-01-01 02:00:00\n", " 2444.0\n", + " 30.000000\n", " \n", " \n", - " 2012-01-01 03:00:00\n", + " 3\n", + " 2012-01-01 03:00:00\n", " 2402.0\n", + " 31.000000\n", " \n", " \n", - " 2012-01-01 04:00:00\n", + " 4\n", + " 2012-01-01 04:00:00\n", " 2403.0\n", + " 32.000000\n", " \n", " \n", "\n", "" ], "text/plain": [ - " load\n", - "2012-01-01 00:00:00 2698.0\n", - "2012-01-01 01:00:00 2558.0\n", - "2012-01-01 02:00:00 2444.0\n", - "2012-01-01 03:00:00 2402.0\n", - "2012-01-01 04:00:00 2403.0" + " timestamp load temp\n", + "0 2012-01-01 00:00:00 2698.0 32.000000\n", + "1 2012-01-01 01:00:00 2558.0 32.666667\n", + "2 2012-01-01 02:00:00 2444.0 30.000000\n", + "3 2012-01-01 03:00:00 2402.0 31.000000\n", + "4 2012-01-01 04:00:00 2403.0 32.000000" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "energy = load_data(data_dir)[['load']]\n", + "energy = pd.read_csv(os.path.join(data_dir, 'energy.csv.gz'), parse_dates=['timestamp'])\n", + "# load_data(data_dir)[['load']]\n", "energy.head()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Reindex the dataframe such that the dataframe has a record for every time point\n", + "between the minimum and maximum timestamp in the time series. This helps to \n", + "identify missing time periods in the data (there are none in this dataset)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "load False\n", + "temp False\n", + "dtype: bool" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "energy.index = energy['timestamp']\n", + "energy = energy.reindex(pd.date_range(min(energy['timestamp']),\n", + " max(energy['timestamp']),\n", + " freq='H'))\n", + "energy = energy.drop('timestamp', axis=1)\n", + "\n", + "# Check for missing\n", + "energy.isna().any()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, save as parquet file." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "energy.to_parquet(os.path.join(data_dir, 'energy.parquet'))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -142,17 +252,19 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4kAAAHVCAYAAABc/b7wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeZwkZX0/8M+zh6AcKgjGSOLiFY0SDCJJUNQo0XgRlXgAPzWJR5AYYzAqKiScgsiN3Pd9LwvLHux9nzN737M7O7M7Mzv3fR/9/P7orp7q6qeqn6quuz/v12thpruq+pnqqqee8/sIKSWIiIiIiIiIAGBK1AkgIiIiIiKi+GAlkYiIiIiIiPJYSSQiIiIiIqI8VhKJiIiIiIgoj5VEIiIiIiIiymMlkYiIiIiIiPKmRZ2AKLztbW+TM2bMiDoZREREREREkaiurm6XUp6keq8iK4kzZsxAVVVV1MkgIiIiIiKKhBCi3u49DjclIiIiIiKiPFYSiYiIiIiIKI+VRCIiIiIiIsqryDmJREREREREADA2NoaGhgYMDw9HnZRAHH300TjllFMwffp07X1YSSQiIiIioorV0NCA4447DjNmzIAQIurk+EpKiY6ODjQ0NODUU0/V3o/DTYmIiIiIqGINDw/jxBNPTF0FEQCEEDjxxBNd95KykkhERERERBUtjRVEg5e/jZVEIiIiIiKiCB177LG+HOfKK6/ETTfdVPZxWEkkIiIiIiKiPFYSiYiIiIiIYkBKiV/84hf48Ic/jNNOOw3PPfccAKC/vx+f/exnccYZZ+C0007DK6+8kt/nuuuuw/vf/3584hOfwN69e31JB6ObEhERERERAbhq9k7saur19Zh/+afH4/++8iGtbWfOnIktW7Zg69ataG9vx8c+9jF88pOfxEknnYSXX34Zxx9/PNrb2/G3f/u3OO+887Bp0yY8++yz2LJlC8bHx3HGGWfgox/9aNlpZk8iERERERFRDKxatQoXXHABpk6dire//e341Kc+hY0bN0JKid/85jf4q7/6K5x77rlobGxES0sLVq5cia997Wt405vehOOPPx7nnXeeL+lgTyIRERERERGg3eMXtqeeegptbW2orq7G9OnTMWPGDNfLWrjBnkQiIiIiIqIYOOecc/Dcc89hYmICbW1tWLFiBc466yz09PTg5JNPxvTp07F06VLU19cDAD75yU9i1qxZGBoaQl9fH2bPnu1LOtiTSEREFHN9w2MYHsvgpOOOijopREQUoK997WtYu3YtTj/9dAghcOONN+JP/uRPcNFFF+ErX/kKTjvtNJx55pn4wAc+AAA444wz8K1vfQunn346Tj75ZHzsYx/zJR1CSunLgZLkzDPPlFVVVVEng4iISMtHr1mIjoFR1N3wpaiTQkSUOrt378YHP/jBqJMRKNXfKISollKeqdqew02JiIhirmNgNOokEBFRBWElkYiIiIiIiPJYSSQiIiIiIqI8VhKJiIiIiKiipTlOi5e/jZVEIiKimJrISHz5zpVRJ4OIKNWOPvpodHR0pLKiKKVER0cHjj76aFf7cQkMIiKimOoaHMWOxt6ok0FElGqnnHIKGhoa0NbWFnVSAnH00UfjlFNOcbUPK4lEREQxNDI+ger6rqiTQUSUetOnT8epp54adTJihZVEIiKiGLry1V14ZsOhqJNBREQViHMSiYiIYmjr4e6ok0BERBWKlUQiIqIYyqQwgAIRESUDK4lEREQxNJFhJZGIiKLBSiIREVEMsZJIRERRYSWRiIgohsZZSSQiooiwkkhERBRD7EkkIqKosJJIREQUQ5KBa4iIKCKsJBIREREREVEeK4lEREQxJISIOglERFShWEkkIiIiIiKiPFYSiYiIiIiIKI+VRCIiIiKiAEkp8cclNWjvH4k6KURaWEkkIiIiIgrQpkNduGnBPvzPC1ujTgqRltAqiUKIZUKIYSFEf+7fXtN7Fwoh6oUQA0KIWUKIE0zvnSCEeDn3Xr0Q4kLLcW33JSIiSirGrSFKj4lM9v/9w+PRJoRIU9g9iT+RUh6b+/cXACCE+BCA+wB8B8DbAQwCuNu0z10ARnPvXQTgntw+OvsSERElEiuJROkxJXc/Z7j+KSXEtKgTgGzFb7aUcgUACCGuALBbCHEcgAyA8wF8WErZD2CVEOJVZCuFlzntK6Xsi+BvISIiIiIqYCxpk2EdkRIi7J7E64UQ7UKI1UKIT+de+xCA/ABtKeUBZHsO35/7Ny6l3Gc6xtbcPqX2LSCE+JEQokoIUdXW1ubjn0REROQ/AXYlEhFRNMKsJP4KwLsBvBPA/QBmCyHeA+BYAD2WbXsAHJd7r9fmPZTYt4CU8n4p5ZlSyjNPOumkcv4OIiIiIiLXOIyckiK04aZSyvWmXx8TQlwA4IsA+gEcb9n8eAB9yA43tXsPJfYlIiIiIiIil6JcAkMCEAB2AjjdeFEI8W4ARwHYl/s3TQjxPtN+p+f2QYl9iYiIEos9DkREFJVQKolCiLcIIT4vhDhaCDFNCHERgE8CmA/gKQBfEUKcI4Q4BsDVAGZKKfuklAMAZgK4WghxjBDi4wD+CcATuUPb7hvG30VEREREpIvBTSkpwupJnA7gWgBtANoB/CeAr0op90kpdwK4GNkKXyuy8wkvMe17CYA35t57BsCPc/tAY18iIqJEYkciERkka5cUslDmJEop2wB8zOH9pwE8bfNeJ4CvetmXiIgoqQTHmxIRgF/P3IZnNhxG3Q1fijopVEGinJNIREREREQOntlwOOokUAViJZGIiIiIKATlDBDgkFMKEyuJRAkxkZG4b/kBDI1OaO/zk6c34W9+tyjAVBEREVEYJjKsJFJ4WEkkSohXtjTi+nl7cNsi/RVeXtt2BC29IwGmiojK0dwzjKtm78T4RKboPc5IJCKzcVYSKUSsJBIlxMDIOACgP/d/Ikq+S5/fgkdW12HNgY6ok0JEMceeRAoTK4lECWE8GhjwkCg9jMph7/BYxCmhMHUPjqKldzjqZFDCsIpIYWIlkSghjHD4a9njQJQa7zrxTQCAU976pohTQmE689pF+JvfLY46GZQwDFxDYWIlkSghjA7EA20D6BoYjTQtROSPt7xxOgAW/ioN55YRUdyxkkiUEOZhpgOjnJdIlBQzNzWguYdDC4moPGxaoDCxkkiUQOx0IEqGnqExXPr8Vlz04DrX+/I2JyJgspGYz34KEyuJRAkxxdSVyOA1RMkwMp5d1/RA2wAau4eKN8jdzCz7EVFJzCgoRKwkEoWgo38Ee5v7yjoG64VECWQq1B1sGyh6m/c1EZXCfIKiwEoiUQi+ff86fP62Fb4dT7ArkSgZTLeqZDcAEZWBeQiFiZVEohDUtPaXfQzWC4mSzWk+EecaEaXb+fesKfsYzCcoTKwkEiWEMHVJsL5IlBBS+WMeG3+IKtfnb12BP7y+p+R2gnOXKQKsJBIlBQuTRInGtRDT7TsPrcclT1VHnQxKkL0tfbhr6YGok0GkxEoiUYiueW2X533NdUT2PhAlj3MVkRXIpFtZ046525ujTgalGBuaKEysJBKF6KFVBz3vaw5WI9itSJQI0vaXLKc7mQVCIgIm8wnmCBQmVhKJArb5UJcvx2FPIlGyMTIhEXlhPPPZbkRhmhZ1AojSbn+ZkU3HJzKYu6O5oHjJBwVR8riNbsqlboiIKCqsJBIFrNz63JPr6nHl7F34u3ef6Et6iCgabiuCHG5KRGYcjUBh4nBToqCVmac3944AADoGRkyH5IOCKAlkiSUwiIgM59+zBp+7dXnR6/k4BMxEKESsJBIFrNwKndNchJ7BMfx65jYMjU4Uvbe/ta+szyUifzn1DCax7NfRP4IZl83B+tqOqJNClArV9V3Y16KYosI6IkWAlUSigJU7YmyK4uFgHPOOJTV4ZsNhPLPhUNF+N87fW94HE5GvVFlBkmcdVtdng3I9sLI24pQk17ztR6JOAhGREiuJRAErt+Vvaq4rUdULkcm9luHcJaJYMo8kSNttasynnMik7A8L0Y+f2oRMRuKp9fUYGS8eEUJklrY8hOKNlUSigJWbqRsFsYKeROM9h34IBkYkihuH4aaKt+JeHuweHAUALN3bFnFKku217Ufw25d34PZFNVEnhWJqcp3EuOcKlCasJBIFrNxevin5nsTJ15p7hrOv5R4YqgiJbHEkihd1dNPw02ElpcQtC/fhSM+Qq/2OO5oB0v3QPzwOAOjKVbqJ7PC5TmFiJZEoYH7l6eay5Pn3rMkeWxa/Z1CNANvf2s85MEQh0i3UqYaTh1V/3HWkF3csrsFPnt7sar+3vukNAICPzXhrEMmqGEZDQSZTetum7iE0dburzFPyNFq+4zg0JlHlYSWRKGh+Nf0pHhJGwXJHU4/qg4teOfeW5fjxU5v8SQ8RuaIq6DkNGQ+r08DIolRRknU4/Q1UmpuhhGffsARn37Ak2ARR5C5+olr5OjsSKUysJBIlhKoYZjwwZm5qLHqPsSSI0qdzwP8hicJjeH1mMf5gLxFZDY0VNtgYDTFOy+gQ+Y2VRKKg+VQCcDvvkA+T9JvISBzuHMwHEKHk0r1b//XRjb5/ttcCqJyMoEVlmDz/ESeEYo/XCIWJs86JAhZk+clpeBJ7EtPvyld34ol19QCAuhu+FHFqSKXkbegyg6htVSy0XaYpueZirwVQ1hHLZMxJLHH+e4fHgk8LxYK1wYa9zRQF9iQSJYTbZwTXTky/F6sbok4ChWw8gNaffE+WywGkDMc/qZy1InXnJH4/gF5kCt/mQ90lh43zzqI4YCWRKCHYkkiUXsp1EhWvBbFwfX5OosdJicybgCte2eF5X9VUApVtDaoAZZREhzsHPe3Htl8KEyuJRAHzqwA1hSUxosQpNc/P7V09prNOgktT8sMdvZVAGd0UeGVzcfAwXfmzV+L08xGQHm5HBAzmIg+z957CxEoiUcCCLECxcEaUDqrCn6pSEExPgjHc1B0WVyeV04hn7NraN+K8HfP7FClx95jerm3zfx6y1URG4oZ5e9DR73wNUmVhJZGIiCgiTnWLsIaWXfnqztwHuttPVvhwUyklXtvWlB0CXMY5MM7fqv3tGB33v6eY4qjwgnH63s3zF4PKE1bUtOHe5Qdw+Szvw6YpfVhJJAqYXwUo3XkrXrcnosq0an87gDKGm1ZoVjNrSyN+8vRmPLzqYFl9fOYewrEJ+8pCpZ7nSnDzgr0Fv5vvRPP3HlS7kTEs3ro+I1U2VhKJAhbkc91roYFrKKYDC43xp32rxeCWdD/cNAaJjlB7X7aHp6V3OJTP4+2eHta8u6FryGHbyY2DenbnRwUEcnRKKlYSiQIWx4I864hEUSjODIxepCfX14edmCJu84XJgmUMM7kQSQBTpgR/Djg6JL10r5/gehJz6eA1RiasJBIlhNus22l71hGJ4mXu9uaok+B6uKmxNcuV/vXA6H4Ds7c2+fSJFAdTLRdQ2KN9jHuf9zKZsZJIFLA4trJnpERr3zDaSkTTo3DM2tyI7kHnxZUpneJUKItTWpImjF4+8yf8fv6ewD+PwjN1il5xPKi642SDz+RV1j04ikwA67JScrCSSBS0GBa8pATOum4xPnbdoqiTUvHq2gfws+e24KIH10edFIrAmgMd+Z+jbrRxP9y0sguQ5nphKNm8OYBJZZ/6xOkZHHN8f6qlNG7/9QY1JzHXk5j7vWtgFB+5eiH+YAmoQ5WFlUSimOseyvYwuX00WBu2D3cO5n82B5xgS2G0RnKhz3c29aKp2z54AaVP73BhwfHLd64s+P2Q6Z6Ns0qdK2euqJVzCgqiVzrU/irzLKfDz1/Y4vi+7lzAwHoSLXMSO3MjW17fEf0weIpO6JVEIcT7hBDDQognc79/WgiREUL0m/59z7T9CUKIl4UQA0KIeiHEhZbjXZh7fUAIMUsIcULYfxORk3If7I+srvPl2KtzYe6tbl7IlsIomcsGzSFFSaR4aukdyVcSWiO4Fiq0rueTyZPXPzJuu9WRniEc6WFjUKVp6S0cJWC91aaGEPjIidFWzDxALZORGHdYniatouhJvAvARstrTVLKY03/HrNsPwrg7QAuAnCPEOJDAJD7/30AvpN7fxDA3UH/AURu+NXKXu5Rjpo+ebubWyMX724t88hUjikcQpZqTr1Nqnv6YPsAAGA0ggKJ6+GmwSQjMR5dU5f/2fzd/vx5+16jv7t+Cf7u+iW271fVd9m+V6k9tmlQarkYa09i2M8CI31FS3NwdAsA4LsPb8B7fzsv6mSELtRKohDi2wC6ASzW3P4YAOcDuEJK2S+lXAXgVWQrhUC20jhbSrlCStkP4AoAXxdCHOd/6sOzfF8bqus7o04G+SQOj/W9zX0FD6GGrslhbF4X0Ca/eF8DKw7XFnmnKvRPJGn4d4WvrdaYK0A3dg0VzCc1Kvpe/Osj1jZ0qgTWrCDsNUjzy9lYEjI6nsHI+ESy8qUArLIZiZV2oVUShRDHA7gawKWKt08WQrQIIQ4KIW7NVQ4B4P0AxqWU+0zbbgXwodzPH8r9DgCQUh5Attfx/b7/ASH63sMbcP49a6NOBiWAU8uy+Z2v3rW64L1zb1mR/7nC8/7IFcxHcrkvv7pkU929EzFptFl7oAOHOpIxJzJq83cWztsKM09t7x/B3ua+8D6QPCl1W+ve9kFdWhlL4Bpz3vQXl8/HxU9WB/TJFGdh9iReA+AhKWWD5fU9AD4C4B0APgPgowBuyb13LIBey/Y9AI4zvd/j8H6eEOJHQogqIURVW1ub5z+CKEx+RQ8cGZ8I/DPIG/PDmF9F+jj1CKjaeIwW+yiHFo5NZHDBA+vwyT8sddzObohapQsqT1Wd53NvWY7P37ai+A2KlaQ0xtoF0Fm4qyXklFAchFJJFEJ8BMC5AG61vielbJZS7pJSZqSUBwH8EtkhpgDQD+B4yy7HA+jTfN/8OfdLKc+UUp550kknef9jArZ4N2/EtCmnAOXXgyUjgf96Vj1PJikPr7QyVwY43LSyqL7uTAxiI/zo8Sqt7WSFDzfV9eDKWl+WG1Kd5+4SSytQPLiNIh76nEQGriGFaSF9zqcBzABwKFcgOhbAVCHEX0opz7BsKzFZed0HYJoQ4n1Syprca6cD2Jn7eWfudwCAEOLdAI7K7ZdI339M7+FM5BfOSYxWQU9iZKmguBiPQS1x6V53o20YUKWQ9T6+ds5u/z+D+XaiWJ+zbu4Z866BLYHBpw8phDXc9H4A70F2WOlHANwLYA6Azwsh/l4I8S6R9WcAbgDwCgBIKQcAzARwtRDiGCHExwH8E4Ancsd9CsBXhBDn5OYxXg1gppSSA/QpNvwqPwWVhbOsEa3CNdLc7Tswaj+MmOKhILqp9T3F9ta5QXHGvMNGBOdl8yH7qKgUPbeNsWHfWw+tOgjANCeRDT+EkCqJUsrB3LDSZillM7LDRIellG0A/hrAGgADuf9vB/BT0+6XAHgjgFYAzwD4sZRyZ+64OwFcjGxlsRXZuYiXhPE3EQVlV1Nvfg5hOa3Fupk8WxCjZZ4D4ua7qHYIlU/JlaSluIyrlcXJQlHkqF+7e00En0q6rI/yIO6ZwdFx7Gvx1keyozEb/sMoN/CeJiC84aYFpJRXmn6+BZOBalTbdgL4qsP7TwN42s/0RUmI0i1IWw5347R3vjnyxVfJf03dQ/jiHStxwVl/juu/flrBe/y2K4CL0uWeZmtML0oaVSNQkkLNG+lnp0MhDgUlK2tPovUK8aOx9pKnNmHZ3jbsu/YLeMO08vqAeE8TEPI6iVRaqfuyur4TX71rNb59P5fISKOuwVEA4Q4dOtw55FioWbirBTMum1OwDhgFw039QLDZgCgSnQOjOMshEA2riGRVTttP4TJJ9gdaV9sBIB7zmikdWElMmKbuYQDAxrouDHE+UuoYdTWjl7icwoabKsSKGvuFYh9fWwcA2HWEPVdBY+tt+jjdw6r3kjT8u1LnLV01eydaHRrN3HYkelleIDlXCQHF97WbO0f3epqaux/LGY1gNBjbLYVBlYWVxJgp9dA1v83WovQxMndVBj00FlyjwLDDsY1rkkOo4oXP8OTb0Whd5hf50n8S7rZKzRPGSkwctavo729Vzxdr6BrS+lxz+eBIz7Dj+rcUL6qRHwMj4/mfrbeSl3trSq5x2Y+iIacz+UdKiYPtA1EnwxNWEmOGt2VlMyr+06YWXwlBZjJO1x2vyfBUaJm7Yl34wHrb9yq1ApYEpYZ62311596iXvRe95u2XhPKRgaKJVXDwdc9BBtyyham5Sp2E8w7YuWBlbX4+5uWJfJ+ZSUxZkr1DnAeUrpZF6culdc7XS9uepp0Wg352Em24bEJrD3QEXUyKo7byl5D1xC+fOdKdPSPBpSiQqPj3rsdKnW4qe88FurvWXZAe9tTfz0HP3qc6zDHhRDAXo+RSO0Yz3GOMouXqrpsjImGrsGIU+IeK4kJxkJ7+kRV5rL73LGJDJbvc7ewNoXj1zO3u9r+ty/vwAUPrENtW39AKSI/PLz6IHY09uKlTQ2220gpsavJnznCV87eaTquu30rsbdTSok5249E8tnWSrmbYGJSAgs8zH0kf1gb+EvdOl7uLGOaSjl1RGn5P5XPuG2TmF2ykhgzpXoK2XBLYRocMc15SWAGF5VLnqrGo6sPKt+TUkYy7KQmNx+qb3i8xJYUFJ2et6OmTwUADDoEJntlSxO+eMdKzN/RXHaaNh7sLPsYlTQovW8kgPtH88HudkF2Si5+1elhlOuT+JWykhg3Lp61mYxEa+9wcGmhyEUd6XCqaW5k1GlJkrnbm3Hl7F3K92ZvO4Iv37kKs7c2hZyqLH6L8faG3D3nNAzUGKZ2wIdeYXP9xHsjZHquqomMLLuH1CnyqZLm51mjVu5rKfz+nQKQqTywohb7WzmyIApe77UgKo89Q2P+H5Tykty5w0pizJS6lszv3764Bmf9bjEau/Uio1H87G/tU2bQfjwHFu1u1d7W7sGT4LwttoxCmVHA9+sBMjaRwYzL5uCxNXX514bHJnDxE9Wo70hmZLU0cHsvG/ei036Tc5b9LTFWeu/FwMg43vObubhj8f6yjjM6nnH1XPZ62q0Rr29bVKO979hEBtfN3Y2v3b3a46eTn/y49fLDGl0e7VcvbptMh5H/VHpmYMNuhJCOJJ5SVhIT7JHVdQCAFvYmJta5t6zA1wN4SHcPugt68cqWppK9EknM4OLM7/NpDA2+ecHe/GtrDrRj/s5mXPnqTrvdKER7m/XnEToV0uLXMh27BHliNNg9s+GQ7Ta6f6mb57JfZ69nyH2wI663HE9hjtyZv7P8YeuVwm6EkBOvlfc4YCUxZrw8/Fl4T7YDbfa9PCWjm9oUL4bH3M1cf3VrE758xyrHbcpYn5d81Nwz7DisTL1Ae1qK8eEbm8j4th7dTQv2aW+rc7vFJ++PTULK4mdhzk1PjM6WAyPjnE+cIl7vXZ1rMz75AgHJXpWAlcSY8Ra4hjkCFSq12LOKdegSwCurXG4D1OgUAP72+sX4YRmh7DmMyJ3P37oCf3H5fM/7u44Ymt8v/t9T2pbAyAeY8OHUuzmGddtVNe1F2yzarR+ZdGR8An915euY5xCF1cszgoJjvQa8XIN+BEiZzH/KOAjljU9k8tGQk3hOWUmMscOdqjVV0vVQpmCMB9Dtl4RCa9w8vrau6LX8fDKbfXY29WDW5kbH465UFCIfXFVbcHyz0fEMtjYkbyHfOKhtj2Y+ZxS3m9s6X9ryBD/rvOWcmdsXF/c4u6mQt/aOoHd4HNfN3W27zSd+v9RT2sgf6bpzyM6AKUJ8Er9zVhJj7L+e3ay1Xcqe0xXOXSkl6Ib8tBUCw6Y6fdbvzFr4+9Idq/Cz57a4/qzXc/NKzB9ptCyvOdBh+3kUT9ZIlmZ+hlQ3j17xfrun45oq1YAD6N8/5WSdfmW7xnF+PXNb0XudA+7nL5J/gny28rldvo/fsARfvH2lr8f86TN6Zfo4YSUxxiY073POFUu+nqExx5D3bqyv7cDf37TMl2OZ8TLz3+4jvcj4dAPrlgtYgEgG54pKaMnQlJJrKr/odbhzEnWU85U/s+Fw/udaH5ZNoeDZXT3uhjFLLN7d4ukZs8GX9VOTq7F7CLuO6AcasxW7vNodVhLjTJEbxK9wQH44/aoFZc0zM/v1zO2+HKe1b7jgQcW6hXtOp2zL4W584faVuH9FrdaxhscmcNdS+9D87f0u12YzkbL8teHIjvq8ljrfOt8HvzJ/+Ton0dW2PlcoHcoJn7l5uatj/W7ublTVVXaFIQhB3rrG9fvy5kZ8/7EqPLW+3vUxfv7CVp9TlVyLXcwHtkp6mZ2VxJgpdUGp3mbhLh2W72vL/xz1V1rT0oezrluMx3LLrGTxOnOyZn97ybmEwORaZg1d2TnH2zWD21w7Zxf+8Ppe2/e7Br0viHz/ilqc+uu56B3mosph2HCwE6f+eq7yPSOPd2r8nxwWmd3oMzctw9/8bpF/CXQt4SWhHJ1lK3Sft27y8G7Lvava1Uth0481lO9fUYt/vndt2cehQqWuj8L39S4m6zXSnLueG7iWdlnm7ajcJUKmRZ0AKmTOGHSfMSy6p9dVs92vb5fxoYZ5wQPrAAArauJTcY27Cx9cDwD46l+/M5Dj17WrAln5w1gXrqN/FMcfPT2wz6GsVfuLAw9ZOd7HltJgVMF1JqUjc/jyndllgPz4a9z0DjZ0lS7Euwmjb543ua62w2FLig//76H8NeNyiKqdHY09+PA731xmqpKnnCawpDefsScxxqQE+kfG8eDKWscbl4X39DLPJdE14cMF0d6fDWpgjpLKy6zQOTcuwcVPVDtu41jON7bR3d6npw2/x3CpvtNpU0p/mTq3sR95vx/XVVV9V/kHiRHH5632Mfz9fJ3vSfWZ1l5KiosgA9dk/y/c1xEdHekp3dOeRuXkkUkPFMdKYsxYr6fr5uzCtXN2Y/Hu1tz7yb7gyJnbr1e1ecbH5a/8CqqSRoc7hzA/F1G0x6Yg5vdco6Bx6Ho4nOqIRh7g1JMYt6dA2ioivvQklnGQQ8rlr/SVuj6c7vOHVx3EjMvmYGS8eN1ccmdnUw96hmyeDSWvD1XJv/cAACAASURBVPcXkLUSl2+I9ClfH6/QtTXd9OKnDSuJMSYh8xnM4NgEXqxusFnwnAU7muTHcFODuVeyUusPHf0jJXvzfzurjGBBpuOyDagy+NXYZ70i19d2YOmeVl+OXcmc8jrdfLCcr9gYyVFwPM19MxmJwdFx0yvFCbb7G6SUuPq1XQCA/uFx9Uak7Ut3rMKFuakbpVi/E7fP20Mdkw0LRplwinAfiMlpU3PchEpSyc9lzkmMsUxmsgXjybX12GAXYaxCC++k5mslsTIbDgv8zwtbsXRvG86ccQI+8mdvUW7T61OB6jsPbSj/IIULJVLEVHfjVI3hpo49iTa7f+v+bIG07oYv6SStokgp8dCqg/jK6X+Ktx9/dMlt7d/0OWGadAuqty3ahzuW2EdBdrLetOwBixX+2NmUXUbB+v15Pb92l2bn4GjRNn4PN33jG6b6dKTksOsJ1pX0RzB7EmNMAvkrrH3APrw9M/P0iFuQAXNhqVJ7rI0K4JhDjdn8ICiIcKozr8xjuvxk9GxlJLCvpS/i1KTfVI0Sf6X23AelrmMQ187ZjW/fr9ezk1Svbm0quY3dpeXUe1ipvUh+KbensBSnHMWvz3rvycf6c6AEOf2qBVpRj+0kvReSlUSiGLlxvv0SB7r8HD9fEG23Qgutk/M6HLYxnfKfPbdF77j5ilm0J7ZncAwHc9Ex711+AJ+7dQW2N+gty0He6BQctK4LjW1q2/qxv5UV/4ncZO2D7QNlLQ0RXWOZn/m6+yHu33vYh1EOFBjz92i9Ql1dsz4MtU6b5l51J011fRc2HXIO2pX0c8ZKYsyY82vdycYX5ULvWx1o62evQELp5itBt1KZHy4Jz+s8M87xN+9biwdXqhe+t/sanM7Z/tZ+x8+1DnPxbR6bJVF7TXnE1sPdAIDG7uCW20iqHZrrWerQGW7qHBk3N9dI47M+c/NynHvLCqyo+J6gyXPelKB14/7fg+vxQtVhz9FNla+VeN/pdfKmaLhpiRPcMTA5fHT3keJyXCYjCyv7puvbeF14mJOYdg/lAjP54fx71uDrd6/Bpc9vwRdvX+nLMeOGlcSYsd7L5RQLP3vzcnzu1hXlJIcSyM+KY6agJ7HynjTrajuwsW6ypfDaObuV25VTgdvR2Kt8/Su5NdsoHr7s8ftQ3TZalUSH97xcbgt2Ve6C0IClp6Ws5SnKT4sbq/a34xcvbvN1bpP5bxjPSGXUyo//fomPn0heGMMcL5+1I/+ahETf8Bje/Zu5+G/TqBXz9T04mg1wmPCRjoG4xgjMNOJfYKaZmxqx64j6OZ50rCTGXNBLXkzYPCCIgOiHQkZNd/5SEHep1zD4fSPjjkuXHGjrx19fvQDNFbrmVRw45ev5Rgmf10mc4HI2eUaDV8/QGP7t0Y1o7ause8E6/HDO9iNF24yOs1wQpKIhoYrbs61PPczRWHJm1pbJ+afmLMVo0MoHrqnw57jKx65dFHUSEoGVxJixFh2M32vbBhz387qm0WdvXob3/naep30pQDHJ1GOSjNizK/O/vLkR1WUsNG4ektrcoz9ETlXoM/zyxW3oGhzD/B3221CwdALX+N1A4+f6qUmkOuMvVB3Gkj2tuHeZehi5oaquM9/wEtmMRB8bjK2X1vgEM/qw6dzediMOpk0tfl0Vi8BYAuOxtfXuElcBhsYmMOOyOegbLh29VPfOu2XhvqLXkn5nsZKYEv/1jF6wDKu6Ds49Sht/hyVxnUQ99mf9/HvWYGWNt/lgz244BCAbXGZfi/McRrMjPUP41n1r0aoRlY2tzPp6PCwarwoaMVXjyev3t1Kp0YkN5kqWm8AeK2va8M/3rsUDK2tRXd8Z2SLzXu/TZzYe9jklk57dcAiHPY54oNJU7QJSAkdNK16KQrWtl3YFp3shjTnIEY0RNbrn8Y7FNWjtHcaAj0NZo8ZKYkrM39mM375cxoLeRAqlHgrDYxPYaLd+ZwVp6HIuKN28oLiFUYdx/vtG3FVOnq9qwPqDnXho1UFPn0tqWxu6fTmOTq+QU0+iEYTGTcXPbrRp0FMa4qIwKFzuNY3AHkaQm4W7WnD+PWtx1au7AkphMFQBi3Trm0ccRi+Mjmdw2czt+Od713hNWkXzo9HmDdMmi/DKSmLZn5B+OveCsdaljrN+t9jz/PU4YiUx5tw8v59afyi4hFBFMhdUVQ+1y2ftwDfuXYv6Dufh0HHw3MZDqAkg2m/P0Bj2NDsft9ziQJAFefOxa0pEXK10fg0BLXedxCoPQ5jt0r5bM+DC0OgEhsfse9Gc3osbIy8zvgVrI4/5TBnD+Lpyi5VHFaBCNw/wM6/4wWNVtu8Z57BroLzFxiuWxznHV83eqexVnlIhjT3l8uM0Oa2ZDCC/pFQasJIYc7ztya2g5q6oHlh7c5Wjbg/D8ML2q5e24x9cRPvt1ZirAEBvaInHykW+x8PT3rqfkcZBRMHw61TpRDfV4So9im0PuZhu8MH/nY+P32Af8TKJBSMjq1y0u7Xg9b7h8aJetAO5uABhDdsdm8i4XrZEN22621mX4THnicw2/PVcVfGwYNU53nRIPZqBZUU91vPk5X6+f4XzHOaC4yf8RmElMWYGRidbYxN+bVHKqK7HKVPisSB8EH7o0IpuprV+WZlp8Vrv7x9Oz9yIOPBynat2CbrRX9Vjrkq7bkOIwbx2G6C33l5cFJxzjbTazQE+3KkXQGpvidEFpdy0YC++G9AC9uZlfdxI61pwUdC5XYbG9PNv5fxF/eRM7uO0U9xvcg1+NKIv2t3iQ0qSgZXEmAtrvshch2iIFL44ZsWqNBkdImmsJO5ymIfgdq5fVKenictc+MqvVSR0hoZtOex9/uMX7yguzAcdwHJKzEsTBYuN51+zJ8vsxr/6tfLmLt63XL+3wqCbz1z16k7l669sbSr43Xq8hi79CMvkTPVdWYdsXz5L/T2pv+bgRyek7ynvzWab3tw0inm2Xlms6xKFOcT8J09vCu/DyDdBVz5KHX9arpZYaSHUr3FZAJSQmLmpwTEQhN1+gDq8eZBSWOf3hV+NIX6dXrvjjCnuR91hT9btdNfRDfsaLYc1cE0S6KS03OtqtqWSqCU5pzBUXoYZWue66c4X9lOCbglPioab8lnniJXEGHllS2PB71KGl//q3icTGYkHV9YmKkhBpQkqk1c99IyCYSrzWc3zqFM4HhiZwKXPb8VFD673lhSX32m5l4DxeWMTGVw3Zxe6LMMMK5WXgp9ql58+s9mH1Lgr4Ohuau0lf+9v52HCpgvV/Pk+TbMMjNt7KIl5WiQjOpJ4omKgnLmt6q+ZX4QOv8pHL1U3OL7fnBvFk/RvhZXEGBm3PIj3tvRhZNzdCshXzd7paS0vXa9sacS1c3bjtkU1gX0G6RmfyPheIcxkJGZtblS+55TZxbx8GCid78AoZLf2jrg6dtStnPN2NOOBlQdx7Zzd0SYkJvwabhoFZSOP4tq9XZG3j2dKP4eS1AOhW0Cvru/EE2UsRD7jsjmhRn7WzS+sc1ETfFnHQvfgKC5+orog0JCXvDsO38O8Hc1RJyERfv7CVsf3b1+cjjIyK4kxonrGllp/zeqR1XX4/et7Cl67f8UBT5+tYgTWcRvwgPx304J9vlciXt3ahJ89t8Xfg1Ke25b+R9fUAYiuEj6RqxzoVBIqQZLn3uomvU8RrVfn64/70E1z8iaHm5pfKz5B59+zFtsbe8r63PUHw1tHVrenu71fb2SA0/WefyveX3sofvRENebvbHYVaEh1ahOcvSRGeMPi0/FlspKYQhlLc/etC2swNOo8PDQdl3Nl2dtcOF+hLheCvpyy2sBoYQGxoNDBiyTP7Tk2Gns8FwJYEIuFJPck6lRw+22Wc7Hfd/L1aTEab/rshkO4YtaOktuZU2z9bo90+xP0qSXE4FFhXp5hLQWSBF6WfylnxKj53GcyEqv3t6u38/krSkUl1pJNpeJvChAriTHiVwhjq6GxCXzwf+f7cCSKs0/ftMz3YzL/LE2naFxu5cJt62dNqzp8P5XH05zEAO8iN8fWuQat6+JNfk6yXDZzO55YVzhM1NzTmf/R9Jr1u31wpfvooio3L9zny3F+8HjpJXle2eIh8AyVTZU7h3XPPLz6IC56cD0W7KqcZRnKEVo/oiz8f1Kxkhhz2xrKG+rit/i0FSdfUIusBjWcIslD7bzQPosuTnecWt+3N4YfOS/pYncLuAlcE3Ek5KiZ81vlUD/rCwl92PmZbKc831iixRqVvRJ5Gb2jev5rPx9Mmx3uzI5Saep2jpzNYINZOnE+7BrLKhEriSkU1sO6c2AUMzc5R3gib2rbwgt24IeYlw89CWKOldd7M4jpXi95uHd/8vQmfPPetf4nJiHsonw6+eWL2wJIiXvlNErZ7Rv3imEp5tsq6X9L2JabgrRUOi8Ns6rLzcs1OCU3zLtU3vSTp/2JqJxkaw90FL2mqpj/8LHSvfaVgpXEGAlrQm1T91DRchtWi3e3YGTcvuVJSuDHT1bj0ue3ug6uQ1lODwS7uUFmQVRirNegUxrvXrYfG+rCC8rgl6B6cLU+2+N+OouvB8n49Ne2HUnkd+4XL9/fzqZ49NhKABfcvw4vmkK3q545n/vLtyv3TTpVfml+aXC0dJ6bVl5yF93yyrt/PUcreF7aeHnOWIdI65gq9CqJK2vKr9RH+ez0w6FOvcb3XT6sT5nwU5XHSmKchFAOPNDWj7NvWIL/enaLbTCb6voufP+xKvyuRNj7lt7shHwON0kPp7qI9b0b5+8NNjEBedlmiQ9dmYwseAC4atxJyYOD4sHN5SSlxNraDvxPidDt6n39TUscnfP7pQW/J3S0qa+c6h267VYZCfxu7p7SGyaYpzY8xbldVaMOQOO069RcT+L4hPMd6EelJen3uO6z2td7P+EnjZXEFNrX2mf73g5TOG9zN7s5A+kZyobHPtTp3EMY95DncVcq71gUg4nodtdIkrX2uVur0Oq/ylgiJE5zEp3x3k6bsRKFSINqq58+k/6haqqlP5IorByGOcQkrTmuRe8rttA8qb3mOXMO+5h7/iotpoCK6pwrTwsv7rzQK4lCiPcJIYaFEE+aXrtQCFEvhBgQQswSQpxgeu8EIcTLuffqhRAXWo5nu2+l2nyo25fjKO8d4fQuqXjNmxft9lZJLKfubt3VnPbhsQncu/yAp3lZabLbMhTFzfmO23N6wLZgHLOEkpKb4V9O0wcKj1n8mt38syRdJd2Dk2sDTi7xZ3/zshHU2d3LKm8IaTn8vJp+/NSmotdKV0oLvVTdgPoO9fDLpA8r9eKeZQcKOlEoK4qexLsAbDR+EUJ8CMB9AL4D4O0ABgHcbdl+NPfeRQDuye2js2+iJOGR1NpbXi8MTSqVEXupjD2y+mBZ15G1XGT+/fbFNbhh3p6yh2uGbX9rPy64f13+93Kff9bzO29Hs/a+Xj5aSol5O4542LO0r/xxlaf9blm4D9+4d43PqaEg6ecn3m6QuBcsv3SHt2s9afz8HnQP9dCqgzj7+sWBpiXOdEaIWLco59Tszy1xJDDZ0FGqp9D6/s9f2Iqv3Km+J9L6tTk1Cv1+/h58OXc+/IgBkJxRQ85CrSQKIb4NoBuAOTe5CMBsKeUKKWU/gCsAfF0IcZwQ4hgA5wO4QkrZL6VcBeBVZCuFjvuG9TclWamMYNnetqJM/vbFNcXH8TNRlLfdQ6vWVbN3lfWZToFr+oazvU5JC6V93ZxdWFtbHNWsFN3nxDPrD7k+thtSAr99ufTC4F7YR9EVGJ/IYGBE/V3fsbgGG+u6AklTnCW50KtdRUzun6hN59ZOQqOtX9bZ5o96F8M1r+1CU88wrp9bGMegEq4lO6X+dtX7qmtuW4P9yDCJyedUqSGv5veNfKx3WD2SpJKHps7bfsSXJTDy6yQmvIQcWiVRCHE8gKsBXGp560MA8jPppZQHkO05fH/u37iU0rwa7dbcPqX2TZwwhre4/Qyn+VuV9BAth11+Wyrr2NNsP7c0MA5f6niuJ6K9v7J7k63fm+4wPsBbJWNjJNFEJX74eBUunxVM5ZT84eZy0t3WXTAcFxvHiE6ykzratKN/tPRGFi9U+7OU1X0ran05TpytPdBRFPSv1H3Q0DWo1zCh2Oi8P6523if3fzcVu1KjCuzeTur9nqfxJaiG8layMHsSrwHwkJTSmhsdC8DaZdID4Ljce9ZYtMZ7pfYtIIT4kRCiSghR1dbG9X102d1T5sxiye7WUNKSNk95CHdtZvfdlNPY0GAJVqR6Jty2qLg3Oc6s58OuZW9kfAJ9w6VbEK1n90DAa1p+yzRUNkxL907mk5yflWU/hzP+jMXPS/HaW5qk8mO5eW+cTQQ83LRzYBR3L9tfct/xiQz2tkTQ0Bmgw52DuOCBdbhspru1T3c09hYPN1Vs53YZNAFTT6KL/UpdI0nv/bKV0j8rSKFUEoUQHwFwLoBbFW/3Azje8trxAPpKvFdq3wJSyvullGdKKc886aST3P0BCaSz4HU594u5zHjdXOelMkjtyjKHhkoAC3yOgHrHktIP/6Rxeuyu2NeWbxX+5r1rcdqVC0ruV5ObD5IW6nyAlUKVcu9Zv3nNwzsH7HubKqEcZeSbbPtw1qG4Tv79iSqt5Y9uWrAPX7h9ZRDJisxAbi3NPUcKi5iqe8Zc0Qpl+KaLj8jkVi2bYnP9J77H0KJrYBQ/e3Zz/vsjfdNC+pxPA5gB4FCuRfpYAFOFEH8JYD6A040NhRDvBnAUgH0AMgCmCSHeJ6U0ui9OB7Az9/NOh30Tx8/nlZcFrzMZiSlThG1rlrEuIsVHqWVKypW2hwUw+Tftb+3Hdx/egBOPeQOqr/gHbG1gZDO/rKvtwLfvX4eZl5yNM/78rVEnJ7W83p/t/SM44Zg3KCtJroawxnyJnJsX7EVt+wA+/KdvVr4fxzSXa9Bm/WO/6C6lsmp/+kZsGQFN3Fb6MlJqle+89OAZ5TXt5R0w2ZNoF6DFbg3tpLpzyX7M2tKE3UfC69k2Tn3S85iwKon3A3jW9Pv/IFtp/DGAkwGsFUKcA2ATsvMWZ0op+wBACDETwNVCiB8A+AiAfwJwdu44TzntmzRBt2pmSoxDn7ejGV/6q3cUvGbs8erWpuK1stgKqyWoPMJ2uKmvn1Kc+vecdIyvnxA0u/vKGFraMTCKQx3BVrbjbL9Gz6jbIYjGGp9rD3SwkhhDlbKMzZ25kRFztrmPDryvJV0jBvyimxckvXCsYvS8WSuJpf5W1f2mOo+HO4dcpUcC+OPS/VppUKXHrpKoClBofF4SPbz6YOifmZbrP5ThplLKQSlls/EP2WGiw1LKNinlTgAXI1vha0V2PuElpt0vAfDG3HvPAPhxbh9o7Esm//38FscKhFNXfHUkwTMoau2mIAhTc0/IT74/HcO1zfPsKnkYyqNr6kpuozMnsal7CA1d2cr2g6vCfyiTPqeekCSWbdbsb8eGg+6fURxu6l6p9oXWvuyIo51N1nASySfyPYnWd4pPynLTnG7dnsfGbneVRDM3vZtGh4Hd9d816D74URKkdq5lgMLqSSwgpbzS8vvTAJ622bYTwFcdjmW7b9IE/cB6ZUsTNjo8SH/54jZ888w/K0xTsEmiIAT0pRmtj2lpITOfJtUDNohgLWnuwDn7hiUAgLobvpR/LclLRqSZMSfJX9F816PjGVz44HoAhdceBaNUQfus6xan/nuwPhnM2Vxr3zBOPu5o/OiJ6vxrwdxvhemwZrWHHaaiGMNNp9pMSkzrSAO70QFpG17rp1DXSaToNfVMzits7NJvtVIVmFmBjKkI8vd41wXUVyp7EbJ0KnKLd3sLkBTv6yIY/3DLcjwcUk+qm5bxPz/hTfmfjUYRL/fAnubJHqI4fL+/esldpElS0y0ol/Odt/QOY+ne5EdDd4pU+puZxcsGqRoh/bh1pM3PAHDOjUvRbBNHIlNiuKldJTGtjX5B9JwaeXPSzxgriRXs87etiDoJFSGojNWugBd0ppS0hXat58n4PswBmsx/0uytTWEkK1F6h8cxPOa+tTVZV4o/alr7cfVr9lFQb1vkX1w1r7fiuEPXRqn86h9vi1fESq8NGFTouw+v19qunF6mr921Gv/6yEbP+0dNp01FdW8F9cg036uqz2i3Wefa6EnsHxlX3u9Je8aXy65HtRxulzOJK1YSYyQOF9XYRGEGV1lZRbLY5eNBZ/DRX6X+MFcezafsP3MBmtLyd0ZpfCKgcVYhGR33P/13xmCZmSAiYL5Q5c+C7BSNjXVdWtuV83gxj2RKMmvFyvy7Ks/wc+3Kws8t+K3o/V6bdX/NFX1VhPSEZ9uutPWP2PaoEiuJsRKH6/R7D29g6Thgq2raURPgIsNpnU/gN/Mak5U8oT3IvzzpZ/X6ef6vARvVkC3zNX7T6/br3LlbAmPSfStqPaQqWnzUuVfJeaXB6QxU1xdXtvuHx1FniaDtRzZgPoTqsW8Xudrc2Tk8VlwjjLonsa1vBHcurgklr/zXRzYG0pOYFqwkxkgcevjXHOgo+P3yWTsK5qCYBRHYI42sX+v/e2g9/uHW8of62p3+UkudVBq7q/QOU5jvONx7ceI0HNGNpJ9XL0snlBLmKZm7fTL95u+ixmHZk0qqBFTOX+qfpN/TQTCfko+/921F77cPqId9+poGxRcz422ll6tSVQijbmi+9PktuHnhPmw+3B3K5+0+Elwk3oGRZEdOZyWRHC3c1YIfPFZV9Homw1bYuApqaEtSFc9JLN6GZ6zQNQ5z6tww1vBKqlPe+kZX28ctsMMlT21Svv7WN70h5JQEh42V4Yq6lykOHJeQUc3xC6jSZa7MqT7CLpnmhqA4VhKN4fBezlvP0Bg2HOx0NdXhogf15uN6cc6NSwM7dhhYSYyRuD7rrPMUAWB7Y08EKSEdLb3Bt1ra6R0ew4zL5uCVLY2RpaEU1WMnboX7UCn+dNU1VImnaMaJpVvizXTOkZ/nsdR1O32qOjjTjz/9Htu0uFuUO16Tl3Y2uXsuxfSRG2sVmA3kGX+79bI33zOq86Our/h7JpXPNZ39FBtFXUk0uEnF9XN3Y+GuFpx+1QJ88761uHmhfwHCKhkriVRSS+8ImiyLvLI1kVQO5eZd3Lc8PvOTdAJC8Wr2j5vW357BMdvh7EkUxXXkFN3TrpfttW32EXzdZO3//dxW/Y1D8J9Pb3a1/QaHdYNJjY9+ZxmZXeojbF4bOlUVQrvRSHH+7u9bUYsfPj456m0HOzJ8wUoiaanrGCj4fU9zYeCVpI+7DlSMM9ZKpCo2Wx9+lXQ917YPlN7IhYdX668R+M371sZuSQWzuN+6bf0j+L5iOoDBruC4rrYz9777z4zDiBcpJdYe6CiOMunyODM3x3fEQ1wNjnrLG9MwWsP4E6yN5IWRsiV+M3N74X4R5SQtNtFkzck//541ivej/a6MNbxjkNVUPFYSyRd2c18oSPHNQncFOBE8CNsaCifIP7AyPj2hSXOgTb/SuTfAKL9RCLtwpYpMaBbEqLFpMYgE+MqWJlzwwDq8UN3gudLqZd1P0pvO0NpXXDl5LYAgUFFxus2lBPa39Re95uYYfqVJJ38dV/UkRjjctKN/BM25nthyUzF/RzO6BkbLT1QFYyWRtOxrsY+GBwBbG8KJQkXklk4h8qrZhYFaGCHWuxjUIQjAyPhEycKeqoej1FSCOKwpZqztdlixxpuuD1wx36/kkMUVs3YUvdY1OFlYj7qnqlxFPYmW9+o7vF+XXulMATKea6W2tMs3jPxi6Z5WPLGuXjttUkq8UHUYI+OlG2a6h4rXduwcGMV5f1yFhi7989rRP4qLn6wuGIJK7rGSSEWGNBdatluMnAoFNdRkkcNcJHJWas4IL2c99R3FvYZccyo8Tmf6oVX6w37d4PdLpaR3Mfbsk6G1b6Sgh8o89zcjJc55X+EyGK/vbA4+ZRoPrWc3HtY6VqkI6f/66EZlQ4CdRbtb8YsXt+HmBd6Cycza3IhtDT14cKV+njaauwirFOtWhiHpDSEGVhKpCIeOUimj43qlgIW7WnwrrF7yVDWeXn/Il2N98761vhynkry6tXj+1qf+sAxSyoI1J/c2p2sIaTn2BTyc1qkY8o43H124raLQ4mU5mKkB9CROZCS+dvdqLN/X5mo/a/qllKkpnCWb87WWhq+o0RLMzyAlcMIxhUvMHLGZG+gnncZoc2+uk/EJf7+g/pFs72Crx4A+V+eWZHrSRe9l1E1ZSZtyY4eVRPJMJ2okpdNNC/ZqbffDx6t8W3Nv7vZm/Obl7aU31FBqmFoaCjF++9VL6nNfXd+FW0zhxsMoEMWV+bI50jOEz926ItDPW7Kn1T4t1gqU5f1P/WEphjzMywvi1ugYGMHmQ934+fNbSm774MraguvNyk1BksKTtsr79KmTxWdzSUj3r/T7bOjMkMgPNw35uzDKiuXO4lDNn4yrlt5hzLhsTtTJKBsrieRZVBG7KHq1luAky/ZlC6s6cw7ioNQC3BIyFlEck2DM0urM5XGyOvrjHTChvmMQL1U3FL8R86/v2jm7Hd/fdYQ92VFTLuxu83OSmLO2qTal58gqwzqVRM2k2f0JXv80r89SKb2fz6ivsa7B4rmVScRKIvkiba2EfkrjqbF+38a6iPO2+zf3IpORRetzAsArW8IJW5/G7y0I1vWorOctKQ0Hfot6QerioZjF23ip0PvVdjIwMo7dPgzJ8tKLQ8Ha1pD+Nersbm8poxnqqNNob9zvL5dY+sUpX/CSr5XzLP2XRzYWvTY8NoEZl83BU+vtRw2wjdcfrCTGSKneDaK4sJvY7ucl/Mel+3H2DUuKgqNsKnMi+iBD3/vqurmFPTvWuTpxXgfRb+bbolTwhziIsh77w8er8IXbV2K8IMrJZAZyuHMQMy6bgzX728NPHJWlvb94mYxBU0C8NDcqZ6TUKsuFsQRGxyighwAAIABJREFU8TbZje5cst9xO7tK4vXz9uA9v5nrOm0Gt+UDIaCcp9yd66Uzz4WnYLCSSJ6lOJ+nEuwKl+aHY7kFgVW5wmFTd+Ect3Ivu96hsZKtjAMjE+jg+kq+ONiuv25imkTdk6gTpCKqocFSSqw50JH9Of+fQusPdgIAXtzUULAfJdMfXtebxx43dab8q2DIbJmXYu+wv8MRtZbAkNBaN9D3CqyLp7ZOPXLu9uyam8wOgsdKInlmvj97h8cxlt6412XZ1pi+oTd2hbXC9aLK+4yg+tWf0oiQ6jbKIpXWPzJeUYV8vyMEumWdu6cqqEX1dSzdqw64o+xpMKXxsTV1gaSHwpWUXGD+jiP49E3LsHBX8XJT5cZk+PVMf4KwGXRSk5ES33l4vS/H8uKVLU2+HMeIdtraV9xjTf5iJZF84xRpr5L5tWxDnNgVLk992zH5n8vtpQiyIMGR3eFq6xvBh//vddy97ED+tYGR8fzPSxOUdzhVdJMWzEt5j4Zwb7gZHdDWP7kmXfWhbsfj9ioW4ibyamdTds7srqbiubNxa+/SSU9GAjsaS88D1l3iyotuzWU4AL2saGNdp3pfPuN9wUoieWZ9IMct0/TTlsPdmFVisnclsasAnnz8UfmfG7vU60iNTWRw/j1rsK62w9Nnh3Gd8fnir5bc+livbTuSf+3xtZNBB/710eLgBHub+9DQ5bxUSZzFbc15deAa1YaBJwWXmxbiNp8m1SlbWdOOv75mofI41kp51+AYOhTz4SheklJWsF6Pums9RhK4Rmu4qd6Jt1sD0quVNZPzip2SsGJfGz5z83JXx+43NTaS/1hJJM9GK2h46VfvWo2fPVd6Da+h0Qn8/PmtqS+o2FXwzJdEj02rfmPXEKrru/DLF7c5foZfD1ovLYoJKcMkWqlet8/ftgKf+P3SkFKjT7eAm4RAZBlFLTHs3lAJ9Tn1evbaUp73UviUQ7Vt7hMJRFJL1BpuGtE86ZmbJhvYnbLFnzy9ybfPbONQVF+wkhgj8S9SFConvb98cStuW2S/IHJSvby5ES9tashP0k/rHCy7Z425pfIN09TZi7GF+WEhpcSCnc0FDzGZ/7/6w4ww2Lcvco5w1q5Yr06UuHqTdi8mRRrOa5ru6DAC10gpcfms7UVLpah4qVcnoTJO6RO3R7vucNOob5etpuVRGroGsd1huRSdtKqGAgPpWacwaqwkkmfl3ITPVzXgthKF+zj6ydOblGv3GWZvzU7MNiIbxu1BEjTz33vsUdMctzXn/y9tasSPnqjGk7l1j3qHx7DhoHqugVFp7BvODjN5Yl2dIh0StyzchwNt/bbH0U4ckYnjnMQY3++qpIVRSewcGMWT6w7hy3euUr4vpX7vJW/LdEjM3N1cLcW4TVTpzmRkUSTsUo2QQdA5pwfa+jE14lri9x7eAAAYn8jgE79fiq/8UZ0v2LHmv0mNmpsUrCTGyH8+sznqJFAJr207UrQunNna3DDMiKPfR8Zc6LzdZg0jVSHbmLO2fG82qmiHovfPrKl7KP9QbO8fLVpuYGdTL+5YXIN/U8x1o+i09A7jcGd2nmEUBSm30joSwKCaMVDyT9b42tr6RvLrG5oPZxdkYvLQ9ge3a5zrtBTQU/6VUYiMq9Hpktp8uDiYUhT1MJ2ANMv3tWFKTCZL37eiNv/z/tZ+5Ta8l6PHSiKRSzpZrFFZqrQ8zlxJNCoDVpPDTYvP5GKNKJdPrjuEs29YgruXTkbKfHxtXcE2RqVxeGwCSqW+xEr74kLSMTCKc26M3zxDN3QvjaiHdenwVAnW2OUb967BhQ+uz33G5Os9itEnErJgmz3NvcrtdBtReevGn85lNzg6jvk7jpTeMEBT8j2J9svHjCcsNsO0mFQSzcFxzr1FHaxGGVdL8aLdkFMqHyuJ5Jt2U8CApu4hPLmu3mHr9Lll4eQcy6gX0o7KNof5BVZOjyrze3bhuNccmIyYdtXsXVikWMvKq8r89oKjDEwSj7KKa7r1qri1gqsCOaiGm/qR7LqOyQYic5Cr6kNdJff9x9tW4hv3rSl6XZUP3GVqKKJ0ufzlHbj4yU3Y2RTdOsNTcyVk1fQRYyRL0h71YQw3HRwdR22bunfQDd2UfvGOlWV/Fqmxkki+MYc1/97DG3D5rB2pjPI5xSaTvcM0vHLC6EmMW0nRJ8cfrZ5veL9pCIndX249Jf/zwlbHeQX/8ojekNEfPF6Flzc3YMZlc9DpYi0mlYTWX2LLWhmZyMiyK1GN3UMYGrXpKSYtngq4Lm8Ocw/gPcuKK3Wv7yxu3NnXUkYBM6V5bqU5lBuJMjAS3T1uDM2ccOhJTNozPujhpuMTGfzl/76Oz9y8HE+vP6QsA+r2EB81bWrRa8k628nHSiIFoitXSFdlrmbzdzQX9EDGxWvbmvBidYPn/aMKNR2W6VM1sg7FKXh5c8Pk0JLcs0p1npfva/OUridya+/VtQ84blfyMclaoq9q2wsL/eYGJa8+fsOSfBCEMOkG3VhV4+0aDlMcRjz89JnN+TPaO1x+RMLo/yLyU5QjDqZYAteYGS+NKIabxvnxYbc0lV/GJiZP1m9e3o4v3VEcmOaGeXuKzqmqzHTUdFZRouYYflAI8QQ08lwp5Xd9SxElXv/IuHLZAZWLn6wGANTd8KUgk+TaT57Otn5/bMZb8a4Tjyl4TyciYH54iv9JiwWdB7eqMH3Na5NBf5wO8X+v7vSQqsmHeqmyb1KHOiaVNZLxi9WHcek//EXBa5mMdN3KvaFEIJQgON3+5veac8GY4mzV/vai16LsGRk09Qyr7lHet+mwen873nXiMXjvycfabhOHZ6f1cisYbpr7ZWRMUUms4OvU+tzXzQcfXn0wiORQmUpV0/cDOJD71wPgqwCmAmjI7ftPAIpDO1FFW3dAvdB6En37/nVFr2ktWhuHJ1yAdNdkKt5v8sUDbc69faWoIiFW1XcVfY63Y5OfrD1WqmtjPGU3TcJGoeVFkeykDdmj8nz/sSqce8tydA6M4vWdzXhoVXEFwbgm4pAXKwPX5P5f31H4HOuusPX5Zm5qwAX3r8OMy+Zoj0xQBa1TBbpjkJroOfYkSimvMn4WQrwO4EtSypWm1z4B4IrgkkdJV13XhS+c9o6ok6FtfCKDX720Pf/7kR5FK5hGPrj7SDYjW6oRrTOtdAp++1v7Avrswv+7xQW6/WUdSiSlLGpxjsPQR6tyUvRCGcPVk+q6Obvwam6t2KiwvpkcvUNj+PcnsqOJ/uGDb8efn/imom3WHujAmTNOCDtpAIDr5+0B4LxOorWCe7B9AGdFlF4df/rmo9GkKtd4dOnzW/M/j2fcRHotfaO+sqWx6DW36ypSedwM+P1bANZulfUA/s6/5FAamG/9Hz+1KbJ0eLGtsQcvbXIu3NnNSTLXKxq7h/DwqoP4Ue4BWAmslULzb3ZzNFsVERd9SUvu0+3qeklYoy9NrL2EEsWFeXcFjOg4DjeNxSC58pRTyXpg5UG09Lq/p3U+Mw3nlgqZ84VP/mEpdjRORjI13rnZFDU8Kk5LMZz/0VNCTUu5jppeHAwmLqyNs+bIyBQNN5XEzQB+J4R4IwDk/n8dgC1BJIwoCnaRS83sCjTWPa9+bVf5CUqQ383dbfvefzytbizQray95LJXptxOKVYh/WXtJVSd3zj2JKp0DY7aLv6cBlsUi4OrdGtEENYdRjpzU3GPgYrOfbm9MbolE8idgZHxgt/nRbwuoh3VZWxc/+89qXhepU7cgjTKZLwvEcTRO/HkppL4LwA+DqBHCNGC7BzFTwBg0BoqkOT5JTpxM2wriRWUyXUMFBcQH19buC6m+TzN29GsPM5KzQiQP39ha+mNFJ9t/125OhyVSafQNOpiUeoo85jP37YiH6H3Mzctw789qrdES1r0DY9jIiPxw8erfDvmHUtqSm9EqXPp84V9DEajYSYjsflQ/MJdmLOd7z9WhVU1xYGfgNJR3dPq729aptXfz8dvcmhXEqWUdVLKswG8F8B5AN4rpTxbSlkXVOIoeRbtarHtNUoCa0/iNEWt0Xa4aSApip+hMb11q3QeFncr1k3T5TT8bGh03PY9HZX6kA+K9WyqGlTuXLxf/3gRfj19w5PXVm37AJZU4LzjR1Yf1Ao8pfs9XXDWn/l2LEqOWstSRX9cms0D3PQGX/nqTvzgMf8aLFSMGANW/++h9cronZV6rWpHdK6UwlIKOAauUZFSHhJCHAYghBBTcq8lYzIJBe4HPrYuR2Ha1MLcy2kuQqUaVoT8BhTnynKihscmQptVdMeSbGHDbs7jYIlF2Ie5SLuvdIaSrjXNPxksUckPajjX4t0taO0bwQVn/bmn/Sslb2jrH/G1N/fNb5zu27EoOewuITfzkx9dU+dPYhwYeZMqAmerspJYIRmBwkHNqOU6pyiomAWkT7snUQjxp0KIl4UQHQDGAYyZ/hGV1No3XDAxPY6sPYdOoa+tKmUIo93fOTpe+GC31gvuX1HrbzoCbI5kT6LPFKfz6fWHbDe/fZHz8EPd3mwdNS19uG7OLkgp8f3HqvDrmZPRjXkZlKdbc+Hus9/ztqLXKiU/pWIuRp4HRvXsVwXiUz0rEjK9OhB+Rh9t6Bry7VjkjZs5ifcBGAXwWQD9AM4A8CqAiwNIF6XQ3/9hGb58Z7zDF1uHm7rpSWTEzELW4aB+FuxVx/dTHAopadI3Utwz2NhdWAAwF8pW2sz1MegGOtHxL49sxAMrD5YVFv5b9621jeCbRr1D41p33xnXLNQ6nmqOMyvolSsOgV90b2fVdnFIv5249HJan9+6AbMoXG4qiWcD+Dcp5RYAUkq5FcD3Afw8kJRRqnQOjGIgAUP4pk0pvCWkBIaK0i1x97L9mHHZnMJhcawjFvD6LPrqXau1tgu0JzEhyzEklWr4qfmVXTZzgAx+FsKMdqGJCe/HXH+wE4MhDqeO2uDouK+VuJ8+s1lru0oKDlbJuhSNBnGlahyKST1MKQ5JU93FrCTGk5tK4gSyw0wBoFsIcRKAAQDv9D1VlDq3Lyq91lEmIyNv5VKVQXY3FxZYpQQeX5ON5Nk9ODmcisWXQl6/St2HRZDlxQrqFEoknaVqdBlDzP/z2eKKSpuLOTG9mkMr08C67mUQVF9x1M8HCkcclo+yXmvXz1Mv8aRqsIpzT2Kc00bx46aSuB7AF3M/vw7gOQAzASQ7UgnFxrt/Mxe/eHGbL8eSUuLOxTWo79CbRO3E2lIoAUzNFSwnMhKZjMQ4xydq8fP5tKe5z7+DWbAwGoHcKf/AFfOUb79QdRgzLpuDodEJraVqdBn38lZL48S2hu6CYDqlnH3DEv8SFXMTE8E36I1benZ5S1YOv6cm+OG+5eo59apHf5zXfC1nxISfeD8ng5tK4ncALM/9/DMASwHsAHCh34miyvWiZdH0bQ3dGBl3/8Bo6xvBzQv34VN/WFZy2/2tffjh41W2n2PN7zNS5lu5pQTOu2sV3vvbeRUTaKESMvdK+BvjxgiHbxc9965cePwjPUNlD5nqHR5DT24UwPSp6sdgVV2X6+NWSuNCGL0Rf3h9b8Hvu470YtaWpsA/l6JnN1Lgxvl7MOOyOcr3orr3kteTGHUKYLt8TqWUoZLEzTqJ3VLKztzPQ1LKa6SUv5JSHgkueVTJDncO4rw/rsb/vbLT/c4uMptzb1mBhbtasK2hR1k5sGb4Uk5mZhkpsaOxN/eRlZHD6T5jrNvF+LlZZIQ9w7FjFBwzEvhfL3mCyelXLcDpVy8AMNmTaGX3uhMjL0i7jAx+bhPD31cuVSVxzrYjjuvqvrrV3wYE7edcwqKbxqUCu7+tP+okkAY3S2BMF0JcJYQ4KIQYFkLU5n5/Q5AJpHQ44iFyYE9ujs+2BudlM2ZtbsQVs3YUvOZlzpJdmbCokgjgcGc2MuPcHZNtJJXSCjZb82Gc5F4V63IeFD3janJzn3UPjuIb965BU1Ek1cmfa1rUhZVSn6O6vv/nha36iUu65N7eFHPWzv1Nh7rwH08XLz9hdrC99NSS6vpO7G/1t3KijoAe35sjLpXEJAUnqmRuhpveCOBcAP8O4HRkl774DIDf6+wshHhSCHFECNErhNgnhPhB7vUZQggphOg3/bvCtN9RQoiHc/s1CyEutRz3s0KIPUKIQSHEUiHEu1z8TRSCvc19WLCrxfP+pbK0nz23BU+sqy94TbeSWJiZC+WyCk55qnlx3QqpI2qznrd7l9u3AhM52d/an18yw3yfvcFmqKhh1uZGbKzrcpwvOGrTa1zqflbNO3KzCHiSfeYDJ3uuI87fwcFH5MwcEA4A+oaLl9Cx0um9O/+etTj3luWlN4T+yBfVdjGZ9qfUGZPKWV3HYNFr1u+douemkvgNAOdJKRdIKfdKKRcA+BqAb2rufz2AGVLK4wGcB+BaIcRHTe+/RUp5bO7fNabXrwTwPgDvAvD3AH4phPhHABBCvA3Z4DlXADgB2SA6z7n4mygEn79thavtZ1w2B//+RJVtS373YOlMTnek2EOrDuZ/nj5VvZO1MGiuWJrLhAzPXijIdQwpnY70qBdPPveW5cre3VNOeKPj8cq6AkvczxvqOotei3PACj+dcMx0z70lFz/p3CNENFJiJMfSPa3FL0bUQ6b61Divmepn0uL8d5I/3FQS7Z6YWiVjKeVOKaUxyUDm/r1HY9fvAbhGStklpdwN4AEA/5J77+sAdkopX5BSDiNboTxdCPEBnTRRfL2+U93zWF3fhY9cvRBztzu3RuvOD9xqGsp67FHTtOckGp6rOqz1OZVon2IYX5yH4VD0/u760hFCzYFtDilao83Ml5vba69UDnLhA+uLXquUSiJRmJotjUd3LqkBkB0pYIjq1lPPSayMfMDcyE7p5KaS+AKA2UKIzwshPpjrzZuVe12LEOJuIcQggD0AjgCYa3q7XgjRIIR4JNdDCCHEWwG8A4B5osdWAB/K/fwh83tSygEAB0zvU8rsaMxW6taVCk1vU8J7cGUtak0Tps2dh3aBKqz5/ar97W4+koh89KMnJlddcrNe3w3z9rj6nIGR0kPcrCqpklg5fymFqW+4eMjhiprCZ64E0NQ9hJ89t8X0mvqK7Bkcw02v79Vapqq5ZzgfAEd3JIxyuGmF5AO1GvNAKdncVBJ/CWARgLsAVAO4E9llMH6hewAp5SUAjgNwDrLDREcAtAP4GLLDST+ae/+p3C7H5v5vjlzSk9vGeN8a1cT8fp4Q4kdCiCohRFVbW5tukikAuhOWjczX3FJnZL4l5xya8ui1BzrQOTCK4bEJXDtnN75x79r8e+bjTBFCPXREt1WQtcQCb3nT9KiTQCnU0KUeklrKfSuK1zlbvb8dbztWHXvtepeVSiDeUQ39Vim9JRQu69InAIpaJDYf6i5aR9Pu3rt2zi78cel+29FJhqbuIfzt9Yvx02c2Y9jFOo2VPK1izQF1gznZS9poKsdKohDiM8Y/AJ8AsAzAjwB8BdkANktzr2uTUk5IKVcBOAXAj6WU/VLKKinluJSyBcBPAHxOCHEcAKPL53jTIY4HYKyi3W95z/q++XPvl1KeKaU886STTnKTZPJZ91BxS2GVYn7Pw7mhDObWKqNgoqojtvaqI6he8MA6XPjAuvw+vaaWSvM8Qrt7V7fgxzpiIetDnChspa7Af3t0I85+z9t8+7xKCVxDFJTBUUUFTfFwtVbO7BothnIVvle2NCrfN6w/WDg6qZzANZXy5KsvMdyfkm9aifcfsnk9H4089/O7PX62ak6icewpUsouIcQRZKOpLsy9fjoAY5GsncjOWcwmRohjcscsbxEtCpRqVOee5qJ6PdYcyGbaqoAVqjmH5rlK1gfInua+fGZuHgpiTYuqlUd36AgD1xQa41qD5INShTsnf8zNXTJsb+jB246b7DksFSDDrUoZZgYka91TSg7de8h6/dkFUTEqMjubitcwnchIZKTE9KlTCoLQ1bT0431vP7ZoexVl5ZT3Btkwr7OdBI49iVLKU23+vTv371QpZckKohDiZCHEt4UQxwohpgohPg/gAgCLhRB/I4T4CyHEFCHEiQDuALBMSmkMI30cwOVCiLfmAtL8EMCjufdeBvBhIcT5QoijAfwvgG1SSvfjhCg0qgqeKk81zxE0oh7uymX0E4oWe91hHxkJ7GvJVkpfqG4ouX3ShgfEhWq+WK9GKHMis1JrDzpF2OuyhFQ/0jOkFRzHq0qqJBIFQXcYc8fASMHvD6zMjjzafaQXdy6ebBzanotj0NhdPEz963evxvt+Ow8TGYkpptLw42vrtNPL4gGlmZs5ieWQAH4MoAFAF4CbAPxMSvkqsr2Q85EdIroD2XmKF5j2/T9kg9HUA1gO4A9SyvkAIKVsA3A+gOtyx/0bAN8O4e+hMihbURQ57TRTVJn2vuw8xpm5aGb721SRMx0PV+Bzt65AvyUwhV0lU7fcp7vsRqVggZnCcN3c3drbBt1IUUmXvN+9sEQA8MqWpqLXVI/WK1/dVfRaW98IvnD7Sty8cJ9WoBojuvl/PrMJb5g6Nf/6C9UNBWsgO1Hd8pU8T5HSJZRKopSyTUr5KSnlW6SUx0spT5NSPpB775lcj+QxUsp3SCm/K6VsNu07IqX8t9x+b5dS3mI59iIp5QeklG+UUn5aSlkXxt9E3k1R1KZUk8rNWy3Y1Vwwl1BVASnVAml9W1lXVbymOu7fnHqC42cRkT/GSsxtdROGnet6ESXPa9uKl7xSjVT56l2r8z+7mf4xd3tz0Wv/8bTeep7sSSQ3kna5lJqTSOQ7VY9bk2IoiNmdS/YXLD0xpJjcXurms7bu2S15YaWqJJaMrkpEsTPGwDK+UAYXIYqYakipLusjXX95neLtWHGktAhruClRnrKCpXrJst3mQ93an6HuEXTep294XJm5qyqJDP9OlDyPramLOgmpcOnzzvNEiYJWqpl2tc16xrrHY3RTCkLSYlywkkihU9URa9uKF2V1WqvoPSepI4+9vLkBGw4WL6cBlH5ofPO+tcrXVUs5eKmEElEwth6ebEB6dWsTHlmtHoK6r6V4LjMRJU+pwTzffXiD7Xs3zt+DK191DoKvO6eeDcaUZqwkUuhU0U1VrEtfCAF84cN/AmAygI3ZFCHw389txTfvW6tcfmFdrfM6SNkhVMUZ/i9e3Fb0mqo1iA8LomjctGByAe6fPrMZV80uDmphh3ctUfLozPjoHhxVvn73sgN4tMSoAt3gM8rANSwLUEqwkkihO1RG1DCnB4P5LVXY/L98x/Fan6tD2cjI5wJRJIwGmgOKqMdElD4HFaOPrP77uS04erpeMfdP3nx0we+605fVU1T09qXKk7RLg5VECt3596zR2s7aGiegP09gZU3x0FLrHEdVS6H+PAS9IahEFDxjaNhlLxX3+pfCEFREyaPzvO0YGMWxR3mLz6jbG8jnPqUZK4kUW9bMt1RI68Ndej2UflC1FHK4KVE0jPvRfAs2aOYHvGuJkkcnwu623DqIOqz5wIRuJZHPfXIhaZcLK4kUW27XNPvOQ/YT1QG9HgPtoNeqnsSE3fxEaSGlxH88vQlV9V3519xEQyYiMitnyOiWw8x7KB1YSaTYKupJ9Pv4iofA525dobev8jXWEomikJHAHMuC2+U0+NgFvCCiyqDbQ6ia2kJkJ2nlRFYSKb4s95IQehHN7Bxz1NTy0mOiGlrKnkSiaKjux5aeYa19Vbet3XI4RJRO1iyEwWeIWEmkGLMW/CYy0teK2MJdLZ73VaVjZFwzHBoR+UpVoPuzE96ot69iZ66nSFRZVta0Ffyuu04iUZqxkkixZc2irXm22wnjFz+5qeD3u5bu95AqdVqIKDqDI+NFrx01TW/kAAuDRHTbopqC3xmIjoIwtZzhcBFgJZFiq1Qmfeqv5+K5jYdCSk0hRjQjio+a1uKeP91nMeuIRFSE+QL57J1veSOmTU1WtStZqSWyeHlzo+d9y2kpZCsjUbwt39dWeiPwXiaiYswXiFhJpBj7p9PfWXIbUUbM03KeAXx+EMXbds010jjclCi9Rsa8xQrQXSeRSFfCRpoCYCUxNjh8sZjODVXfMeD5+OWccX5bRPGmW8hjjwFRevUp5ivrGPZYuSRKE1YSY4LllP/f3n3HSVLW+QP/fLsn7eTZmdnZndkwYcNsnA2zOedE3IWFZVkyS1oyLEhOSzBwJ4p6cICioogBFE9ARFHuOHUNqJyoh4IY8PAnklSQ3ef3R1f1VFdXd1dVV1VXdX3erxcvdrpCP91dT9UTv0+2z37/pYL7/MFmmHsrxRQO/9diDhQRhcff39lvaz9WEomIiLKxkkixxbIhUfl67uU3bO333V//xeeUEFkb3WJvmRYiij4ONyXXWF8JHuciEcVLc21l1mvv8j5ARESUhZXEkOCcxOBxmBlRvFQk+Mij8IhizwIRxQefmBRb7Ekkipc/v/l2qZNAlFZMdG4iIr+xkhgSrK4E7//eYIGRiIhKQ/HJT0QhxkpiSHDkIxERUXy47Uk8d/UEj1NCRH6L4sgBVhIpUopZ8oKIiCgs3M5JXDulw9uEEBFZYCUxJDjsxJ5nXvprqZNARERERFTWWEkMCQ43JSIiio/oDT4jojhhJZGIiIgoYMI1MIgoxFhJJCIiIgoYq4hE8RHFNiFWEomIiIgCFsVCIxHFByuJIcE5iURERPHB4aZEFGasJIYEo5sSERHF19Fzx5Q6CUTkkyg2CbGSSERERBQwc6Hx5q0zSpIOIiIrrCSGBIebEhERxQdHmxJRmLGSGBKsIxIREcVHgrVEIgoxVhKJiIiIiIgojZXEkFAcb0pELvS115U6CUTkAqObEsVHFPM7K4khwSoiEbnxpbMWlzoJRORC9IqMRBQnrCQSEUVYY01lqZNAPnvs/GXYtay31MkgIqIYYSUxJDjalIiIrEzsaMBlmyaXOhnksQRLYESxEcVDTjJ/AAAgAElEQVSRA7xFhQUriURERLGxoKfV8TFfPWeJDykhIsrGSiIRERFRwPZs6Hd8zNTOJo48IqJAsJIYEopdiURERLFRkXA3AM1cXmirr/YiOUREGVhJJCIiIoqIA2xTJqIAsJIYEhw+QkREVP6mdzXhsfOXwe2yaT2tmWujRnD5NaL4iWA+ZSUxJFhHJCIiKn/9IxswsaPB9fFNtZWYMqrR0TFVSRb3iMiZwO4aIvIpEfmjiLwuIr8UkVMM21aLyHMi8jcR+aaIjDNsqxaRu7XjXhaRC0znzXksERERERERORNk09JNALqVUo0ADgFwg4jMEZE2AF8EcCWA4QD2AbjfcNw1ACYAGAdgJYA9IrIBAGwcGxmK402JYm1SET0LRBQ9EuA4UQWFyzY5j6ZKRN6I4GjT4CqJSqlnlVJv639q//UB2ALgWaXUA0qpfyBVKRwQEf1udjyA65VSryqlfg7gTgAnaNsKHRsZrCISERFFz9IJbY729/p570fhc2B0kw9nJYqvO48bLHUSHAt0kLqIfERE/gbgOQB/BPAfAKYCeEbfRyn1FoDnAUwVkRYAo4zbtX9P1f6d81gfP0bJ9bTVFd6JiCKFwSeIoufaQ6bi7hPmljQNM2xU6ATiKEBe0rQ8x/C6KqfJIiKD3vb6UifBsUAriUqpMwE0AFiK1DDRtwHUA3jNtOtr2n71hr/N21Dg2AwisktE9onIvldeeaWYj+ELjjYlIoqnvnY2/EVVX3s9KkscFOaDR88quI/TtZgHxjS7TQ4RlYnA72xKqf1KqacAjAZwBoA3AZjDdDUCeEPbBtN2fRsKHGt+3zuUUoNKqcH29vbiPoQPnNzA2eFAREQUfSMba4o+R111hQcpyXTozC7Pz0lE0VLK5q8KpOYkPgtgQH9RROr015VSryI1LHXAcNyAdgzyHetryomIiAJw05bppU4C+WjZRGfzGUuFjdNE8RNIJVFERojI0SJSLyJJEVkPYDuAbwD4EoBpIrJVRGoAXAXgJ0qp57TD7wVwhYi0aAFpTgXwcW1boWOjg8NNicilT5w0D5MLrJs2tdPZumoUnHxRLlf1jwgwJeSUeRTQResmliglRETeCqonUSE1tPR3AF4F8H4A5ymlvqyUegXAVgB7tW3zARxtOPZqpILRvAjgSQDvU0o9AgA2jo0MJ3VE1ieJyk8x4fCXT2zHl3cv9jA1FBbswQm3pCnfLhpfuGewFDEIin1PBtYiih/vB7Jb0Cpzy/NsfxyA5bIV2rIZJ2n/OTq2XPFeHW+jW4bhd6/+vdTJII8Vm69LHTyD3OM6udFlng8Y1PPZzRXDq4yInGCpIiSclBF4oycqP2ypj68gF1Unb5UqCqi5YWFYZTLv/k4usQW9w90kiYjKDCuJRAExrztFZMR6AlG0eBGZtFi7lvUCAM5ZPSHvfuKgj/Pek+YXlSYiKg+sJIaE0zWMKHqah1V6ch5WJsqTk0KczkmQDI5ojCjm97Lj5fP+MG2pioLndHAdVVWwaEhEAc1JpMJYgCOKNzeV/5FNw7xPCBHZYsyz07uasH5qh+tz3bLV36VO2NZARE6xkkgUEPYAUj5uLg8nxxxgSxSRp4xZ6itnLwEA/Oi3r5Y8LcXskxsfYERxwzEFIcHiWxzwIUt52GxFqK9m21654Z2B3Mp32/jsrgW29rOHpZRSa6uvLnUSKGZYSQwJJyHQWaCINzdz16h8GCuJTgp+xy/q9j4x5Dvm9/Aq5egQO0WGgdGZkVcZ+4CoNPQAU1HDSmIUscxARLBfSH3h5s3YPm8sPnzMLH8TRBQjVtkv13Imq/tH+JMGm/cAgTgabmo+bUUif3Gxs6n0kV6JwiqqjbSsJIZEMXMFJnU0eJcQCrWDZowqdRIoRJz2Mh00o9OnlFAxOF+5fOQaFfTRY+cYdvLgfUwnsXpf43Ulkr3P6Bb7ga8uWj/JWQLJc7xPRFdUfzpWEkNqz4bcN2TzxcYbR3xw0W3icptE4eDkfuzXshJ6Q5GbwDX3nbLAekeT7+xZiSPmjHaaNLLQ215X6iQQ2cZKYkjN72m1ve+ZK8f7mBLyihf1O6UUGwVizlgw5bVQ/vgbR4tVxbEpzxq5QQUdFmR3YFrNUeyzqMTwGvSOna9yzHDrHl7+DN4aP6K+1EkIPYbJCwm3D4ofXbnW24QQUaj5VWCrqkjgnXcP+HNyohi77tCpvr+HnSKEnXLGyUuyA2xwBIt3RrfU4vlX3ip1MihgUc1C7EmMuKheeOQOH9bESyCaOHe8/LjNi8a6mutzOGxYrqpIWMxjtHcsbzneef+RA66P5b2fgsZKYkgUE5qaN474cLJUCgA8eNZin1JCXmqurbRdYssXrGbN5MIRFBnhNHy4zEU0eTOFwN1xW2an5giOaKi2dZ7PnbYwax+7b23nc757gMtr2NFQU3gAX4KFOgoJVhJDwu2DgoULyqcyyesjCpaMb7O9rzFwjbln+eatMwoev7A3x3xnlvF85bbcxxwcrKpk4WJRrjljQTp9eS9+ccMGtNRV5d1v17JeHDzQiQkdDZgxusnVe9VWFq7YzLe4r/gVrCeuWN7zzvQud3nBraj+dszBRGXus7sWoLeNEdXCzn6rfjQfNnGXryEw30gS1t2DZacCqP+WfhT8Hjh9oa39RATVFcmhNFlcKZXJBC7bNBkf2p4aPbB6ckfG9kLLZgBARULQVJs78I7u+IXj7CSbKBS+cvaSUichElhJDInsW3XuokFGIVGi20JBwVjQ24ojB8eUOhlUgJv5QXVVyZz76cwR3HJWMnkbKRkOLwunBb3DLV9PVxK9GG4K/Vypk7m9FkaZFrO/bfssJAusl2N1yzHfh6Z0NrpKD8Bbilu5vjfeJqIrqr8dK4kh4WSumdN5aVRenNxr2IBQhgw/6ar+/HMQD5/VZX9eKm8rJVPBYeGh4aSn3otfbahXsjjbTA2Bhwx0FnnGFBY3woO/BQWNS2CEFgsNlI3PiPJlN3iV8c6Qr0B7x845WDd1ZJGpoiDMHNOMn/3+9VIng0zcNLLZOcLY0Kvn+6G87O4u72YYut+Vjqj2nvjJzneS67fk90lBY09iSLi9V4sUFxmV4oHXSPmwOxwt1345RyKwAFIy79k4GY+dv6zUySCb9DxkVZg3D/ssfLLU/4LKfgfNGAUA6Gmrg9OSx6yxzY7250gWdzi/s/xENSewkhgSTlr0RjWVPrIalYYfN5rt88b6cFZyyu49oNlGEIm87+N4A/lpXs9w1FVXYGKOdRQ5xCxYEzvqC+4zojFVETxpSY/lth4HgcKG5iQW3relyLwPAP961Ez84Io1ePQ8540Sa0yBbwrZz4vXleMWdlu+HtWKRlx0NZdf2ZyVxNDKfXM9et7Q3AMBCxFR4cn8FRRflm+rHwqZ3t1aiwKxDSggdvPxvxw109d0lOODjsiuC9ZOKrhPXXUSL9y8GTsXWPf41FUXDiilO6BlfDsjBE5anF0pdaoimUBrfTWqKhKOyw5O4yHYWbc1bqx6V+1GH2dk63DrH2nd0BdlrCSGRu6brznC2ubpo2weSWHixe904IBy1SiQc4QhHzqh4OR3aKuvLuq9cl0L+pDkpmHF91ZQ9LBxIKXC0Grm1+3RmAWdREr1Oj1W58u7HItp08CY/MNP33fEAD518nw3SStbByxuwJdvnpzxd6keyxNGFO5FJ2vTuhpRX1N+YV5YSQyJfAX/Gw+fnvG3sUApIox2GhFe/EzzeoYXPb9wcNxQo4OAjQxe2j7P/VIjQf0Oua4fL8P6U7aZBQrUFAzHcwZ95iTfe92o19furFJgTmuh1FRVJDAyZN835cZ7f3EOlGFhipXEMlCG12WZKu6Xevo9q3DCom6Md/hgN7v1qIGhP/hQsO1n164vuM/VB08NICW5edEQwYKC95qGVaKywt0Xy6BT3grb5X0gTxAcABg7vNa39xYRLJ3Qlvma9g1Z9jLmuRR5ldpTU2l/KLIb41rdXy/sbyAzVhJD4h//PJDxtzGz5ms99GJOot3x8FScjkb3LarXHjIVo5qGQURw2/ZZto+zunRqq4aGRBzDoDWeqkp6c0vNFaCivSH/UFNbFQoWBErCi6Cy9540z5O0WKYjbLWnmDh2fmpe49zuFsvtxt8liN8o73BT07ZCtxJeUtaOnDM673YugRFN5Tiqj5XEkHjox7/P+NvJpVZsS/OIxuLmOJE995w415PzNNQUP2fs9OV9OHVpD05Z2svWQ5v8fj4bHzC5glg8cu7S4t+nwOsMWx8Mq7mldVXZvQzG32PZxHbf0sMCaErQt8OFfa144ebNeaOW69FSw5Y3w5UaKhbvAe4pBdRa3L+jjpXEclDkU+2Gw6Z5kw7Ka0SD+55Er2/el27sx+Wbp3h7Uirqd9p7+LSCwUNEJP97cLhppO2wiJYZ1HDTsFVAKCUhgnVTnC094SXj9WduUCzUc8LAaN7y++uMW4Ox1z1/ectUEc0KrCSGlN1rVwRorq0qvGMeddXlF5GJUviMjo4544bjS2ctAuDv75Yzuqk+N8q/tyYquTCUg52UTY3BxYJcrmi/FoUjmRgqJpqTbQ7U8fR7VvmcqvJk93oIuhJXaFhsuTOvLFBIOUYGZyUxpJy0cFRVJHDYzE4fU0NRpYdzL8ex8kGzF6LeWSlu22Cuh3C+eci5t9n5lQv2TLFlIZbMP/tj5ztfbD0KnNwKF/a2Wr5uZ01DL6UbcAJ8Wz3ASkbgHNOXZ76XmIfM8k5izc7vuN0iXoCtZ5CL9OQ6/15TZP24+eyuhZgyqtGTc0V1pAYriSE0c0xzVjHu+hxDQqN64ZEzbn/l8SPKb3HXoE3rasTXPS4wjxk+DDdvmY7F4zMjCxasvxU4L9sCyK3jFnZn/D2xg/eOJabIn7qkj116WXlYSpOvJ49qxO3HzMZNW4YqCmNbU3Mj9YioLTZGMXUXEW0zzsLQVldVwSqCOfpvLuX67OUVEEKfOXVB1gW302KuChGQOxLmwOgmm2co07ubRz5w5ExM6GjwrEGmta4K39mzCkdbtBTrD+W+9twRh/MVHhqHuRs6vm1wdEmGtFGmo+aOQX2Jhv8fPqurJO8bNC/meBbbk1jhIJNtnT0am2eMAgCsmDSiqPe1w1j22DxjVMb1uHV2F+7ftQCHDKRGLrU3VKMzzzqIIkBFMoEVk/wLuETeiVung52RP3s29AeQkvBiJTGEhjmIkBSG1iYqLVtDDFkPdG3SSG96VOysd9ZcW4V7TpyLO3YOunqP2qqKgi2fVtdCW311+nXeUkqnr70eP7t2feZaZ8y7oeO2kqgP4V03daTtY85c0YdZY1vwws2bS967KyKY39uaXtsxKYJOLdgWnzH2hbUyFrfypJ1pOH6OGogCVhJDigsoUwY3d++43fFDTs/ThX6WlZNGoClH77BI4UpcZYG1GtsbqtHXXoc543Kty+bddXPx+kmenSvKlLK6o+e+HkrxveX71eM6kiXXU/jggVGuzjexowG/2rsRG6bZryT6HSF072GZ887svN3BA53YPH2U5XX6xIXLDefiMyhoxXzncfu9vCxlF1wzNKJfLSuJEZIQ4PJNk02vFX/lOW3VunDtxKLfk/x3hM3IZGwBtifom/zuleN9OW9lMoFvXLgCXzhjES5a509erkgIPnfaQpzl02eIk/qa4oefNhR5jmPLqJLoxf3u0Jnuh+YWasQJ2ljTnEE7309tVQVu3zEbIxqzh5r2ttd7lbSy5eezpJhzMwCifyJaR2QlMbQMN2r94vr1TZtx6rLejN28uNk46bWsqUwgEfPud79VOSxE5Hqox7X1P6yah6WCPOxZb2+Ow0Ul6oXzIhLuwJhmzOtxFj48zEoV2nxRXytqq4qvJHZYFOa9NLol//qeYRJEm9iBA96cx6+GIipfxXQcTOuyG8egTLCBvCBWEiNOvx0Eda2HdSx9OXEaer7YQv241txBUsi53SvH46GzFme9XpEUvHDzZmybO6ao83s5JMiv4UXldpcQAfqLnJvqJJvq++aKHjm101lY9kK/x1kr+yI7HMopJx/Tq1EW7Q3V9t9T+/+ccS2uG4oGxjS7Os6N9PxEliZ9Z6f8FZNsXBLXHzo1/e8fX7XW0TMhqkN5ma1DytgKn69lyO6FV1eVxOdPX5j++yM7ZrtPHPnKqtU/36/spBxj1TK9a1kvPnnyPAdnoXwuWj/JspAW9CPCzq3BqoHBi3JxRTKaD8R8Vk/2P7JkFouv8alLVuJzpy3M3lCEQsOCI1q+KRljHjptWS8W9dkLow/AkwBSD3h8feRzQE9vnovkgOEL+dixs3GdobBN9u65Jyzq9jsZsWP3Wffl3Yvx0FmLsdOwTFBzbRUeOa8815I1YiUxpCqSCXRpUcPyPaDtjvwc3VKLwe6hiuem6e4m3lMI2bjT5dslmRAsncAQ5X6zKkQ5LRBWJhO293XaC6KH5d9cxL2hV1u6oyIx9Gi5gHOYPTW6pRZ1DpfJsLcId+6dxrSUz1p34wJYt+/i9UPX/HtMcQQK0RtuiqmYF7O+ndNGohsOm4ZFfa15e7dHNg71pG6YNiprTc64sdeA5386wvCepWR3FNaM0c22e+dH5hjaH9V2NlYSIyDfDaVQT+LHjp1d+ByRvXzLk9PCQXtj4aFMbfWpfZwMe4qLnjZ7w23dFtryDUnR5wPbaewZO9x55cCJL+9eggvXTsTJS3pcn2OUtmbaovGtto+J6xTnoQYCi8aDgNNS6BkwrCoZ6BBGP91uMYom9xB/d7/Eqv4OV8cZ37FUz+WkwxvdtK4m3HfqAlRX5F6665pDsnsOh9dZD6Wm3Bj13r27js9eVsrT6KbaDf1xQ3Rfo6iOxmAlMcQOpFsUC19duRpExg4vXAC2uvEE0dpK9uVbfPnERd04cXE3Zo3NXYg7eu4Y3LZ9VqyD2cwPOJDK9TaGVOkBLuwEG5g+OhVUwO7DxklrtQgwpbMRZ6+eUNTcib72enxnz0qcsbzP9TnCyutW9iALDbYqHBEtxDhlFYTIuP6gnfVMA1Gi38OLuYV3HT+Y8ayxCr704JnZ87bjyrpHy90N51+Ompn1GoeqAq319hrIm3MsP2VXvY8NuaXASmJIWBUYvJib4KSiaYeIN9EPyZkts3MvZ5FICK4+eGreB0EiIThkoNNVZNphlblbiKPkQ9tnFXW8Hy37G6ePxNopHbYCVNgfZmo/fxbqtTh4oBPv3TrD9vkqEgmMGV7r6H7zmVMX2N63nAR5Gy30c0S1lduNQvk4mZCMBrfbj0n1PB63MJgGtlI8XlsMBePNM4pfBmH15A5cf9i0vPuYl96Ig0MGrL9bNz95Q47KiDlC6XlrJlj25MaNMdc/fkGqt88qr31l9xJH53322vXoba/DVQdNybtfTUTLUeVV5Y0wq4t1aCiaF2sh5tuWe66UlWSCbQt+svrui5ljUqyqigT+/s/9JXt/r7SUaHhTvsae2qoK3Hlc9jAYy/No/7dbAXNy28i1721Hz4SIYM8XfpK1rbGmAq//492M185a6bwHcX6v/aGppeJFHWryKOfRUcNUdxtWGZ/7vvF73zxjFDbP2AwAuPfpF22f44tnLsLb/3S+Fob+3Hfz2z989hL88LevOj7uO5eswrSrHwVQfj0hYXJZen7q0K/7ld1L8Pu//j1rX8syYYHapNV88lwRkq3EZTjr+BG51/Ic43AkQV11BZ64cEXefbpbayNbSYzPXT+CDhiGgpmZhx/mytoqzzl0dhZr7ja0+p24uLvg/hQ8dvDm5+b7+ewu971c5rcLY29Noe8kX4X0vy9bnfWa3SE9UVRs9vJ7qPdt22fh6oOtW7OHVQ0VUPraracgFLo+P3h0cT3xYWFrGLYH7zN7bAsW9rloALHxzM5lWleTq6AwtREtwJaD1DSCoSvuGi0PWy2V5PczvpzLEJaj9QKqFEc5FgQriSGRd7ipxbb/OHeprTlP6VbJfJXE6gpMyNOyAqSiKgLA0gltGS0iHz9xbsE0kDNuKxPze8tn8XI/5Hog6MMzrebhDo5r8TVNfnDy2KvWeocKtXJaXZN2F3kPYd24JJwMwXUzpP+QgU6cuNg66NBNW6Y7Pp+Z1dI8UWTnVxj6+rP3XjrB/nIWbpQ6cA35J9ctwBiQbM2UVNCj2WMznz3mYy3vEJblSOfTD8w2Tx+VswEqSk5c3J1R1nWbx+ZEsFzgViCVRBGpFpG7RORFEXlDRH4sIhu1bd0iokTkTcN/V5qOvVtEXheRl0XkAtO5V4vIcyLyNxH5pohEMjKH9dACfdhJ9oU8saMhY82WQ3ONdc8TQc/IPFnXPHWtqiKBb160Iqs1ecWkEqwdVoZeuHmz62P133hU0zBP5w9ON81tiLqqHBEZulpSS83c5/PcuKALfXbe74RF3Thn9YSCEU0fM60H1WBj9AGFR6FIkiLxqczbCgSn/d/8HHzh5s345MnzvU+UhUADG8Xlxw+pJePtNTwYK3x2f7J81/vkUUPLluhLrpn95Jp1+ODR2cFwokaQit3w9QuGoo/efYK9qR5G3754Je49ydm60lFu8AmqJ7ECwEsAlgNoAnAFgM+JSLdhn2alVL323/WG168BMAHAOAArAewRkQ0AICJtAL4I4EoAwwHsA3C/r58kQLkeVFbWTOnAdYdmBy8ZmsdU4L1MlVTzPEiR1FIBUR1XXWp7NhQOTDK3uwUnL+lJ99q6UYo5BYfOLD7QQRBExHKo9O3HzMbtx8y2fEgaH7BOv1u/h+64mf9nVlOZxAVrJxbM1xMM0R+fu34Dvn/5mqLfO+70IFJeF9B7LYaTFiqkFFOIKWbJlFKw9Uk9DvjmhJ0pIn7ZvXJ88G9KEBF0NhXuqT95aW/638YgdG31qUYgq0vG7nX05d2LLXsdG2sqUeFFyNsSs/oexo9wPk98bKu/S1GFTSC/vFLqLaXUNUqpF5RSB5RSDwP4DYA5Ng4/HsD1SqlXlVI/B3AngBO0bVsAPKuUekAp9Q+kKpQDItLv/afwV755h3YfVMct7M6KYjXUG5lfFOZPlatPaK1SD5y+CFceNAXJhGBhiIJ55LsWZo9tLqpSW2pVyQSaa6uweYb7BeQL8SMvvXDzZly83vo253fltKYy6aixqIynuRTlis2pIBbze3Lndbv3/gdOX5j+95cLROez+j3idL8Pak6iW3XVqbyVq2fHDyKC39y0CReumxjYewLAe4+wHzk5LvLl+ZOX9OAHV6Qa6IydByO1CqbTRg3j3q311SUNkFfWInx/LckVISIdACYCeNbw8osi8jsRuUfrIYSItAAYBeAZw37PANBrQlON25RSbwF43rDd+J67RGSfiOx75ZVXPP08XrAq2N136nycs3pCRnhqOx43dKdP7GhAc21lwRD7ew/PDFf94WOyFxwm71UlE1g+sd2z83lZQbBTSIl6BWDL7K6824339lzDVXMp1VIxm6aPBJBaszAMDpRBNAQ/epQOndmFb1+8Emevyu69WTy+Dc21ldhl6DnIZ5whIp9VdMqCS2CgNL1mYaVfsi5WDCrarLEt+PAxs3DtIfmXkPCaiPh6DdxzwtysSuG2wTG2etBoiB6E6pj5Y9Ov5RsJ4OQXDVPjtNes1kelwgKvJIpIJYBPA/iEUuo5AH8GMBep4aRzADRo2wFAL+W8ZjjFa9o++nbjNvP2NKXUHUqpQaXUYHu7d4VyP40f0YAL1k50fOM2hvetq67Aj69ah6UTUp/5IztmWw5T6x/ZmPG3caw6EO0x1XHiZcXg6kOyJ6qb5ynmCpRRjkTEVY9juo7kIgvdc4LzwFDbBsfgues3hGYdsgMHol9JrPaohf1zpy3M+Htsa63l2qVt9dX48VXrtMiH5CU7z9MDeeIBBOGgGZ0ZEWnLwcr+Edg2mB2xk40T9ujXYm1VBZ67fgMuWjfU8J+3odnB9ysiGN0SXA92kJwubUEpgVYSRSQB4JMA3gGwGwCUUm8qpfYppd5VSv1Je32diDQAeFM71FhjaQTwhvbvN03bzNsjw+v75ENnLcaTF6/Ien3T9FE5h6lReBSa/3awIVCRcc9PnuxsQnU+1RWFCymHDHRGqvnA3KnlON85qO8Mdqeiza7oTz3A3XxPK/uHAkM5CVRQ7NzhCSPqPetFOWLOUMGwFD0zXvAqqNG8ntJEIC70tYtYV4cePGsxvn3xyoLnD0Nn8a5l9npdczE+M3vaUvM67SwPRcXZXwaNSLl4GUjOqKYymVG5ntCRahyO6O3Vd1bztAuxG0zIjij/LoFVEiV1Rd8FoAPAVqXUP3Psmo7XopR6FcAfAQwYtg9gaJjqs8ZtIlIHoA+Zw1hjaWBMM8a1Os8YAPCBIweyXmNjX7CMhS5zMCIAOZc/8WOdOqfzWctJMdf9tK4m/O/ejZ4NJw6yxf3R85bhf/duynhtUkcDDp+VOTw336LEurGttbj+0KmeDqsOWk9bXSgqQjkVeWnkOnzmmObQ9EgXsmJScdeX8Zl5y9YZuOfEuenKIvlHr+CUqgHFT4+aokLn4+b2MmdcCz5w5EDee1O+W4PV0HQCnr9xk6cN7lEWZE/iRwFMBnCwUurv+osiMl9EJolIQkRaAdwG4FtKKX0Y6b0ArhCRFi0gzakAPq5t+xKAaSKyVURqAFwF4CfaMFZy6NpDpmLH/LHYOme0q+P5QHUhxx3ceM8vh/WJomJBgbUmnUY4rUgm0usJjihynbkg22kSCckaBvno+cvwL0dlhkK3W7DbubA7HaApjsJUv/TjOopaI2KhwntddQVWcnmnQHz4mNm475T5aM4xZ6wxwr25ThpYhpYrs9Y/MjsS5xfOWJRRXnOaD930sEVRvq/lPy9dlfVaMuHt/Nyo3R+NgloncRyA0wDMBPCyYT3EHQB6ATyC1BDRnwF4G8B2w+FXIxWM5kUATwJ4n1LqEQBQSr0CYCuAvQJx0gMAACAASURBVABeBTAfwNFBfKZydPyibuw93Pmiy/PLsAWw5AylyrDP2Qh36vIzt8B+dtdC6x01Bw44f4+ZY5rx3iNm4MbDgw1EQeFRYapw1wY83yzfPaStvhqJhNgqyFgF2Ymi7rZo9I7GQdOwSiwa34YLLCKr7j18Gj6zy9/1a730+dPzPz/y0efBmpcfA4Dbts/CkYPuGu9z5euetjpM7TTP1ipPQZehTity6HuYBLUExotKKVFK1RjWQqxXSn1aKfUZpVSPUqpOKTVKKXWcUuplw7FvK6VOUko1KqU6lFK3ms79uFKqXyk1TCm1Qin1QhCfyWthH5afL4tdeRB7urxWyrkwU0Y14pHzlpbs/cPEq4fLtsExaKgpLrpayNsKbPHyYa2vDRaUYtYgNQ61ffjsJfjWRSuKTk9l0t13af4N5na3ZO1z0xbrxsKzIr6O3vZ5qYiQDMQWPubgeQCwY/64EqTEne/sWYneIgLHHcgTUfeQgc68907zvelD22el7w+5rnUBcOyC6Hy/xciX2/0IrFZOwX+4KEpIlCJM/L/tnOO65Wv91I70vwsNkyDn3lfE+lFfOGMRvnau+0revJ7h6B/ZmLGEw0llGsXUaZ2lmIpCubOqbJg9dNZibJ6eHSH2wrXO12drrfN+/q1fbjHk52ldTUUPPQaAX+3dlF4eqSKR/1Ge7zLX80AxFacwzte0augqh4YWKq1cowCKrRiMHZ46vtoQ7KbQEk265RNHoK2+Csct7AaQCmy3dXaBnkcJ/yglt9obMp8NZfoxAxHdwd5lphQP2fVTR9red7QpfPDtx8zGP/ebQ0U6e/+JHfX45Z/eLLxjDBUTgGbOuMKF9Xz0oXEV2rqAepROys6nN2+Zjku/+FNUJgWfONHfOXdhfM5dvG4SkiK4aP0kW0Mop3U14dw1E/DVn/6x6PeOUoXdTqRgNz567Bzc/dRvcs7nslLMdRSlwpZVz5SefOPnKNcGMPJHriyQK0Lww2cvwUEfeirvsQBw53GD+OFv/5qxnt+t22bi1m0z8xyVMryuCvuuWJvxWrrxXnvT6w+bhisf/FnBc+maHa7PHSadTTV45Y2303+XeuRAqd+/GOxJDIkwLjj9iZPmYcvsLnzs2Nm4ZWtmz1ZFMpFexylKBQcq7Jw1EwCk5oo8dNZifPDowg+pqPB6gfs2rTK/dEI7FnkYMttKGFt9W+qqcP1h09A0rBKVSXuPE6ufYK1hZIJdUX7wemVBbyvuOG7Qcq1FI+Olo0eTTG+z+B6dZpMwXJpOrgfj5xu00QNOpVPqotGAaa1Sp/fhaV1N+O5lq3H9oVPzjiBora/G2inO74O56I1oemrbTcPzC32Kr+xeknm+gH8HO5GzzbqarXtz8/1kQZS9w3B/dIuVxJAIYyVx+cR23LptJjZMG8VQyTHSaJg7NzCmOR2dE4hW740dTrOdeXf97yCeARF+zmSwuob6RzbiHIdBUYJ+8PYVMd+olL532eqMvz+0fXbmDvpwU8P3meu7jXrFvKoiVeQpt/tYnARdFnFypeTKNx2NNdipDQUNit4jWat9X46fdRHLIh/dMRuHzUqtHz3W5fJvlI2VxJCwWguP4s0q5DWVlvnBqfdMBlJhiXb5PK07xwP8gnWTHJ3HKgqgn450uTRQqRl7L+qrK5A09TpafYtlcqlluUCb+xr1yi4FZ0FvKzoaozP/WXfhukm46qApOEibA263zmcepqoLe2/Yxumj0mnvH9mA/7p0FR4+O9UbmjfoT8Qqw0FjJTEkxo+IfoXA6T2EmTP/d/a1c5fi1zdustwWpu8u7A8Po0mmeUrFpn14XWoIz7gAWi7LpWBbU5n0pAHk0JmdHqTGvjAO97VLv3b0T/DgWYuHtll8LscBnUJ0P8pHjzCs9yhS+I1zsNagH5qGVeK7l60paRrcqKlM4qQlPemh6OY8mut+ll6Kw9SYZAwCet+p871LqIeMH7HTMPQ0b3TTqNy8SoR3SiqaXlCe6zDAiTkCVRw9kCe6rEj2gua6yhAVcoZVBrvmmxs3ayH9t88bU+SZhh4oZ68aj8Hu4bj7hEFcsqG/yPOSU4fNshf5L47ef+RA1tqMANKlpZljmnHykh7jS9hvIxR8hOvJWTZOsx+4jUqnoabS1fw0v0Q1C5iHWB+jLQdjnge5c2FqWYwWU+AaY3ltUZ+/8++d0ofWuqnv6eVXP0X5vsmJZlS0zuZheOS8pRjdUovPfv8l28dtnD4KpyztwcM/+SO++MPf+5jC8NArhUd+7GkAwIzRza7OU19dgfk9w/Hd3/zFs7RdtqkfEzuc9/BcvKEfn3j6Rc/S4Ydl2jp1xfYG6Q+hO48bTD9cV/V7F2wgn+ld5bPwcZwab3fMHxv4ex4xZzQ2ThuJP7/5ds59pnelAnLoWcIYndZpr3WUC0EUfsbennK41qaV4F4+umWoR/bxC5anK953HjeYsd+ZK8bjzBXZ88MPnjEK9dVJrJg4wtX737J1Oo6aOxbdl3414/VkQmw1UOXynT0rMUaLvp8O1qNdI7mGzho111bhR1euxazrv+46DeUsPN0RFGn9IxuRdHj3FqQK2FFaMNctvdA1t3s45nYPx32nzMddxw8WOCq/Bb2tAJARMrsYu5b1YcUk6weA/lCbYDEsutyCGuWbH7xnQz9mjG7Cwr7WwNLzyHlL8YmT5uH4Mpq3PLLJ+TqBxS7tUioXOpxr6ZW66oqsYdDGO7S5Z0FEhtZWc3ArF8Sr0k/BazAEUzPORbbsLY8AvRcvSDPHDDVIt7tYYktEsKq/o2Ak5ZzHazeVTtO9/9CBTpy4uNvx+WaPbcZTlwxVEAFgQU+rti31rJg4sh6D41pww2HT8p6rxefexCiXkVhJJM8Mq0qiztAaXWjeR5zLFYvGt2H15OJ6oM5aOR7vP3IAB8/IXpzca4fP6sLjFyzDsonhGmbih3zLZEwa2YAv714S6E2/f2Qjlk9sj/ScODM3y6rsmD823SLMoerODLWwD11D+w+k/m8sdJvD5ptZvR6G+/jnTluIQmXXxhrrPMsKbvj927FzcP6aVNCh7rahho+uIhewL8YM09IYTpTqXt6gPbekhCX/vdrUD92NW6ZbzJcsfJ7xI+ozekcBYGX/CDxz9bp0A3p1RRKfP2MRZo0tXQPjJRv6cfOWGYV3DClWEslTxvHtXzt3aQlTUv6qKhI4Ys7oQB44IpIVXOni9bl7SBpyFMiIgNQQHyf2bJiEQ2cOzUF84sLlXifJN2Go2h+wGHY1qPXMpnsPDaLWIDGvZzgGLebEX7RuIh4+ewkePW8ZnrhoRfAJI0fuPsF6dM3Iphqcu2YC7jxuEPecMDf9etARjgEAklpD8JMnFxe85YkLl+P+XQs8SpQ92+am5uTXVJQgjoD2U7XVZTbw1VQm0dOWOeJhu6mn9a7jB/HeI+xVtLwaWeWVM1b0+d5T6SeW5Mg3hdYVM7ZfZx5Xh+dfecuXNJF38g0RicowIKteBHYshI95jkxlMgFgf2kS41CYgkwZc2V3Wx1euHlz5g4uLv6w5PRkQtDbXodfG54du1dNyLn/qKZUL1QQgSvInkLzu81BVvQ6Ym9bHX79Z3/KDFb10OlF9CLqetvr0Rvw2quXb5qMi9dPKkl0X/1rtPrudi4Yh8mjGvGLP72BKx/8WdZ2fdTVns//JP1aRTI899Vyxm+ZSkYvj1Qlh1q1zlrZh8+cGmzrmtduPHx64Z0oUDkXB7d4fWWOeZnkrc6mmqxhu+esyg6YYEUkGg0RIxuzP2MpHFDZw03zyTncNMfx+YZo+2XXst6ijt+zYRI+umN2oPOLyRtVWgWhUZurWJEMx70g7EOXEwlBTYmikRt/o1GmeYmJhGBeT+ZIAKulKYw9jueuzt0ARN5hJZFKzhjp6+i5YzMWf46izQHMEQyDqK7bt6bAXNCV/awkBuE7l6zCM1evy3jtgnWTsqLt5dJaX429h0/D9y5f7fi9e9vtrWvZUFOB/7p0Vfrvb1y4HOun2p9LPLUzHBFp9Z6DQhUit2XcIoITujK6ZRgu2zS5qHPUVCaxcXo87tVRlK+x9X+uW48rD5qCD2wbAABsnp65ZmpQwzgv25S59BHX3EsZaSjDPXz2Euxa1ouDZgz9RvrX9Kl8Q3YtvsoHzxxa37UjzOXEiA3Xz4eVRCo5EUnfVMLSIliMpmGVaDUNYepoLI9AG+XwDLzzuDmGv6J/vUVVMiFIWvQGmoeUWdEbKHbMH4cRDc4LC4+et8zWfjNGN2UsytzXXo/1U7PX18vVAxqWskJjTSUeOW8pPnDkQN79lMoMIW+W6+NcsHZiEakjynZMnqVjKpIJnLykB33t9Xj+xk04bNZQBeSMFX2Y3+tf77BxBEPzsNRz/sg5qXm9+w0PSDfBucpNVTKBaV1NuGzTZG2KQIoeICvX+peCVCBEs6bacM03zEm7DhaPj/4oBVYSKRQ2aS26xlDXUbbA1GJ/eJks/G1VqPfbT6/J7G3qH5m9DMcvb9ho+3xRC8oRR4+dvwzf9DHQSKXN+Sx2A2Ocuyb8laT+kY22h5o5zSJ6QIySK4NGLHImmZB0ZQ0YWhrKa4+ctxRNwypx61FDlb8DpkaV4Q4DcpUTYwOUXlnONXcz1/qFR8wejS2zunDB2om4w+aIkjDS130sFJcjClhJDJFhJRorXirG+8Plmyfjx1etDcX8HS+Y5+iUS8Vks2F4VlAfyVgxffba9Xjg9IVZ+xRKi7kH9NKN/dY7UihM7GjIingH2Fsc2W+bTEMUp3U1ZjWe3LFT762OVr7fr32/XkeNrEom8NEds23tO2BYzy0XPbLyp06enxFlmXXE6Nu9cjy+eOYiR8c01VZielfxwWSA7IXu9VEL/SMb8czV6zLmrB8w5ZeKZAIHxWS6idGpS3uwdc5oXLE5NQR8YHQzjpk/Fv96lHVvqp5PzXeZYVVJ3HrUTLTWV6OrOdjlTe48bhDXHDzFk3M1ahFW21ysRxk2rCSGyJMXr8BXz1lS6mQUxcl6NMYHejIhGWHxvRq+5DZyXa9WQHU7TPTAAVeHhV5FMlEwYMh5a7ybUG4cFjisMom66gpXvc3mMm+5NEbEjR7e3K+gNZM6snupzWoqkxnvf+7q7HtVuhAUrTpiumI+Msd8H6vPY2d48DHzx2bN/6u0mFpw8fpJ+IiNyqS+HMqSCW04a6W9YEcUDRetn5ReDN2JZp+GIm6dk3sUkD7nePnEdl/eOyoW9aXWT9YrdhUJwY2HT89Y6N4oqd9IQnR/XDulAycs7vHkXIfN7MJ7t87AGSv6PDlfKbGSGCIjGmswtdOb1rBS2blgHO47ZT4eO9/enB+/maNo2XXumgmYNbYZxy3sznj9WwWGwD15cWr7KgY/SXNSUDbPmZpkMbTUjVw9I1ErxMfNY+cvw+cNPccPnrUYN2+Z7knP/FGD2UMkFeyNALjbsFabXkmqNcyh0QPFRG290HNWjcd9p8x3NKfL7fyvKaOyg/qctqwXIxqyG+YW+jjHjMqTV9Ev8815HhjTjJ9cs44BkEzM91GzT50yD6cv70O7i56224+ZjYfOWlx4xxJKJATb5o6xPa0hzKL/CShUEgnBovFtmGijRb7SZm9AMV32bgOtdLfW4UtnLkajqZBnXl/oP85ZmvG33tNx5GD2AtVx9eOr1mGDRbAPK1vn5P7ejA8ec29DriupXStwsi4YTRM7GjIWSe9pq8PR83IHtHDiHIseb/P9Itd1s8yi5+BbWgMRACyf0I73bOzHNYdMLSKFwatIJrBofFsg73Xqsl7sXDAu4zWrxpyrD56C05YXt9wFxc9Ilw3ETjWWSRwFp2aNHRoWrj+b9bmIhWIXjB/RgEs39rtq7Ns8Y5StIenkDVYSyVfb5+UOaGB3qYgvnrEo8EKC1b1ri0XwmSmdjRmhuvX5C8ab3+nLoz/kwAlzQbtpWCWG12cP+zUPacs1tDc9D81hVe+yTf05Gwk4d4nsrO3npAxj7HFIJASnLe8ruwKkiKQjORarrqoC1x82zXT+7Ir5iYt7bOdX/Te9ZAPnHMedVdYNYkka/bZSiiBvQfr86UPzRvVns97IU10Rr/ga5YyVRPLVijwLk9udWza2tRZbZ7srmBhDY+dz0brCcyBvzTkJ21CEsXgulFuQlAMWrYXfuHB50ee9f1dmQBo9BLr+PrkCApgLmroTjfMLHIb0p3jTh47y+sh2xeYpOHlJ4bk7bgJ4mHsW0kOCbdYS9eitG6fZG7lA5Sdf44/VEGezjdOKGzr6rhaQwOvgT2GmP6PXTunAqUt7cNVB3gSAefo9q/DUJSs9ORe5w0oilYXv7FmZETJ/6YTUkKm5hqFq+exelTn0LFevldXj50DQK0mXmDmiG5AZ6tnOs/GERd1Z8xa6TdEsq7Tx/DWVSXzv8tW4cYv14spHzx1jOWwlNR/A+rc5fFYX1k/t8GzeCoXX9YdOTd8PjKzKkvpLzdqw8XKJSuylptpKXFmgEPiV3Utw29Gz0tEOrYxrtQ5qYfzOZ2pD2grNcdLdedwgzl8zMee5qXzZ6cE710ZQtYNmjMKv9tpfUsnsWG0I9ZxxzoPvRInxW9bXO6xMJnD55ilocRkw0GxU0zCMbmFeLiVWEikwvRYh7XPRb/jHLRyXf0dNZ/Ow9HzBSR0NuPekefjuZattRVs9dkH2HKfJo1JzKs1FE2Mr5V3Hp9bxmTPOXkW0XOhDdszP4g9tnwUg9cDI/t4y/77mkKkZQYGM4a6tCqAjGmoyF+M1nC8pqUXZBy0eyvpyBXVVmXNL66sr8G87BzEiRxTHw2ba64Gm8JvX04p7T5pna9+L1k1EV/MwnLC4GwB7Et3oaavD9NFNSCQknb+sohz25lhDzHiP1aMgGhukHswTtGLM8Fqcu2YCK/cx1KpNaajLE7laX5ogH/OzxqmlE9rxws2b0ZHj2VIuEgnBbdtn4alLVqIz4OUqKDisJFIg1kzucDQkUR/SqC+LYScATWdTDS7Z0I+7ThiEiNi+SXe3ZlZexw6vRUWOh4QxHXrBZUpnIxq0B1McyiafOnk+Hjh9YVZB7OCBTjx89hIcNCO7grXfYk2QXKHr7XyFxp4FPRl6a6bR1QdPxY+uXJu34GDlX4+ehRdu3uzoGAovq0qD+Z6y74o12DBtFP7z0lXoaavXjgsideVlYd9QJNKDZ4zCvx83iBMXdQNIfcdA5prA7z1iBgBg/dRUlFhjhbBxWCrfLpswFCio3SL6KdENh03De7fOwCwtqIl5gM+EEfU55wi3aRXM1f0jMKyK8+nsOmSgkz19ZY6VRAqMk9Zd/f6eb+53v2F5BNHOf8aKPsc3LUfpMj54YlqAbKmryjmMd1qOBY3NC5AXy1gA0H8/q4aEZEI8G/pC0TCsMokvnLGw4H7mIYzGKMqdzakGJifrvlLKtYaIriKCNVM6kNBu5LUWBfAGUwNOIiF47voNuHnLdKzXoiInEoLOgKJVUjQ11FRim2HqwX7TA6HVIniabt8Va/EvRw3g1m2ZcQe2MUo5xVy0FnGiyHLaIj9ZqwBO1iaaW81JCWJIkbnSM2Y4h1U48YTWe5wvgJGZPiRoRI5op6nztWNgdFNGMCFzoYDi45QlPfj3p34DILVWaa5hxMBQMJR8Iw2mdjbh8QuWobfNekhkLuNH1LsOslUunA7V03OtcR54TWXSs6VOKJ6mm57dV2y2nkerz2E8fFZmvuVIEiL2JFJAnJbfN04fhccvWJ5uSTY7dsFYHDIwNKzRSX3xgdMzexny9VbONvUkiEg6CIbxMFZPMunBfIa76MXbMqsL7ztiBnYtzb3sSWNNJR7avSQjYM45qyZYzkukeNEriPrwY/3eoAdRqdfWPq2pTGLlpOz1DnXjRzSke8DsevyC5ThjRTyWvNmzYZKtHttCKrTvuLm2vJYLodKaaVpLL9col6cvXRVEcogiiZVECi2rOWa6Gw6bnrWwfS7moBXD66rw3ctWp//2oj9SL+BUGVrRf7V3I567foMHZ48efZie3fWSrjxoKApiIiE4cnBMznmhuYxtrcXnz1hUeEeKhX/ZNhNrp3SgRwuYpc9zNuZ3fdiyuUBJhZ25YrzjoF1WjYVrJnfg0o39uMJm2Hw761sSAfYaj/ONOiCKOw43pUAUOxm8yyJ6lrGwkG/o6bKJmb0FAuuhZrVVSfztnf0F02K1TuBndy3Afz3//9LrdAGpYVeVMZ0D/5Eds/GbP79l+3ffUOTaVERm00c34c7jBtN/67cLY++gnpfPX1t4nVSzyqRgJOfJOaJ/38YO2kRCcPrywr2v+y3uu0TFsJojS0RD2JNIvlrdPwKnLu3BdYZgBm401FRmrTvkVYNyvRbx7GvnLrXc/vDZSzLW+zpeW7phsmFh3tEttdimL/xMqKuuyBrek+v7JQqCXrmoslhKxU214+fXbcA3L1xRfMJiRG/Mc1O51qOaVhWxPAHFi56/79g5J2vbJ06ah/+5Lp4jfYjsYk8i+apCW1zVD3YXWDZLmHodD5/VlTpfjtNN62rKqPCsmzqSk9pdMFaqiYJ27IJx+L833saZK4d6rfR7iPmeYIfT4dCUWp/01m0DWNTX5vjYu4+fi2//6s9orecSGOTMcou5x8sn5p6PTEQprCRSSXgRpGBBb2vhnUy2zxuDca2ZS2ToPQzvamv5VSY5nMkvbfVVOGIOe1zJH5dt6s+5raYyics2Tc58TZszW8E8X7THL1hma78tLqO/jmiswRFz4h05ltwxRs69YvNk/PC3r5YwNUTRwUoiBeqowTG4f99LePjsJUWfa8Zo+8EmBse1YOzwWty0ZUbOffQgFi21XFfPL/uuWJvx9/uPHChRSqicDM1Xc9a7d8sRM/Dp//4t5uVY95PsGz+iofBORCV2Sp6o2USUiZVECtQtR8zALUfkrqg5Na9nOL73m78U3M9O1MsJI+pxyYZ+bJnd5UXSyAb2DJAX1k0ZiXv+8wUs6nM2uqCtvhrnrpngU6qIKAwCWFKZqCyxkkiR815DJfPjJ87F/3vzHU/OKyKxWeOMqJws7GvlPGEisuRmzjERMbopRZC+7hkA1FZVYMzw2jx7ExERUdys7h8BgMumELnFnkQiIiIiKiu375iNV//mzUgjojhiJZEiQ1ksxFyMHfPHYvXkEd6cjIiIiEKjpjKJUU3DSp0MoshiJZEiQ48+Kh7NL9h7+HRPzkNEREREVE44J5Eio6YydblWcH4BEREREZFv2JNIkfHBo2fhs997CdO7mkqdFCIiIiKissVKIkVGR2MN1zQjIiIiIvIZh5sSERERERFRGiuJRERERERElBZIJVFEqkXkLhF5UUTeEJEfi8hGw/bVIvKciPxNRL4pIuNMx94tIq+LyMsicoHp3DmPJSIiIiIiImeC6kmsAPASgOUAmgBcAeBzItItIm0AvgjgSgDDAewDcL/h2GsATAAwDsBKAHtEZAMA2DiWiIiIiIiIHAgkcI1S6i2kKnu6h0XkNwDmAGgF8KxS6gEAEJFrAPxZRPqVUs8BOB7ACUqpVwG8KiJ3AjgBwCMAthQ4loiIiIiIiBwoyZxEEekAMBHAswCmAnhG36ZVKJ8HMFVEWgCMMm7X/j1V+3fOYy3ec5eI7BORfa+88oq3H4iIiIiIiKhMBF5JFJFKAJ8G8Amtt68ewGum3V4D0KBtg2m7vg0Fjs2glLpDKTWolBpsb28v7kMQERERERGVqUAriSKSAPBJAO8A2K29/CaARtOujQDe0LbBtF3fVuhYIoqpvva6UieBiIiIKLICmZMIACIiAO4C0AFgk1Lqn9qmZ5Gad6jvVwegD6m5hq+KyB8BDAD4urbLgHZM3mN9/ChEFGLf2bMSTbWVpU4GERERUWQF2ZP4UQCTARyslPq74fUvAZgmIltFpAbAVQB+Ygg8cy+AK0SkRUT6AZwK4OM2jyWimBkzvBaNNawkEhEREbkV1DqJ4wCcBmAmgJdF5E3tvx1KqVcAbAWwF8CrAOYDONpw+NVIBaN5EcCTAN6nlHoEAGwcS0RERERERA6IUqrUaQjc4OCg2rdvX6mTQUREREREVBIi8gOl1KDVtpIsgUFEREREREThxEoiERERERERpbGSSERERERERGmsJBIREREREVEaK4lERERERESUxkoiERERERERpbGSSERERERERGmsJBIREREREVEaK4lERERERESUxkoiERERERERpbGSSERERERERGmsJBIREREREVEaK4lERERERESUJkqpUqchcCLyCoAXizxNE4DXPEiOURuAP3t8TsD7tPrx2f04b1TSGaVz+nXeOJ8zCvme11I0zunXeaNyTj/O61c6/cj3UfjsUTmnX+eNyjn9OG9U0unXOcP6rJ+klGqw3KKU4n8u/gNwhw/n3BeFtPrx2eOcziidM0ppjdA5Q5/veS1F45xRSmtUPr+P6fQ830fhs0flnFFKa1Q+f1TS6eM5Q/msz5cuDjd17yulToADXqfVr88e13RG6Zx+nTfO5/SLl2nltRSNc/p13qic04/zxjXP+3G+KJ3Tr/NG5Zx+nDcq6fTrnH7xLa2xHG4aViKyTyk1WOp0EFFwmO+J4of5nihewprn86WLPYnhckepE0BEgWO+J4of5nuieAlrns+ZLvYkEhERERERURp7EolCSEQ+LiI3lDodRBQM5nmi+GG+pzBjJZEoQCLyLRE5pdTpIKJgMM8TxQ/zPZUDVhKJiIiIiIgojZVEohIQkRNE5CnTa0pExpcqTUTkH+Z5ovhhvqcoYyXRIyLC75IoZpjvieKH+Z4oXuKa52P5ob0mIkml1IFSp4OIgsN8TxQ/zPdE8RLnPM9KYhFEJAkASqn9ItImIreJyPkiMrXUaSMifzDfE8UP8z1RvDDPs5JYFKXUfgAQkcUAngTQAeAQAO8TkZnaNn7HW3wA6AAADb9JREFUZOUtALX6HyIysoRpIQeY78kl5vkIY74nl5jvI4p5npVER0RETH9Xi8h9AK4G8CGl1FEAdgN4HsAeAIhrFzUV9AyAqSIyU0RqAFxT4vRQDsz35BHm+QhhviePMN9HBPN8NlYSbZCUpFJKGV9XSr0N4NsApgNo0F57FsDXAIwRkSO04/k9k5FSSv0SwHUAHgfwKwBP5T+EgsZ8Tx5ino8I5nvyEPN9BDDP5yam74QMRCRhbCUQkXoAlwN4A8APlFKPai0PDwF4DsAHlVK/F5F2AGcCWAZgs1LqHyVIPoWQiPwQwHVKqQdLnRayxnxPXmKejwbme/IS8334Mc8XVra132KJyAYAe0VkrPb3KQB+DWAygAEAHxKRnVrLw10AFmj/QSn1CoBvAhAAS0qQfAohbbLzZAA/KnVayBrzPXmJeT4amO/JS8z34cc8bw8riblVAFgDYJ6I1AIYBHC2UuowbVzyEwD2AoBS6iEAvwSwQUSmaMd/D8BWpdTjwSedwkZEbgHwGIBLlFIvljo9lBPzPXmCeT5SmO/JE8z3kcE8bwOHm+YhIrcDaARwA4A3lFJ/EJEJAP4dwGikxih/Ril1rojMBvAZpCa43q+PbdYnwprHOhNRODHfE8UP8z1RvDDPF8aeRAuGCEcfBNANYBWAv4hIL4DPAXhaKdUH4A4Au0WkRyn1QwCnKKU+a7xYlCbYT0BETjHfE8UP8z1RvDDP28dKogWllBIR0aJSfQ3AZqTGKfcB+ItS6lJt12qkJrNu1Y77DpAdRpeIwo/5nih+mO+J4oV53j4ONy1Ai3b0JaTGJ/8DwBakLpplAPYBOFMp9VrpUkhEXmO+J4of5nuieGGez489iXlo4XHfBPBJAIsBvIzU2OVKAO9XSu1QSr2mrbGS97sUkSrjef1MNxG552W+185Xbzy3X+kmIvc8ft73ikij9u/Y9DoQRYnHeX6uiEzyP9XBYk+iTSJyP4BXAFytlPp/hteTSqn9eY4bC+BmAO8A+J1S6grfE0tEnnCb77V9xiI15+FdpNZd2qWUetfP9BJR8YrM92cBeB+A45RSn/c3pUTkhSLK+GMA3AOgF8B2pdR3fU9sgNiqXYChFfA2AHORGrMMEUkCQIGL53Skuqv/iFRX9lEicre2jd89UUgVk++1/S4H8EMALwG4DsBaALebzk1EIVJsvtcMAHgVqdD6E/xIJxF5w02e148RkfcCeBbAz5VSvXoFsZye8ayoFKBNcE0opf4TqYUz12uvFyokNgOYAGC3UupCpdS9AI4EsEVEGpVSB/xOOxG54zbfA+kHxAEAG5RS5yilfgrgKQCN2mR5Dt8gCqEi831S++evANwPYD6AJSJS7Vd6iag4bvK84Rm+BsC3lVJnA4CIzNPK/mVTtyqbD+InpdQBSS22+XcAv8i1n/4w0AqJbyO1psoj2msJAM0Afo7UhUhEIWY33wMZeb9Ce4DcqpTaJyJzROQXAA4B8GsAhxnnJxNRuLh43pt7HBYiNfzsYQCHAujxNcFEVBQXeb5Ge2kngFUicoaI/DeAOwH8B4B7DY1GkcZKon2HAfgRgC+aN4hIizaM9GNAet2Uvyul9imlXtd6Dw4gFU73DQBvBplwInItZ74HLPP+u9r/39Z26QTwYaVUHYBbkVqI93IRafA74UTkmpPn/X7tdb089RKAMQDuAlADYLuI3CAiM4JIOBG54iTP/0NrEH4WqVEDtwP4dwBLAFyI1LqL52rHRrpTiIFrbMo1TExEpgP4EIBWAK8D+IBS6otWk11F5KMA/qmUOieQRBNRUfIND3WY90Ub1nIEgA8AmKKUesvv9BORcy6e9wl9ComIfBvAiUqp50XkKwA2AvgqgB1aJEUiChkXeb5KKfWOiFQAWKqU+qbhOX8ygD1KqchHO2VPok155hFVIRU+9wQA3wBwqnbx7DdMbk1oXc9zkGp1gIicIiJn+J9yInKrwPzBgnnfoEL7/xtIBbVo9DqtROQNF8/7A4Zh5N8FcK2I/BSpfP4UgBcA1PmaaCJyzUWef0drEH4XwLe0ffXn/AEAL4hh+auoYiXRIRHpF5HlIjJCe+mnAD6vlPoBgEcBKAC79d21/yukHhb/B2C0iDwBYC9Sw1KIKAJc5n09hPY/RWQyUmswfU0p9ccg005EzjnJ81qhMQFgFICpAP5VKbUcwC0AhgefeiJyyuFzXgHp4DeiPecnATgdwKPlMHKAlUSbRCQpInci1Up4NYAnReRgpdQ7SqnXtN2eQeoiOkxExmmtiwmthWIQwCakxi5/SynVoZR6uBSfhYjsKyLvizb3cLWIPATgvwB8SSn1npJ8ECKyxU2eB1IBMABcCWCxUuou7bWvKaV2KqX+FPwnISI7inzOD0NqyZsHAXwPwINKqVtL8kE8xkqifVMBjEdqDZV1AD4O4IMiskzfQWs1+AaAPwA4X3vtgDbU9K8ArgHQrZS6LtCUE1Ex3OZ9hVSQql8hNSdprFLqxkBTTkRuuMrzmpeUUn/TA9lEPXAFUUwU85z/B4BXtG2jlVI3BZpyH7GSmIeINBkili0AME4p9WcAB5RStyDV4nC8iPQaDvslUktfTBORG0XkaQDLlVLfV0pdVw7dz0TlzsO8v0Yp9Rul1B1KqTcC/RBEZJtHef4/AawG0r2KheY1E1GJePyc/7VS6kPl9pxnJdGCiEwQkUcBfBrAF7ShJP8D4LciMlO/+QO4CcAAgHRoa6XUOwD2I3XBHQ/g35RSTwT6AYjIFR/y/tcD/QBE5IjHef5OpdSjgX4AInKEz3n7WEk00ULXPoHUeil7kJpwfiVSUYv+hFQ3NABAKfUTpCa17tSOTYrIWgCfB/ARpVSXUurjgX4AInKFeZ8oXpjnieKFed4ZrpNoIiI3AHhRKXWn9vdoAM8BmIjUhTIbht5BETkYwM0A5mrzELoAvKWU+mtJPgARucK8TxQvzPNE8cI870xF4V1i52MA3gYAEakG8DcAzwMYBuABpCa1nicizyulXgQwF8BjSqm/AYBS6vclSTURFYt5nyhemOeJ4oV53gFWEk2UUr8DUhHJlFJvi8gUpIblvqStg3QbUmudfVVE/gpgEoAdpUsxEXmBeZ8oXpjnieKFed4ZVhJzMEQkWwHgF9pkVSilfiYiWwHMAjBVKfWJEiWRiHzAvE8UL8zzRPHCPG8PK4k5iEhSKbUfwDwAj2ivnYFUq8JepdQ+APtKmEQi8gHzPlG8MM8TxQvzvD2sJOaglNovIhVIRT4aISLfBtAN4CSl1CslTRwR+YZ5nyhemOeJ4oV53h5GN81DRKYDeAapsLgfUEq9v8RJIqIAMO8TxQvzPFG8MM8XxkpiHiJSBWA3Uuuh/KPU6SGiYDDvE8UL8zxRvDDPF8ZKIhEREREREaUlSp0AIiIiIiIiCg9WEomIiIiIiCiNlUQiIiIiIiJKYyWRiIiIiIiI0lhJJCIiIiIiojRWEomIiACIyFgReVNEkqVOCxERUSmxkkhERLElIi+IyBoAUEr9VilVr5TaH+D7rxCR3wX1fkRERHawkkhERERERERprCQSEVEsicgnAYwF8BVtmOkeEVEiUqFt/5aI3CAi/6Vt/4qItIrIp0XkdRH5voh0G87XLyJfF5G/iMgvRGSbYdsmEfkfEXlDRH4vIheJSB2ArwHo1M7/poh0isg8EXlaRP4qIn8UkQ+LSJXhXEpEzhSRX2nnu15E+rR0vi4in9P313sqReQyEfmz1nO6I5hvmIiIooqVRCIiiiWl1E4AvwVwsFKqHsDnLHY7GsBOAF0A+gA8DeAeAMMB/BzA1QCgVfi+DuA+ACO04z4iIlO089wF4DSlVAOAaQCeUEq9BWAjgD9ow1zrlVJ/ALAfwPkA2gAsBLAawJmmdK0HMAfAAgB7ANwB4FgAY7TzbzfsO1I7VxeA4wHcISKTHH1ZREQUK6wkEhER5XaPUup5pdRrSPX6Pa+Uelwp9S6ABwDM0vY7CMALSql7lFLvKqV+BOALAI7Utv8TwBQRaVRKvaqU+mGuN1RK/UAp9d/aeV4A8G8Alpt2e69S6nWl1LMAfgbgMaXUrw3pnGXa/0ql1NtKqScBfBXANhAREeXASiIREVFufzL8++8Wf9dr/x4HYL42RPSvIvJXADuQ6sUDgK0ANgF4UUSeFJGFud5QRCaKyMMi8rKIvA7gRqR6At2kCwBe1XotdS8C6Mz1/kRERKwkEhFRnCmPzvMSgCeVUs2G/+qVUmcAgFLq+0qpQ5Eaivoghoa2Wr3/RwE8B2CCUqoRwGUApIi0tWjDYXVjAfyhiPMREVGZYyWRiIji7E8Aej04z8MAJorIThGp1P6bKyKTRaRKRHaISJNS6p8AXgdwwPD+rSLSZDhXg7bPmyLSD+AMD9J3rZaOpUgNjX3Ag3MSEVGZYiWRiIji7CYAV2jDQ49wexKl1BsA1iEVsOYPAF4GcAuAam2XnQBe0IaPno7UUFQopZ4D8BkAv9aGqXYCuAjAMQDeAHAngPvdpkvzMoBXtXR9GsDp2vsSERFZEqW8GmlDREREYSIiKwB8Sik1utRpISKi6GBPIhEREREREaWxkkhERERERERpHG5KREREREREaexJJCIiIiIiojRWEomIiIiIiCiNlUQiIiIiIiJKYyWRiIiIiIiI0lhJJCIiIiIiojRWEomIiIiIiCjt/wNPd0THgOVChAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -172,17 +284,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -205,9 +319,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "dftf2" }, "language_info": { "codemirror_mode": { @@ -219,7 +333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/1_CNN_dilated.ipynb b/1_CNN_dilated.ipynb index 0102e08..87e512d 100644 --- a/1_CNN_dilated.ipynb +++ b/1_CNN_dilated.ipynb @@ -24,29 +24,28 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "import warnings\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", "import datetime as dt\n", - "from collections import UserDict\n", - "from glob import glob\n", - "from IPython.display import Image\n", + "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", - "from common.utils import load_data, mape\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", "\n", + "import pandas as pd\n", "pd.options.display.float_format = '{:,.2f}'.format\n", - "np.set_printoptions(precision=2)\n", - "warnings.filterwarnings(\"ignore\")" + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import tensorflow as tf\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Load data into Pandas dataframe" + "Load the data from csv into a Pandas dataframe. Make sure to first complete the [0_data_setup](0_data_setup.ipynb) notebook." ] }, { @@ -58,6 +57,19 @@ "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -105,7 +117,10 @@ } ], "source": [ - "energy = load_data('data/')[['load']]\n", + "import os\n", + "\n", + "file_name = os.path.join('data', 'energy.parquet')\n", + "energy = pd.read_parquet(file_name)[['load']]\n", "energy.head()" ] }, @@ -117,7 +132,10 @@ "\n", "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", "\n", - "We will allocate the period 1st November 2014 to 31st December 2014 to the test set. The period 1st September 2014 to 31st October is allocated to validation set. All other time periods are available for the training set." + "We will allocate data as follows:\n", + "* November 1, 2014 to December 31, 2014: **test** set. \n", + "* September 1, 2014 to October 31, 2014: **validation** set. \n", + "* Everything up to August 31, 2014: **training** set." ] }, { @@ -137,9 +155,9 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, "metadata": { @@ -149,10 +167,12 @@ } ], "source": [ - "energy[energy.index < valid_start_dt][['load']].rename(columns={'load':'train'}) \\\n", - " .join(energy[(energy.index >=valid_start_dt) & (energy.index < test_start_dt)][['load']] \\\n", + "energy[:valid_start_dt][['load']] \\\n", + " .rename(columns={'load':'train'}) \\\n", + " .join(energy[valid_start_dt:test_start_dt][['load']] \\\n", " .rename(columns={'load':'validation'}), how='outer') \\\n", - " .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n", + " .join(energy[test_start_dt:][['load']] \\\n", + " .rename(columns={'load':'test'}), how='outer') \\\n", " .plot(y=['train', 'validation', 'test'], figsize=(15, 8), fontsize=12)\n", "plt.xlabel('timestamp', fontsize=12)\n", "plt.ylabel('load', fontsize=12)\n", @@ -167,34 +187,15 @@ "\n", "For this example, we will set *T=10*. This means that the input for each sample is a vector of the prevous 10 hours of the energy load. The choice of *T=10* was arbitrary but should be selected through experimentation.\n", "\n", - "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)" + "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)\n", + "\n", + "![One step forecast](./images/one_step_forecast_T10.png)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('./images/one_step_forecast_T10.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, "outputs": [], "source": [ "T = 10\n", @@ -208,8 +209,8 @@ "Our data preparation for the training set will involve the following steps:\n", "\n", "1. Filter the original dataset to include only that time period reserved for the training set\n", - "2. Scale the time series such that the values fall within the interval (0, 1)\n", - "3. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n", + "2. Scale the time series such that the values are centered with unit variance\n", + "3. Shift the values of the time series to create a Pandas dataframe containing rows with all the data for a single training example\n", "4. Discard any samples with missing values\n", "5. Transform this Pandas dataframe into a numpy array of shape (samples, features) for input into Keras" ] @@ -224,30 +225,44 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "train = energy.copy()[energy.index < valid_start_dt][['load']]" + "train = energy.copy()[:valid_start_dt][['load']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Scale the time series such that the values fall within the interval (0, 1)\n", - "Scale data to be in range (0, 1). This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data." + "### 2. Scale the time series such that the values are *normal*\n", + "\n", + "Scale data to be in a reasonable range for numeric stability. For this set we *normalize*, i.e. scale for the mean to be 0, standard deviation of 1. This transformation should be calibrated on the training set only to prevent information from the validation or test sets leaking into the training data (which would give optimistic performance estimates)." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -258,43 +273,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
2012-01-01 00:00:000.22-1.07
2012-01-01 01:00:000.18-1.32
2012-01-01 02:00:000.14-1.52
2012-01-01 03:00:000.13-1.59
2012-01-01 04:00:000.13-1.59
2012-01-01 05:00:000.15-1.50
2012-01-01 06:00:000.18-1.31
2012-01-01 07:00:000.23-1.03
2012-01-01 08:00:000.29-0.69
2012-01-01 09:00:000.35-0.36
\n", @@ -302,26 +317,27 @@ ], "text/plain": [ " load\n", - "2012-01-01 00:00:00 0.22\n", - "2012-01-01 01:00:00 0.18\n", - "2012-01-01 02:00:00 0.14\n", - "2012-01-01 03:00:00 0.13\n", - "2012-01-01 04:00:00 0.13\n", - "2012-01-01 05:00:00 0.15\n", - "2012-01-01 06:00:00 0.18\n", - "2012-01-01 07:00:00 0.23\n", - "2012-01-01 08:00:00 0.29\n", - "2012-01-01 09:00:00 0.35" + "2012-01-01 00:00:00 -1.07\n", + "2012-01-01 01:00:00 -1.32\n", + "2012-01-01 02:00:00 -1.52\n", + "2012-01-01 03:00:00 -1.59\n", + "2012-01-01 04:00:00 -1.59\n", + "2012-01-01 05:00:00 -1.50\n", + "2012-01-01 06:00:00 -1.31\n", + "2012-01-01 07:00:00 -1.03\n", + "2012-01-01 08:00:00 -0.69\n", + "2012-01-01 09:00:00 -0.36" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from sklearn.preprocessing import MinMaxScaler\n", - "scaler = MinMaxScaler()\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "scaler = StandardScaler()\n", "train['load'] = scaler.fit_transform(train)\n", "train.head(10)" ] @@ -335,14 +351,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, "metadata": { @@ -352,9 +368,9 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, "metadata": { @@ -364,8 +380,12 @@ } ], "source": [ - "energy[energy.index < valid_start_dt][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n", - "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n", + "energy[energy.index < valid_start_dt][['load']]\\\n", + " .rename(columns={'load':'original load'})\\\n", + " .plot.hist(bins=100, fontsize=12)\n", + "train\\\n", + " .rename(columns={'load':'scaled load'})\\\n", + " .plot.hist(bins=100, fontsize=12)\n", "plt.show()" ] }, @@ -373,19 +393,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n", + "### 3. Create a Pandas dataframe containing rows with all the data for a single training example\n", "First, we create the target (*y_t+1*) variable. If we use the convention that the dataframe is indexed on time *t*, we need to shift the *load* variable forward one hour in time. Using the freq parameter we can tell Pandas that the frequency of the time series is hourly. This ensures the shift does not jump over any missing periods in the time series." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -397,53 +430,53 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
2012-01-01 00:00:000.220.18-1.07-1.32
2012-01-01 01:00:000.180.14-1.32-1.52
2012-01-01 02:00:000.140.13-1.52-1.59
2012-01-01 03:00:000.130.13-1.59-1.59
2012-01-01 04:00:000.130.15-1.59-1.50
2012-01-01 05:00:000.150.18-1.50-1.31
2012-01-01 06:00:000.180.23-1.31-1.03
2012-01-01 07:00:000.230.29-1.03-0.69
2012-01-01 08:00:000.290.35-0.69-0.36
2012-01-01 09:00:000.350.37-0.36-0.24
\n", @@ -451,19 +484,19 @@ ], "text/plain": [ " load y_t+1\n", - "2012-01-01 00:00:00 0.22 0.18\n", - "2012-01-01 01:00:00 0.18 0.14\n", - "2012-01-01 02:00:00 0.14 0.13\n", - "2012-01-01 03:00:00 0.13 0.13\n", - "2012-01-01 04:00:00 0.13 0.15\n", - "2012-01-01 05:00:00 0.15 0.18\n", - "2012-01-01 06:00:00 0.18 0.23\n", - "2012-01-01 07:00:00 0.23 0.29\n", - "2012-01-01 08:00:00 0.29 0.35\n", - "2012-01-01 09:00:00 0.35 0.37" + "2012-01-01 00:00:00 -1.07 -1.32\n", + "2012-01-01 01:00:00 -1.32 -1.52\n", + "2012-01-01 02:00:00 -1.52 -1.59\n", + "2012-01-01 03:00:00 -1.59 -1.59\n", + "2012-01-01 04:00:00 -1.59 -1.50\n", + "2012-01-01 05:00:00 -1.50 -1.31\n", + "2012-01-01 06:00:00 -1.31 -1.03\n", + "2012-01-01 07:00:00 -1.03 -0.69\n", + "2012-01-01 08:00:00 -0.69 -0.36\n", + "2012-01-01 09:00:00 -0.36 -0.24" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -478,18 +511,31 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We also need to shift the load variable back 6 times to create the input sequence:" + "We also need to shift the load variable back *T* times to create the input sequence:" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -511,8 +557,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -522,12 +568,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -536,13 +582,13 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -550,114 +596,114 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
2012-01-01 00:00:000.220.18-1.07-1.32nannannannannannan0.22-1.07
2012-01-01 01:00:000.180.14-1.32-1.52nannannannannannan0.220.18-1.07-1.32
2012-01-01 02:00:000.140.13-1.52-1.59nannannannannannan0.220.180.14-1.07-1.32-1.52
2012-01-01 03:00:000.130.13-1.59-1.59nannannannannannan0.220.180.140.13-1.07-1.32-1.52-1.59
2012-01-01 04:00:000.130.15-1.59-1.50nannannannannan0.220.180.140.130.13-1.07-1.32-1.52-1.59-1.59
2012-01-01 05:00:000.150.18-1.50-1.31nannannannan0.220.180.140.130.130.15-1.07-1.32-1.52-1.59-1.59-1.50
2012-01-01 06:00:000.180.23-1.31-1.03nannannan0.220.180.140.130.130.150.18-1.07-1.32-1.52-1.59-1.59-1.50-1.31
2012-01-01 07:00:000.230.29-1.03-0.69nannan0.220.180.140.130.130.150.180.23-1.07-1.32-1.52-1.59-1.59-1.50-1.31-1.03
2012-01-01 08:00:000.290.35-0.69-0.36nan0.220.180.140.130.130.150.180.230.29-1.07-1.32-1.52-1.59-1.59-1.50-1.31-1.03-0.69
2012-01-01 09:00:000.350.370.220.180.140.130.130.150.180.230.290.35-0.36-0.24-1.07-1.32-1.52-1.59-1.59-1.50-1.31-1.03-0.69-0.36
\n", @@ -665,50 +711,50 @@ ], "text/plain": [ " load_original y_t+1 load_t-9 load_t-8 load_t-7 \\\n", - "2012-01-01 00:00:00 0.22 0.18 nan nan nan \n", - "2012-01-01 01:00:00 0.18 0.14 nan nan nan \n", - "2012-01-01 02:00:00 0.14 0.13 nan nan nan \n", - "2012-01-01 03:00:00 0.13 0.13 nan nan nan \n", - "2012-01-01 04:00:00 0.13 0.15 nan nan nan \n", - "2012-01-01 05:00:00 0.15 0.18 nan nan nan \n", - "2012-01-01 06:00:00 0.18 0.23 nan nan nan \n", - "2012-01-01 07:00:00 0.23 0.29 nan nan 0.22 \n", - "2012-01-01 08:00:00 0.29 0.35 nan 0.22 0.18 \n", - "2012-01-01 09:00:00 0.35 0.37 0.22 0.18 0.14 \n", + "2012-01-01 00:00:00 -1.07 -1.32 nan nan nan \n", + "2012-01-01 01:00:00 -1.32 -1.52 nan nan nan \n", + "2012-01-01 02:00:00 -1.52 -1.59 nan nan nan \n", + "2012-01-01 03:00:00 -1.59 -1.59 nan nan nan \n", + "2012-01-01 04:00:00 -1.59 -1.50 nan nan nan \n", + "2012-01-01 05:00:00 -1.50 -1.31 nan nan nan \n", + "2012-01-01 06:00:00 -1.31 -1.03 nan nan nan \n", + "2012-01-01 07:00:00 -1.03 -0.69 nan nan -1.07 \n", + "2012-01-01 08:00:00 -0.69 -0.36 nan -1.07 -1.32 \n", + "2012-01-01 09:00:00 -0.36 -0.24 -1.07 -1.32 -1.52 \n", "\n", " load_t-6 load_t-5 load_t-4 load_t-3 load_t-2 \\\n", "2012-01-01 00:00:00 nan nan nan nan nan \n", "2012-01-01 01:00:00 nan nan nan nan nan \n", - "2012-01-01 02:00:00 nan nan nan nan 0.22 \n", - "2012-01-01 03:00:00 nan nan nan 0.22 0.18 \n", - "2012-01-01 04:00:00 nan nan 0.22 0.18 0.14 \n", - "2012-01-01 05:00:00 nan 0.22 0.18 0.14 0.13 \n", - "2012-01-01 06:00:00 0.22 0.18 0.14 0.13 0.13 \n", - "2012-01-01 07:00:00 0.18 0.14 0.13 0.13 0.15 \n", - "2012-01-01 08:00:00 0.14 0.13 0.13 0.15 0.18 \n", - "2012-01-01 09:00:00 0.13 0.13 0.15 0.18 0.23 \n", + "2012-01-01 02:00:00 nan nan nan nan -1.07 \n", + "2012-01-01 03:00:00 nan nan nan -1.07 -1.32 \n", + "2012-01-01 04:00:00 nan nan -1.07 -1.32 -1.52 \n", + "2012-01-01 05:00:00 nan -1.07 -1.32 -1.52 -1.59 \n", + "2012-01-01 06:00:00 -1.07 -1.32 -1.52 -1.59 -1.59 \n", + "2012-01-01 07:00:00 -1.32 -1.52 -1.59 -1.59 -1.50 \n", + "2012-01-01 08:00:00 -1.52 -1.59 -1.59 -1.50 -1.31 \n", + "2012-01-01 09:00:00 -1.59 -1.59 -1.50 -1.31 -1.03 \n", "\n", " load_t-1 load_t-0 \n", - "2012-01-01 00:00:00 nan 0.22 \n", - "2012-01-01 01:00:00 0.22 0.18 \n", - "2012-01-01 02:00:00 0.18 0.14 \n", - "2012-01-01 03:00:00 0.14 0.13 \n", - "2012-01-01 04:00:00 0.13 0.13 \n", - "2012-01-01 05:00:00 0.13 0.15 \n", - "2012-01-01 06:00:00 0.15 0.18 \n", - "2012-01-01 07:00:00 0.18 0.23 \n", - "2012-01-01 08:00:00 0.23 0.29 \n", - "2012-01-01 09:00:00 0.29 0.35 " + "2012-01-01 00:00:00 nan -1.07 \n", + "2012-01-01 01:00:00 -1.07 -1.32 \n", + "2012-01-01 02:00:00 -1.32 -1.52 \n", + "2012-01-01 03:00:00 -1.52 -1.59 \n", + "2012-01-01 04:00:00 -1.59 -1.59 \n", + "2012-01-01 05:00:00 -1.59 -1.50 \n", + "2012-01-01 06:00:00 -1.50 -1.31 \n", + "2012-01-01 07:00:00 -1.31 -1.03 \n", + "2012-01-01 08:00:00 -1.03 -0.69 \n", + "2012-01-01 09:00:00 -0.69 -0.36 " ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for t in range(1, T+1):\n", - " train_shifted['load_t-'+str(T-t)] = train_shifted['load'].shift(T-t, freq='H')\n", + " train_shifted[f'load_t-{T-t}'] = train_shifted['load'].shift(T-t, freq='H')\n", "train_shifted = train_shifted.rename(columns={'load':'load_original'})\n", "train_shifted.head(10)" ] @@ -718,18 +764,31 @@ "metadata": {}, "source": [ "### 4. Discard any samples with missing values\n", - "Notice how we have missing values for the input sequences for the first 5 samples. We will discard these:" + "Notice how we have missing values for the input sequences for the first *T* samples. We will discard these:" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -751,78 +810,78 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
2012-01-01 09:00:000.350.370.220.180.140.130.130.150.180.230.290.35-0.36-0.24-1.07-1.32-1.52-1.59-1.59-1.50-1.31-1.03-0.69-0.36
2012-01-01 10:00:000.370.370.180.140.130.130.150.180.230.290.350.37-0.24-0.23-1.32-1.52-1.59-1.59-1.50-1.31-1.03-0.69-0.36-0.24
2012-01-01 11:00:000.370.370.140.130.130.150.180.230.290.350.370.37-0.23-0.22-1.52-1.59-1.59-1.50-1.31-1.03-0.69-0.36-0.24-0.23
2012-01-01 12:00:000.370.360.130.130.150.180.230.290.350.370.370.37-0.22-0.28-1.59-1.59-1.50-1.31-1.03-0.69-0.36-0.24-0.23-0.22
2012-01-01 13:00:000.360.350.130.150.180.230.290.350.370.370.370.36-0.28-0.33-1.59-1.50-1.31-1.03-0.69-0.36-0.24-0.23-0.22-0.28
\n", @@ -830,28 +889,28 @@ ], "text/plain": [ " load_original y_t+1 load_t-9 load_t-8 load_t-7 \\\n", - "2012-01-01 09:00:00 0.35 0.37 0.22 0.18 0.14 \n", - "2012-01-01 10:00:00 0.37 0.37 0.18 0.14 0.13 \n", - "2012-01-01 11:00:00 0.37 0.37 0.14 0.13 0.13 \n", - "2012-01-01 12:00:00 0.37 0.36 0.13 0.13 0.15 \n", - "2012-01-01 13:00:00 0.36 0.35 0.13 0.15 0.18 \n", + "2012-01-01 09:00:00 -0.36 -0.24 -1.07 -1.32 -1.52 \n", + "2012-01-01 10:00:00 -0.24 -0.23 -1.32 -1.52 -1.59 \n", + "2012-01-01 11:00:00 -0.23 -0.22 -1.52 -1.59 -1.59 \n", + "2012-01-01 12:00:00 -0.22 -0.28 -1.59 -1.59 -1.50 \n", + "2012-01-01 13:00:00 -0.28 -0.33 -1.59 -1.50 -1.31 \n", "\n", " load_t-6 load_t-5 load_t-4 load_t-3 load_t-2 \\\n", - "2012-01-01 09:00:00 0.13 0.13 0.15 0.18 0.23 \n", - "2012-01-01 10:00:00 0.13 0.15 0.18 0.23 0.29 \n", - "2012-01-01 11:00:00 0.15 0.18 0.23 0.29 0.35 \n", - "2012-01-01 12:00:00 0.18 0.23 0.29 0.35 0.37 \n", - "2012-01-01 13:00:00 0.23 0.29 0.35 0.37 0.37 \n", + "2012-01-01 09:00:00 -1.59 -1.59 -1.50 -1.31 -1.03 \n", + "2012-01-01 10:00:00 -1.59 -1.50 -1.31 -1.03 -0.69 \n", + "2012-01-01 11:00:00 -1.50 -1.31 -1.03 -0.69 -0.36 \n", + "2012-01-01 12:00:00 -1.31 -1.03 -0.69 -0.36 -0.24 \n", + "2012-01-01 13:00:00 -1.03 -0.69 -0.36 -0.24 -0.23 \n", "\n", " load_t-1 load_t-0 \n", - "2012-01-01 09:00:00 0.29 0.35 \n", - "2012-01-01 10:00:00 0.35 0.37 \n", - "2012-01-01 11:00:00 0.37 0.37 \n", - "2012-01-01 12:00:00 0.37 0.37 \n", - "2012-01-01 13:00:00 0.37 0.36 " + "2012-01-01 09:00:00 -0.69 -0.36 \n", + "2012-01-01 10:00:00 -0.36 -0.24 \n", + "2012-01-01 11:00:00 -0.24 -0.23 \n", + "2012-01-01 12:00:00 -0.23 -0.22 \n", + "2012-01-01 13:00:00 -0.22 -0.28 " ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -871,7 +930,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -887,16 +946,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(23366, 1)" + "(23367, 1)" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -914,18 +973,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.37],\n", - " [0.37],\n", - " [0.37]])" + "array([[-0.24],\n", + " [-0.23],\n", + " [-0.22]])" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -943,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -960,16 +1019,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(23366, 10, 1)" + "(23367, 10, 1)" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -987,47 +1046,47 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[[0.22],\n", - " [0.18],\n", - " [0.14],\n", - " [0.13],\n", - " [0.13],\n", - " [0.15],\n", - " [0.18],\n", - " [0.23],\n", - " [0.29],\n", - " [0.35]],\n", + "array([[[-1.07],\n", + " [-1.32],\n", + " [-1.52],\n", + " [-1.59],\n", + " [-1.59],\n", + " [-1.5 ],\n", + " [-1.31],\n", + " [-1.03],\n", + " [-0.69],\n", + " [-0.36]],\n", "\n", - " [[0.18],\n", - " [0.14],\n", - " [0.13],\n", - " [0.13],\n", - " [0.15],\n", - " [0.18],\n", - " [0.23],\n", - " [0.29],\n", - " [0.35],\n", - " [0.37]],\n", + " [[-1.32],\n", + " [-1.52],\n", + " [-1.59],\n", + " [-1.59],\n", + " [-1.5 ],\n", + " [-1.31],\n", + " [-1.03],\n", + " [-0.69],\n", + " [-0.36],\n", + " [-0.24]],\n", "\n", - " [[0.14],\n", - " [0.13],\n", - " [0.13],\n", - " [0.15],\n", - " [0.18],\n", - " [0.23],\n", - " [0.29],\n", - " [0.35],\n", - " [0.37],\n", - " [0.37]]])" + " [[-1.52],\n", + " [-1.59],\n", + " [-1.59],\n", + " [-1.5 ],\n", + " [-1.31],\n", + " [-1.03],\n", + " [-0.69],\n", + " [-0.36],\n", + " [-0.24],\n", + " [-0.23]]])" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1036,15 +1095,35 @@ "X_train[:3]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can sanity check this against the first 3 records of the original dataframe:" + ] + }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -1066,48 +1145,48 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
2012-01-01 09:00:000.350.370.220.180.140.130.130.150.180.230.290.35-0.36-0.24-1.07-1.32-1.52-1.59-1.59-1.50-1.31-1.03-0.69-0.36
2012-01-01 10:00:000.370.370.180.140.130.130.150.180.230.290.350.37-0.24-0.23-1.32-1.52-1.59-1.59-1.50-1.31-1.03-0.69-0.36-0.24
2012-01-01 11:00:000.370.370.140.130.130.150.180.230.290.350.370.37-0.23-0.22-1.52-1.59-1.59-1.50-1.31-1.03-0.69-0.36-0.24-0.23
\n", @@ -1115,22 +1194,22 @@ ], "text/plain": [ " load_original y_t+1 load_t-9 load_t-8 load_t-7 \\\n", - "2012-01-01 09:00:00 0.35 0.37 0.22 0.18 0.14 \n", - "2012-01-01 10:00:00 0.37 0.37 0.18 0.14 0.13 \n", - "2012-01-01 11:00:00 0.37 0.37 0.14 0.13 0.13 \n", + "2012-01-01 09:00:00 -0.36 -0.24 -1.07 -1.32 -1.52 \n", + "2012-01-01 10:00:00 -0.24 -0.23 -1.32 -1.52 -1.59 \n", + "2012-01-01 11:00:00 -0.23 -0.22 -1.52 -1.59 -1.59 \n", "\n", " load_t-6 load_t-5 load_t-4 load_t-3 load_t-2 \\\n", - "2012-01-01 09:00:00 0.13 0.13 0.15 0.18 0.23 \n", - "2012-01-01 10:00:00 0.13 0.15 0.18 0.23 0.29 \n", - "2012-01-01 11:00:00 0.15 0.18 0.23 0.29 0.35 \n", + "2012-01-01 09:00:00 -1.59 -1.59 -1.50 -1.31 -1.03 \n", + "2012-01-01 10:00:00 -1.59 -1.50 -1.31 -1.03 -0.69 \n", + "2012-01-01 11:00:00 -1.50 -1.31 -1.03 -0.69 -0.36 \n", "\n", " load_t-1 load_t-0 \n", - "2012-01-01 09:00:00 0.29 0.35 \n", - "2012-01-01 10:00:00 0.35 0.37 \n", - "2012-01-01 11:00:00 0.37 0.37 " + "2012-01-01 09:00:00 -0.69 -0.36 \n", + "2012-01-01 10:00:00 -0.36 -0.24 \n", + "2012-01-01 11:00:00 -0.24 -0.23 " ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1149,13 +1228,26 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -1197,14 +1289,14 @@ "2014-08-31 19:00:00 3,969.00" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", - "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load']]\n", + "valid = energy.copy()[look_back_dt:test_start_dt][['load']]\n", "valid.head()" ] }, @@ -1217,13 +1309,26 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -1234,23 +1339,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
2014-08-31 15:00:000.570.88
2014-08-31 16:00:000.580.96
2014-08-31 17:00:000.601.10
2014-08-31 18:00:000.611.14
2014-08-31 19:00:000.611.16
\n", @@ -1258,14 +1363,14 @@ ], "text/plain": [ " load\n", - "2014-08-31 15:00:00 0.57\n", - "2014-08-31 16:00:00 0.58\n", - "2014-08-31 17:00:00 0.60\n", - "2014-08-31 18:00:00 0.61\n", - "2014-08-31 19:00:00 0.61" + "2014-08-31 15:00:00 0.88\n", + "2014-08-31 16:00:00 0.96\n", + "2014-08-31 17:00:00 1.10\n", + "2014-08-31 18:00:00 1.14\n", + "2014-08-31 19:00:00 1.16" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1284,7 +1389,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1300,16 +1405,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(1463,)" + "(1464,)" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1320,16 +1425,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(1463, 10, 1)" + "(1464, 10, 1)" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1343,33 +1448,14 @@ "metadata": {}, "source": [ "## Implement the Convolutional Neural Network\n", - "We implement the convolutional neural network with 3 layers, 5 neurons in each layer, a kernel size of 3 in each layer, and dilation rates of 1, 2 and 4 for each successive layer." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('./images/cnn_dilated.png')" + "We implement the convolutional neural network with 3 layers, 5 neurons in each layer, a kernel size of 3 in each layer, and dilation rates of 1, 2 and 4 for each successive layer.\n", + "\n", + "![Dilated CNN](./images/cnn_dilated.png)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1388,31 +1474,21 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "LATENT_DIM = 5\n", "KERNEL_SIZE = 2\n", "BATCH_SIZE = 32\n", - "EPOCHS = 10" + "EPOCHS = 15" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /data/anaconda/envs/dnntutorial/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n" - ] - } - ], + "outputs": [], "source": [ "model = Sequential()\n", "model.add(Conv1D(LATENT_DIM, kernel_size=KERNEL_SIZE, padding='causal', strides=1, activation='relu', dilation_rate=1, input_shape=(T, 1)))\n", @@ -1424,13 +1500,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Model: \"sequential_1\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", @@ -1464,7 +1541,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1478,37 +1555,18 @@ "#### Early stopping trick" ] }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('./images/early_stopping.png')" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Specify the early stopping criteria. We **monitor** the validation loss (in this case the mean squared error) on the validation set after each training epoch. If the validation loss has not improved by **min_delta** after **patience** epochs, we stop the training." + "Specify the early stopping criteria. We **monitor** the validation loss (in this case the mean squared error) on the validation set after each training epoch. If the validation loss has not improved by **min_delta** after **patience** epochs, we stop the training.\n", + "\n", + "![Early Stopping](./images/early_stopping.png)" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1517,7 +1575,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1526,37 +1584,44 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From /data/anaconda/envs/dnntutorial/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.cast instead.\n", - "Train on 23366 samples, validate on 1463 samples\n", - "Epoch 1/10\n", - "23366/23366 [==============================] - 1s 54us/step - loss: 0.0101 - val_loss: 0.0030\n", - "Epoch 2/10\n", - "23366/23366 [==============================] - 1s 42us/step - loss: 0.0019 - val_loss: 0.0011\n", - "Epoch 3/10\n", - "23366/23366 [==============================] - 1s 41us/step - loss: 9.6631e-04 - val_loss: 6.5879e-04\n", - "Epoch 4/10\n", - "23366/23366 [==============================] - 1s 42us/step - loss: 7.3052e-04 - val_loss: 6.1729e-04\n", - "Epoch 5/10\n", - "23366/23366 [==============================] - 1s 42us/step - loss: 6.4154e-04 - val_loss: 4.3739e-04\n", - "Epoch 6/10\n", - "23366/23366 [==============================] - 1s 41us/step - loss: 5.6116e-04 - val_loss: 4.0309e-04\n", - "Epoch 7/10\n", - "23366/23366 [==============================] - 1s 41us/step - loss: 5.2003e-04 - val_loss: 3.5128e-04\n", - "Epoch 8/10\n", - "23366/23366 [==============================] - 1s 41us/step - loss: 4.8347e-04 - val_loss: 4.6161e-04\n", - "Epoch 9/10\n", - "23366/23366 [==============================] - 1s 42us/step - loss: 4.5197e-04 - val_loss: 5.7465e-04\n", - "Epoch 10/10\n", - "23366/23366 [==============================] - 1s 41us/step - loss: 4.3400e-04 - val_loss: 3.0176e-04\n" + "Train on 23367 samples, validate on 1464 samples\n", + "Epoch 1/15\n", + "23367/23367 [==============================] - 1s 53us/step - loss: 0.2969 - val_loss: 0.0436\n", + "Epoch 2/15\n", + "23367/23367 [==============================] - 1s 41us/step - loss: 0.0278 - val_loss: 0.0184\n", + "Epoch 3/15\n", + "23367/23367 [==============================] - 1s 39us/step - loss: 0.0180 - val_loss: 0.0137\n", + "Epoch 4/15\n", + "23367/23367 [==============================] - 1s 39us/step - loss: 0.0151 - val_loss: 0.0115\n", + "Epoch 5/15\n", + "23367/23367 [==============================] - 1s 41us/step - loss: 0.0132 - val_loss: 0.0102\n", + "Epoch 6/15\n", + "23367/23367 [==============================] - 1s 41us/step - loss: 0.0121 - val_loss: 0.0091\n", + "Epoch 7/15\n", + "23367/23367 [==============================] - 1s 40us/step - loss: 0.0113 - val_loss: 0.0081\n", + "Epoch 8/15\n", + "23367/23367 [==============================] - 1s 39us/step - loss: 0.0111 - val_loss: 0.0103\n", + "Epoch 9/15\n", + "23367/23367 [==============================] - 1s 39us/step - loss: 0.0108 - val_loss: 0.0079\n", + "Epoch 10/15\n", + "23367/23367 [==============================] - 1s 41us/step - loss: 0.0107 - val_loss: 0.0078\n", + "Epoch 11/15\n", + "23367/23367 [==============================] - 1s 39us/step - loss: 0.0104 - val_loss: 0.0081\n", + "Epoch 12/15\n", + "23367/23367 [==============================] - 1s 38us/step - loss: 0.0106 - val_loss: 0.0092\n", + "Epoch 13/15\n", + "23367/23367 [==============================] - 1s 40us/step - loss: 0.0103 - val_loss: 0.0080\n", + "Epoch 14/15\n", + "23367/23367 [==============================] - 1s 39us/step - loss: 0.0102 - val_loss: 0.0082\n", + "Epoch 15/15\n", + "23367/23367 [==============================] - 1s 39us/step - loss: 0.0102 - val_loss: 0.0080\n" ] } ], @@ -1570,23 +1635,6 @@ " verbose=1)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the model with the smallest mape" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "best_epoch = np.argmin(np.array(history.history['val_loss']))+1\n", - "model.load_weights(\"model_{:02d}.h5\".format(best_epoch))" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1596,14 +1644,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAJWCAYAAAA+z3HMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzs3Xd41eX9//HXfTJJIOscCJuQE0YYIjKTqLjrQusAraPaVlHBtnZQR/Xb6re71l9ra/WrrVqte+Ce4CYgQ0X2SFiRlYQMSAhZ9++PA4gaQhKS3Gc8H9eVK/HkjFe4el19XZ/7vt8fY60VAAAAQpvHdQAAAAAcOUodAABAGKDUAQAAhAFKHQAAQBig1AEAAIQBSh0AAEAYoNQBAACEAUodAABAGKDUAQAAhIFo1wE6m8/nsxkZGa5jAAAAHNbixYtLrLXdW/LciCt1GRkZWrRokesYAAAAh2WM2djS57L8CgAAEAYodQAAAGGAUgcAABAGIm5PHQAAaD91dXUqKipSTU2N6yghLT4+Xn379lVMTEyb34NSBwAA2qyoqEjdunVTRkaGjDGu44Qka61KS0tVVFSkgQMHtvl9WH4FAABtVlNTI6/XS6E7AsYYeb3eI77aSakDAABHhEJ35Nrj35BSBwAAEAYodQAAAGGAUgcAAEJWeXm5/vnPf7b6dWeeeabKy8tb/borr7xSzz77bKtf1xkodQAAIGQdqtTV19c3+7rXXntNKSkpHRXLCUaaAACAdnH7y8u1Yktlu77nsN5J+tXk4Yf8/U033aSCggIdffTRiomJUXx8vFJTU7Vq1SqtWbNG3/72t7V582bV1NToxz/+saZNmybpy3vB7969W2eccYaOPfZY5efnq0+fPnrxxRfVpUuXw2abM2eOfv7zn6u+vl7jxo3Tvffeq7i4ON1000166aWXFB0drdNOO0133nmnnnnmGd1+++2KiopScnKyPvjgg3b7N9qPUgcAAELWH/7wBy1btkyfffaZ3nvvPZ111llatmzZgXlvDz74oNLS0rRnzx6NGzdOF1xwgbxe71feY+3atXriiSf0wAMPaOrUqXruued02WWXNfu5NTU1uvLKKzVnzhwNHjxY3/3ud3Xvvffq8ssv16xZs7Rq1SoZYw4s8d5xxx1688031adPnzYt+7YEpQ4AALSL5q6odZbx48d/ZYDv3XffrVmzZkmSNm/erLVr136j1A0cOFBHH320JGnMmDHasGHDYT9n9erVGjhwoAYPHixJuuKKK3TPPffo+uuvV3x8vH7wgx/o7LPP1tlnny1JysvL05VXXqmpU6fq/PPPb48/9RvYUwcAAMJGYmLigZ/fe+89zZ49W/PmzdOSJUs0evToJgf8xsXFHfg5KirqsPvxmhMdHa0FCxbowgsv1CuvvKLTTz9dknTffffpN7/5jTZv3qwxY8aotLS0zZ9xyM9u93cEAADoJN26ddOuXbua/F1FRYVSU1OVkJCgVatWaf78+e32uUOGDNGGDRu0bt06ZWVl6dFHH9WkSZO0e/duVVdX68wzz1ReXp4yMzMlSQUFBZowYYImTJig119/XZs3b/7GFcMjRakDAAAhy+v1Ki8vTyNGjFCXLl2Unp5+4Henn3667rvvPmVnZ2vIkCGaOHFiu31ufHy8HnroIU2ZMuXAQYlrr71WO3fu1LnnnquamhpZa3XXXXdJkmbOnKm1a9fKWquTTz5Zo0aNarcs+xlrbbu/aTAbO3asXbRokesYAACEhZUrVyo7O9t1jLDQ1L+lMWaxtXZsS14f0nvqjDHjjTHzjDEfGGOeMMbEuM4EAADgQkiXOkmbJZ1krT1e0gZJ57qNAwAAwsGMGTN09NFHf+XroYcech2rWSG9p85au/Wg/6yV1OgqCwAACB/33HOP6wit1mlX6owx1xtjFhlj9hpjHv7a79KMMbOMMVXGmI3GmEta+d4DJJ0m6eV2jAwAABAyOvNK3RZJv5H0LUlfv/fGPQpcaUuXdLSkV40xS6y1y40xPSU92cT7XWyt3WaMSZL0qKQrrbV1hwuxetsu1dY3KjY61FeeAQAAvtRppc5a+7wkGWPGSuq7/3FjTKKkCySNsNbulvSRMeYlSZdLuslau03SCU29pzEmWoHCd7u1dnVLctQ2NGpJUbnGZaQdyZ8DAAAQVILhctVgSfXW2jUHPbZEUkvuNfIdSRMk3WaMec8Yc1FTTzLGTNu39LtIkvLXtf8UZwAAAJeCodR1lVT5tccqJHU73AuttY9aa73W2hP2fT11iOfdb60da60d2yUmSnMLStohNgAACDVdu3Y95O82bNigESNGdGKa9hUMpW63pKSvPZYkqel7fhyhrnHR+nRTmapr235fNwAAgGATDCNN1kiKNsYMstau3ffYKEnLO+LDEuOitbfBauGGMk0a3L0jPgIAgMj0+k3StqXt+549R0pn/OGQv77pppvUr18/zZgxQ5L061//WtHR0Xr33XdVVlamuro6/eY3v9G557ZulG1NTY2uu+46LVq0SNHR0brrrrt04oknavny5fre976n2tpaNTY26rnnnlPv3r01depUFRUVqaGhQbfddpsuuqjJHWEdqtNK3b5DDdGSoiRFGWPiFdhLV2WMeV7SHcaYqxQ4/XqupNyOyJEYF6XGKKP8dSWUOgAAQtxFF12kG2644UCpe/rpp/Xmm2/qRz/6kZKSklRSUqKJEyfqnHPOkTGmxe97zz33yBijpUuXatWqVTrttNO0Zs0a3Xffffrxj3+sSy+9VLW1tWpoaNBrr72m3r1769VXX5UkVVRUdMjfejideaXuVkm/Oui/L5N0u6RfS5ou6UFJOySVSrrOWtshV+o8xmh0v1TlF3BYAgCAdtXMFbWOMnr0aO3YsUNbtmxRcXGxUlNT1bNnT/3kJz/RBx98II/Hoy+++ELbt29Xz549W/y+H330kX74wx9KkoYOHaoBAwZozZo1ysnJ0W9/+1sVFRXp/PPP16BBgzRy5Ej97Gc/04033qizzz5bxx13XEf9uc3qtD111tpfW2vN175+ve93O62137bWJlpr+1trH+/ILLlZXi3bUqHy6tqO/BgAANAJpkyZomeffVZPPfWULrroIj322GMqLi7W4sWL9dlnnyk9PV01NTXt8lmXXHKJXnrpJXXp0kVnnnmm3nnnHQ0ePFiffPKJRo4cqVtvvVV33HFHu3xWawXDQYlOl5flk7XS/EKu1gEAEOouuugiPfnkk3r22Wc1ZcoUVVRUqEePHoqJidG7776rjRs3tvo9jzvuOD322GOSpDVr1mjTpk0aMmSICgsLlZmZqR/96Ec699xz9fnnn2vLli1KSEjQZZddppkzZ+qTTz5p7z+xRYLhoESnG9U3RQmxUZq7rlSnj+jlOg4AADgCw4cP165du9SnTx/16tVLl156qSZPnqyRI0dq7NixGjp0aKvfc/r06bruuus0cuRIRUdH6+GHH1ZcXJyefvppPfroo4qJiVHPnj11yy23aOHChZo5c6Y8Ho9iYmJ07733dsBfeXjGWuvkg10ZO3asXbRoka54cIGKyqo152cnuI4EAEDIWrlypbKzs13HCAtN/VsaYxZba8e25PURufwqSXlZXhUUV2lbRfussQMAALgUkcuvkpTr90mS8gtKdP4xfQ/zbAAAEC6WLl2qyy+//CuPxcXF6eOPP3aUqH1EbKkb1itJqQkxmruulFIHAMARsNa2agacayNHjtRnn33mOsZXtMd2uIhdfvV4jHL8XuUXlLTLPyQAAJEoPj5epaWl/H/pEbDWqrS0VPHx8Uf0PhF7pU6Scvw+vbZ0mzaUVmugL9F1HAAAQk7fvn1VVFSk4uJi11FCWnx8vPr2PbKVw4gpdcaYyZImZ2VlHXgsz++VJM1dV0KpAwCgDWJiYjRw4EDXMaAIWn611r5srZ2WnJx84LGBvkT1So5XfkGJw2QAAABHLmJKXVOMMcr1+zSvoFSNjewFAAAAoSuiS50k5fq9Kquu04qtla6jAAAAtFnEl7q8rMC8unkF3AcWAACErogvdT2T45XZPVFz2VcHAABCWMSXOknK8/u0YP1O1dY3uo4CAADQJpQ6Be4DW13boCVF5a6jAAAAtAmlTtLETK+MkfLXsa8OAACEJkqdpJSEWA3vncS+OgAAELIodfvk+X36dFOZqmvrXUcBAABoNUrdPrlZPtU1WC3cUOY6CgAAQKtR6vYZl5GqmCij/HUswQIAgNBDqdsnITZao/ulKp8hxAAAIARR6g6Sm+XVsi0VKq+udR0FAACgVSh1B8nL8slaaX4hV+sAAEBoiZhSZ4yZbIy5v6Ki4pDPGdU3RQmxUZrLvDoAABBiIqbUWWtfttZOS05OPuRzYqM9Gj8wjXl1AAAg5ERMqWupXL9XhcVV2lZR4zoKAABAi1HqvibX75Mk5XO1DgAAhBBK3dcM65Wk1IQY9tUBAICQQqn7Go/HKMfvVX5Biay1ruMAAAC0CKWuCTl+n7ZW1GhDabXrKAAAAC1CqWtCnt8rSZrLLcMAAECIoNQ1YaAvUb2S4zksAQAAQgalrgnGGOX6fZpXUKrGRvbVAQCA4EepO4S8LK/Kquu0Ymul6ygAAACHRak7hP3z6uYVMNoEAAAEP0rdIfRMjldm90RuGQYAAEICpa4ZeX6fFqzfqdr6RtdRAAAAmkWpa0ZellfVtQ1aUlTuOgoAAECzKHXNmJjplTHMqwMAAMGPUteMlIRYDe+dpHwOSwAAgCBHqTuMPL9Pn24qU3VtvesoAAAAh0SpO4zcLJ/qGqwWbihzHQUAAOCQIqbUGWMmG2Pur6ioaNXrxmWkKibKKJ99dQAAIIhFTKmz1r5srZ2WnJzcqtclxEZrdP9U9tUBAICgFjGl7kjk+r1atqVC5dW1rqMAAAA0iVLXAnlZPlkrzS/kah0AAAhOlLoWGNU3RQmxUZq7jlIHAACCE6WuBWKjPRo/MI37wAIAgKBFqWuhXL9XhcVV2lZR4zoKAADAN1DqWijX75Mk5XO1DgAABCFKXQsN65Wk1IQY9tUBAICgRKlrIY/HKMfvVX5Biay1ruMAAAB8BaWuFXL9Pm2tqNH6kirXUQAAAL6CUtcKuX6vJHF3CQAAEHQoda0w0JeoXsnxHJYAAABBh1LXCsYY5fp9mldQqsZG9tUBAIDgQalrpbwsr8qq67Ria6XrKAAAAAdQ6lpp/7y6eeyrAwAAQYRS10o9k+OV2T2RW4YBAICgQqlrgzy/TwvW71RtfaPrKAAAAJIodW2Sl+VVdW2DlhSVu44CAAAgiVLXJhMzvTJGmruOJVgAABAcKHVtkJIQqxG9kxlCDAAAggalro1y/V59uqlM1bX1rqMAAABETqkzxkw2xtxfUVHRLu+Xm+VTXYPVwg1l7fJ+AAAARyJiSp219mVr7bTk5OR2eb9xGamKiTLKZ18dAAAIAhFT6tpbQmy0RvdPZV4dAAAICpS6I5Dr92r5lkqVV9e6jgIAACIcpe4I5GX5ZK00v5BTsAAAwC1K3REY1TdFCbFRmruOUgcAANyi1B2B2GiPxg9MY18dAABwjlJ3hPL8PhUWV2lbRY3rKAAAIIJR6o5Qjt8rScrnah0AAHCIUneEhvVKUmpCDPvqAACAU5S6I+TxGOX4vcovKJG11nUcAAAQoSh17SDX79PWihqtL6lyHQUAAEQoSl07yMvySZLyC1iCBQAAblDq2kGGN0G9kuM5LAEAAJyh1LUDY4xy/T7NKyhVYyP76gAAQOej1LWTvCyvyqrrtGJrpesoAAAgAlHq2smX++pYggUAAJ2PUtdO0pPi5e+eyGEJAADgBKWuHeX6fVqwfqdq6xtdRwEAABGGUteO8rK8qq5t0JKictdRAABAhKHUtaOJmV4ZI81dx746AADQuSh17SglIVYjeiezrw4AAHQ6Sl07y/V79emmMlXX1ruOAgAAIgilrp3lZvlU12C1cEOZ6ygAACCCUOra2biMVMVEGeWzrw4AAHQiSl07S4iN1uj+qZrLEGIAANCJIqbUGWMmG2Pur6io6PDPyvP7tHxLpcqrazv8swAAAKQIKnXW2pettdOSk5M7/LNys7yyVppfyClYAADQOSKm1HWmUX1TlBAbpbnrKHUAAKBzUOo6QGy0R+MHprGvDgAAdBpKXQfJ8/tUWFylbRU1rqMAAIAIQKnrILlZXklSPlfrAABAJ6DUdZDsnklKTYhhXx0AAOgUlLoO4vEY5fi9yi8okbXWdRwAABDmKHUdKNfv09aKGq0vqXIdBQAAhDlKXQfKy/JJkvILWIIFAAAdi1LXgTK8CeqdHM9hCQAA0OEodR3IGKMcv0/zCkrV2Mi+OgAA0HEodR0sL8ursuo6rdha6ToKAAAIY5S6DvblvjqWYAEAQMeh1HWw9KR4+bsnclgCAAB0KEpdJ8jL8mnB+p2qrW90HQUAAIQpSl0nyPV7VV3boCVF5a6jAACAMEWp6wQTM70yRpq7jn11AACgY1DqOkFKQqxG9E5WPveBBQAAHYRS10lys7z6dHOZqmvrXUcBAABhiFLXSXL9PtU1WC3cUOY6CgAACEOUuk4yLiNVMVFG+eyrAwAAHYBS10kSYqM1un+q5jKEGAAAdABKXSfK8/u0fEulyqtrXUcBAABhhlLXifKyvLJWml/IKVgAANC+KHWd6Ki+KUqIjdJcRpsAAIB2RqnrRLHRHo0fmMa+OgAA0O4odZ0sz+9TYXGVtlXUuI4CAADCCKWuk+VmeSVJ+VytAwAA7YhS18myeyYpNSGGfXUAAKBdUeo6mcdjlOP3Kr+gRNZa13EAAECYiJhSZ4yZbIy5v6KiwnUU5fp92lpRo/UlVa6jAACAMBExpc5a+7K1dlpycrLrKMrL8kmS5hawBAsAANpHxJS6YJLhTVDv5HjN47AEAABoJ5Q6B4wxys3yaV5BqRob2VcHAACOHKXOkVy/V2XVdVqxtdJ1FAAAEAYodY7s31fHvDoAANAeKHWOpCfFy989UfkclgAAAO2AUudQXpZPC9bvVG19o+soAAAgxFHqHMr1e1Vd26AlReWuowAAgBBHqXNoYqZXxkhz17GvDgAAHBlKnUMpCbEa0TtZ+dwHFgAAHCFKnWO5WV59urlM1bX1rqMAAIAQRqlzLM/vU12D1cINZa6jAACAEEapc2xsRqpioozy2VcHAACOAKXOsYTYaI3un6q5DCEGAABHgFIXBPL8Pi3fUqny6lrXUQAAQIii1AWBvCyvrJXmF3IKFgAAtA2lLgiM6peihNgozWW0CQAAaCNKXRCIifJo/MA09tUBAIA2o9QFiTy/T4XFVdpWUeM6CgAACEGUuiCRm+WVJOVztQ4AALQBpS5IZPdMUlpiLPvqAABAm1DqgoTHY5ST6VV+QYmsta7jAACAEEOpCyI5fq+2VtRofUmV6ygAACDEUOqCSF6WT5I0t4AlWAAA0DqUuiCS4U1Q7+R4zeOwBAAAaCVKXRAxxig3y6d5BaVqbGRfHQAAaDlKXZDJ9XtVVl2nFVsrXUcBAAAhhFIXZPbvq2NeHQAAaA1KXZBJT4qXv3si8+oAAECrUOqCUF6WTws37FRtfaPrKAAAIERQ6oJQrt+n6toGLSkqdx0FAACECEpdEJqYmSZjpLnr2FcHAABahlIXhFISYjWid7Ly2VcHAABaiFIXpHKzvPp0c5mqa+tdRwEAACGAUhek8vw+1TVYLdxQ5joKAAAIAZS6IDUuI00xUUb57KsDAAAtQKkLUl1iozS6f6rmMoQYAAC0AKUuiOX5fVq+pVLl1bWuowAAgCBHqQtieVleWSvNL+QULAAAaB6lLoiN6peixNgobhkGAAAOK9p1gM5ijJksafKoPglSQ70UFfx/ekyUR+MHprGvDgAAHFbEXKmz1r5srZ0W3VgjrXzRdZwWy/X7VFhcpW0VNa6jAACAIBYxpe6A6Dhp3j2Sta6TtEhullcStwwDAADNi7xSl9hD+mKxtHmB6yQtkt0zSWmJscovYF8dAAA4tMgrdQlpUnyKNP8e10laxOMxysn0Kr+gRDZEri4CAIDOF3mlznikMVdKK1+Wyja4TtMiOX6vtlbUaH1JlesoAAAgSEVeqZOk8dMC5e7j+10naZG8LJ8kaS5LsAAA4BAis9Ql95GGnyd98ohUU+k6zWFleBPUOzle8xhtAgAADiEyS50kTZwu1e6SPn3UdZLDMsYoN8uneQWlamxkXx0AAPimyC11fY6R+udK8+8LDCMOcnlZXpVV12nF1uC/sggAADpf5JY6ScqZLlVskla94jrJYeX6A/vq8lmCBQAATYjsUjfkTCk1IzCMOMilJ8XL3z2R+8ACAIAmRXap80RJE66TihZImxe6TnNYeVk+LdywU7X1ja6jAACAIBPZpU6SRl8qxSWHxDDiXL9P1bUNWlJU7joKAAAIMpS6uG7SmO9KK16Syje5TtOsnEyvjOE+sAAA4JsodZI0/prA94//z22Ow0hOiNGI3snKZ18dAAD4GkqdJKX0k4adGxhGvHeX6zTNys3y6tPNZaquDf4xLAAAoPNQ6vbLuV7aWyl9+pjrJM3K8/tU12C1cEOZ6ygAACCIUOr26ztG6jdBmv9PqbHBdZpDGpeRptgoj/LZVwcAAA5CqTtYzgypfKO0+jXXSQ6pS2yURvdP0VyGEAMAgINQ6g429GwppX/QDyPO9fu0fEulyqtrXUcBAABBglJ3sP3DiDfNk75Y7DrNIeVleWWtNL+QU7AAACCAUvd1oy+TYrtJ8/7pOskhjeqXosTYKG4ZBgAADqDUfV18kjTmCmnFC1JFkes0TYqJ8mj8wDT21QEAgAModU0ZP02yjdKC+10nOaRcv0+FxVXaVlHjOgoAAAgClLqmpA6Qss+RFj8s7d3tOk2TcrO8krhlGAAACKDUHUrODKmmQvrscddJmpTdM0lpibHKL2BfHQAAoNQdWr/xUt9x0sf3BuUwYo/HKCfTq/yCEllrXccBAACOUeqaM3G6tLNQWvOG6yRNys3yamtFjdaXVLmOAgAAHKPUNSf7HCm5X9CON8n1+yRJc1mCBQAg4lHqmhMVLU24Rtr4kbTlM9dpviHDm6DeyfHcBxYAAFDqDuuY70qxXaX5wXe1zhij3Cyf5hWWqrGRfXUAAEQySt3hxCdLoy+Xlj0nVW5xneYb8rK8Kq+u04qtla6jAAAAhyh1LTHhmn3DiB9wneQb9u+ry+fuEgAARDRKXUukDZSGniUtelCqDa6TpulJ8fJ3T+Q+sAAARDhKXUvlXC/VlEtLnnCd5BvysnxauGGnausbXUcBAACOUOpaqt8EqfcxgfEmjcFVnnL9PlXXNmhJUbnrKAAAwBFKXUsZE7h12M4Cae1brtN8RU6mVx7DfWABAIhklLrWGHaulNRHmvcP10m+IjkhRiP6JCuffXUAAEQsSl1rRMUETsJu+FDa+rnrNF+R4/fq081lqq6tdx0FAAA4QKlrrWOukGISg24YcZ7fp7oGq4UbylxHAQAADlDqWqtLijT6Mmnps9Kuba7THDAuI02xUR5uGQYAQISi1LXFxGulxvqgGkbcJTZKo/unaC5DiAEAiEiUurZIy5SGnBkYRly3x3WaA3L9Pi3fUqny6lrXUQAAQCej1LVVzgxpz05pyZOukxyQl+WVtdK8Ak7BAgAQaSh1bTUgV+o1KnBgIkiGEY/ql6LE2CjlU+oAAIg4lLq2MiZw67CSNVLBHNdpJEkxUR6NH5jGvjoAACIQpe5IDPu21K1XUA0jzsvyqbC4StsqalxHAQAAnYhSdySiY6Xx06TC96Tty12nkRQYQixxyzAAACINpe5IjblSikmQ5gXHMOLsnklKS4xlXx0AABGGUnekEtKkoy+Rlj4t7d7hOo08HqOcTK/yC0pkrXUdBwAAdBJKXXuYcJ3UUCst/JfrJJKk3CyvtlbUaH1JlesoAACgk1Dq2oMvSxp8hrTw30ExjDjP75MkzWUJFgCAiEGpay8506XqEunzp10n0QBvgnonx3MfWAAAIgilrr1kHCf1HBkYRux4L5sxRrlZPs0rLFVjI/vqAACIBJS69mKMNHGGVLwqKIYR52V5VV5dpxVbK11HAQAAnYBS155GXCB17RkU401y9+2ry+fuEgAARISIKXXGmMnGmPsrKio67kOiY6XxVwWu1O1Y2XGf0wLpSfHK6tFVc9dxWAIAgEgQMaXOWvuytXZacnJyx37QmO9L0V0Ce+scy/V7tWD9TtXWN7qOAgAAOljElLpOk+iVRl0sLXlK2l3sNEqu36c9dQ1aUlTuNAcAAOh4lLqOMHG61LBXWvSg0xg5mV55DPeBBQAgElDqOkL3wdKg06SFD0h1Nc5iJCfEaESfZOWzrw4AgLBHqesoOTOkqmJp2bNOY+T6ffp0c5mqa+ud5gAAAB2LUtdRBk6S0kcExps4HEac6/eqrsFq4YYyZxkAAEDHo9R1FGMCe+t2LJcK33MWY1xGmmKjPNwyDACAMEep60gjL5QSe0jz7nEWoUtslEb3T9FchhADABDWKHUdKTpOGn+1tO5tqXi1sxjHDfJp+ZZKLd/SgYOXAQCAU5S6jjb2+1JUnNNhxJdNHCBf1zj97OklDCIGACBMtbjUGWNONMYM3PdzL2PMf4wxDxljenZcvDCQ6Ns3jPhJqcrNaJGUhFj97ryRWrVtl/7x7jonGQAAQMdqzZW6f0pq2PfzXyTFSGqUdH97hwo7E6dL9TVOhxGfOixd54/uo3veXadlX7AMCwBAuGlNqetjrd1kjImW9C1J0yRdJym3Q5KFkx5DpaxTAsOI6/c6i/GrycPlTYzVz59Zor31DYd/AQAACBmtKXWVxph0SZMkrbDW7t73eEz7xwpDE6dLu7dLy553FiE5IUa/Pz+wDPv3OSzDAgAQTlpT6v4uaaGkxyTtn9GRJ2lVe4cKS/6TpO7ZgfEmDocRn5ydrguO6at73y/Q50XlznIAAID21eJSZ639o6RTJOVZa5/c9/AXkq7qiGBhxxgpZ7q0fam04UOnUf5n8jD5urIMCwBAOGnVSBNr7RprbYEUOA0rqZe1dmmHJAtHI6dKCT6nw4glKblLjP5wwVF+YLN/AAAgAElEQVRas323/jZ7rdMsAACgfbRmpMn7xpi8fT/fKOlJSY8bY27pqHBhJyZeGneVtOYNqcTtnrYTh/TQ1LF9dd/7BfpsM8uwAACEutZcqRshaf6+n6+WdKKkiZKube9QYW3cD6SoWKfDiPe79exhSk+K18+fWaKaOpZhAQAIZa0pdR5J1hjjl2SstSustZslpXZMtDDVtYd01FTps8el6p1OoyTFB5Zh1+3Yrf83e43TLAAA4Mi0ptR9JOkfku6UNEuS9hU87hTfWhOnS/V7pMUPuU6iSYO76+Jx/fTAB4X6ZFOZ6zgAAKCNWlPqrpRULulzSb/e99hQSX9r30gRIH24lHmitOABqb7WdRr98qxs9WQZFgCAkNaakSal1tpbrLW/2j942Fr7qrX2rx0XL4zlzJB2bZWWz3KdRN3iY/THC49SYXGV7nqbZVgAAEJRa06/xhhjbjfGFBpjavZ9v90YE9uRAcOW/2TJN0Sa73YY8X7HDequSyb01wMfFmrxRrd7/QAAQOu1Zvn1TwoMH75W0qh930+S9McOyBX+PB5p4nXS1iXSxrmu00iSbjkzW72Tu2jmM5+zDAsAQIhpTambIukca+1b1trV1tq3JJ0naWrHRIsAoy6WuqRJ89yPN5GkrnHR+tOFR6mwpEp3vrnadRwAANAKrSl1ppWP43BiugTm1q1+TSotcJ1GkpSX5dNlE/vr33PXa+EGlmEBAAgVrSl1z0h62RjzLWNMtjHmdEkv7HscbTXuaikqRvr4PtdJDrj5jGz1Semimc8s0Z5almEBAAgFrSl1v5A0W9I9khZL+rukdyXN7IBckaNbujTiQunT/0p7gmNOXGJctP584ShtKK3Wn95c5ToOAABogWZLnTHmpP1fko6V9J6kaZImS7pGgVJ3bEeHDHs506W6amnxf1wnOSDH79UVOQP0cP4GfVxY6joOAAA4DGObGadhjFl/iF/tf5GRZK21me0drKOMHTvWLlq0yHWMb/rPZKlknXTD54Hl2CBQXVuv0//6oSTpjRuOU0JstONEAABEFmPMYmvt2JY8t9krddbagYf4ytz3NTCUCl1Qy7le2rVFWvGi6yQHJMRG688XHqVNO6v1pzc4DQsAQDBrzZ46dKSsUyXvIGneP4JiGPF+EzK9ujI3Qw/nb9C8ApZhAQAIVpS6YLF/GPGWT6VN812n+YpfnD5EGd4E/eK5JaraW+86DgAAaAKlLpiM+o7UJTVwtS6IJMRG689TRqmobI/+8DqnYQEACEaUumASmyCN/b606lVpZ6HrNF8xLiNN388bqEfnb1T+uhLXcQAAwNdQ6oLNuKslT7T08f+5TvINPz9tiAb6EjXz2c+1m2VYAACCCqUu2CT1kkZcsG8YcbnrNF/RJTZKd045Slsq9uj3r610HQcAAByEUheMcqZLtbulTx5xneQbxgxI01XHDtRjH2/SR2tZhgUAIFhQ6oJRr1FSxnGBJdiG4Fvm/NlpQ5TZPVE3Pve5dtXUuY4DAABEqQteE6dLlUXSyuAZRrxffEyU7pwySlsr9uh3LMMCABAUKHXBavDpUlqmNO+frpM06Zj+qbr6+Ew9sWCzPlhT7DoOAAARj1IXrDyewNW6LxZJmxe4TtOkn5wyWFk9uurG5z5XJcuwAAA4RakLZkdfIsWnBN0w4v32L8Nur6zRb19hGRYAAJcodcEsNlEac6W08mWpbKPrNE06ul+Krpnk11OLNuvd1TtcxwEAIGJR6oLd+GmS8QTlMOL9bjhlkAand9XNzy1VxR6WYQEAcIFSF+yS+0jDzwvMrKupdJ2mSXHRgWXY4t179b+vrHAdBwCAiESpCwUTp0u1u6RPH3Wd5JCO6pui6yb59eziIr2zarvrOAAARBxKXSjoc4zUP1f6+L6gHEa83w9PztKQ9G66+fmlqqhmGRYAgM5EqQsVOTOk8k3SqldcJzmkuOgo/WXqKJXsrtXtryx3HQcAgIhCqQsVQ86QUjOk+cE5jHi/EX2SNeMEv57/5AvNXsEyLAAAnYVSFyo8UYG9dZs/looWuU7TrOtPGqShPbvp5llLVV5d6zoOAAARgVIXSo6+VIpLlubd4zpJs2KjPbpzyiiVVdXq9pc5DQsAQGeg1IWSuK7SmO9KK16Uyje7TtOsEX2SNePELM369Au9tXyb6zgAAIQ9Sl2oGX9N4PuC4B1GvN+ME7M0rFeSbpm1TGVVLMMCANCRKHWhJqWfNOxcafF/pL27XKdp1v5l2PLqWv3qJU7DAgDQkSh1oSjnemlvpfTpY66THNaw3kn60cmD9NKSLXpj2VbXcQAACFuUulDUd4zUb0JgvEljg+s0h3XdCX6N6JOkW19Ypp0swwIA0CEodaEqZ4ZUvlFa/ZrrJIcVExVYhq3YU6f/eXGZ6zgAAIQlSl2oGnq2lNI/6Meb7De0Z5J+fPIgvfL5Vr22lGVYAADaG6UuVHmipAnXSZvmSV8sdp2mRa6d5NfIPsm69YVlKtm913UcAADCCqUulI2+TIrtJs0L7luH7Rcd5dFfpo7S7pp6lmEBAGhnlLpQFp8kjblCWvGCVFHkOk2LDE7vphtOHaTXlm7TK59vcR0HAICwQakLdeOnSbZRWnC/6yQtNu24TI3ql6LbXlim4l0swwIA0B4odaEudYCUfY60+GFp727XaVokOsqjOy88SlW1Dbr1haWy1rqOBABAyKPUhYOcGVJNhfTZ466TtNig9G766amD9eby7XppCcuwAAAcKUpdOOg3Xuo7Tvr43pAYRrzf1cdlanT/FP3qpeXasavGdRwAAEIapS5cTJwu7SyU1rzhOkmLRXmM/nzhKFXXNuiXs5axDAsAwBGg1IWL7HOk5H4hM95kv6weXTXztCF6e8V2vfgZy7AAALQVpS5cREVLE66RNn4kbfnMdZpW+f6xAzVmQGpgGbaSZVgAANqCUhdOjvmuFNtVmh9aV+sCy7BHqaauQbfM4jQsAABtQakLJ/HJ0ujLpWXPSZWhtZSZ2b2rZn5riGav3KFZn37hOg4AACGHUhduJlyzbxjxA66TtNr38gZqXEaqfv3Scm1nGRYAgFah1IWbtIHS0LOkxQ9JtVWu07TK/tOwtQ2Nuvl5lmEBAGgNSl04yrle2lMmLXnCdZJWy/Al6sbTh+qdVTv07OLQuJ8tAADBgFIXjvpNkHofI82/V2psdJ2m1a7IydD4gWm645UV2lqxx3UcAABCAqUuHBkTuHVY6Tpp7Vuu07SaZ99p2PoGq5ueYxkWAICWoNSFq2HnSkl9pPn3uE7SJgO8ibrpjKF6f02xnlnEMiwAAIdDqQtXUTGBk7DrP5C2fu46TZtcPnGAJmam6X9fWaEt5SzDAgDQHEpdODvmCikmMbC3LgR59p2GbbBWNz73OcuwAAA0g1IXzrqkSKMvk5Y+I+3a5jpNm/RLS9DNZ2brw7UlenLhZtdxAAAIWpS6cDfxWqmxXlr4L9dJ2uzS8f2V6/fqt6+uVFFZtes4AAAEJUpduEvLDAwjXvgvadd212naxOMx+uMFR8laTsMCAHAolLpIcOItUv1e6YmLpNrQvNLVLy1Bt5yVrY/WlejxBZtcxwEAIOhQ6iJB+nDpwgelLZ9Jz18tNTa4TtQml4zvr2OzfPrdqyu1eWdollMAADoKpS5SDDlDOv0P0qpXpLf/x3WaNjHG6I8XHiVjjG587nM1NrIMCwDAfpS6SDLxWmn8NdK8f0gL/+06TZv0SemiW8/KVn5BqR77eKPrOAAABA1KXaQ5/ffSoG9Jr82U1s52naZNLhrXT8cP7q7fv75Km0pZhgUAQKLURR5PVGB/Xfow6ZkrpW3LXCdqNWOM/nD+SEUZo5nPLmEZFgAAUeoiU1xX6ZKnpbhu0uNTpcqtrhO1Wu+ULrrt7GH6eP1OPTqfZVgAAEK61Blj0o0x+caY940x7xhjernOFDKSekuXPCXtKd836qTKdaJWmzK2r04Y0l1/eH2VNpaGXn4AANpTSJc6SSWSjrXWTpL0iKQfOM4TWnodJU15SNq2VHou9EadBJZhj1J0lNHMZzgNCwCIbCFd6qy1Ddbaxn3/2U3Scpd5QtLgb0mn/1Fa/ar01m2u07Raz+R4/c/Zw7Rgw049nL/BdRwAAJzptFJnjLneGLPIGLPXGPPw136XZoyZZYypMsZsNMZc0or3PdoY87Gk6yV90s6xI8OEadKE66T590gLHnCdptUuHNNXJw3toT+9uUrrS1iGBQBEps68UrdF0m8kPdjE7+6RVCspXdKlku41xgyXJGNMT2PMe0189ZQka+1n1toJkm6TdHOn/CXh6Fu/lQafIb3+C2nNW67TtIoxRr8/f6Riozya+cwSNbAMCwCIQJ1W6qy1z1trX5BUevDjxphESRdIus1au9ta+5GklyRdvu9126y1JzTxtc0YE3vQW1VIYmhZW3mipAv+JfUcKT37vcA+uxCSnhSvX58zXIs2lumhuetdxwEAoNMFw566wZLqrbVrDnpsiaThLXjt0caYD4wx70q6QdKfm3qSMWbavqXfRcXFxUeeOFzFdZW+85QUnyw9NlWq3OI6UaucN7qPTsnuoT+/uVqFxbtdxwEAoFMFQ6nrKqnya49VKHDwoVnW2gXW2uOttSdaa8+w1jY5cM1ae7+1dqy1dmz37t3bIXIYS+oVmGG3t1J6/CJpb+iUI2OMfnfeSMXHROnnLMMCACJMMJS63ZKSvvZYkqRdDrJAknqOkKY8LG1fJj33g5AaddIjKV63nzNcn2wq14MfsQwLAIgcwVDq1kiKNsYMOuixUWI8iVuDTpXO/LO05g3pzVtcp2mVc4/urdOGpevPb63Wuh2hc6URAIAj0ZkjTaKNMfGSoiRFGWPijTHR1toqSc9LusMYk2iMyZN0rqRHOysbDmHcVVLO9dLH90kf/5/rNC1mjNFvzhuhhNgo/fTpz1Sye6/rSAAAdLjOvFJ3q6Q9km6SdNm+n2/d97vpkrpI2iHpCUnXWWu5UhcMTr1DGnKW9MZN0uo3XKdpsR7d4vWH84/Sqq27dPJf3tfTizbLWvbYAQDCl4m0/6MbO3asXbRokesYoaW2SnroTKlkrfT916Veo1wnarF1O3bp5ueXauGGMuX6vfrdeSOV4Ut0HQsAgBYxxiy21o5tyXODYU8dgl1sonTJU1KX1MCJ2IovXCdqsawe3fTUtBz99rwRWlpUoW/99QPd8+461TU0Hv7FAACEEEodWqZbT+nSpwMjTp64SNobOoeTPR6jSycM0OyfTdJJQwNz7Cb//SN9trncdTQAANoNpQ4tlz5cmvqwtH2F9Oz3pYZ614laJT0pXvdeNkb3Xz5G5dV1Ou+fc3X7y8u1e29o/R0AADSFUofWyTpFOutOae1bgcMTIbgn87ThPfX2T4/X5RMH6OH8DTrtrvf1zqrtrmMBAHBEKHVovbHfl3J/KC18IDDuJAR1i4/RHeeO0LPX5qhrfLS+//AizXj8E+3YVeM6GgAAbUKpQ9uccoeUPVl642Zp1Wuu07TZmAFpeuWHx+lnpw7W28u365S/vK+nFm5i/AkAIORQ6tA2Ho903v1S79GBW4lt+dR1ojaLjfbohycP0us3HKehvZJ043NLdfH981VYzN0oAAChg1KHtotNkL7zpJTgkx6/WKoocp3oiPi7d9WTV0/U788fqRVbK3X63z7UP95Zq9p6xp8AAIJfxJQ6Y8xkY8z9FRUVrqOEl27pgVEnddXSY1OlmkrXiY6Ix2P0nfH9Neenk3RqdrrufGuNJv/9I32yqcx1NAAAmhUxpc5a+7K1dlpycrLrKOGnR7Y09T9S8Srp2e+F3KiTpvRIitc9lx6jf313rCpr6nTBvfn61YvLGH8CAAhaEVPq0MH8J0ln3yWtmy29/ouQHHXSlFOGpevtn07SFTkZemT+Rp161/uavYLxJwCA4EOpQ/sZc6WUd4O06N/S/H+6TtNuusZF69fnDNdz1+UqKT5GVz2ySDMe+0Q7Khl/AgAIHpQ6tK+TfyUNO1d685fSyldcp2lXx/RP1cs/PFYzvzVEb6/crpPvel9PLNikxsbwuCoJAAhtlDq0L49HOu//pD5jpOeukr74xHWidhUb7dGME7P0xo+P0/DeSbr5+aW6+IH5WreD8ScAALcodWh/MV2k7zwhde0uPXGxVL7ZdaJ2l9m9q564eqL+dMFRWr1tl87824e6ew7jTwAA7lDq0DG69pAueUaqq5EeD/1RJ00xxmjquH6a/dNJOm14uu56e43OuvtDLd6403U0AEAEotSh4/QYKl30iFSyRnrmCqmhznWiDtG9W5z+cckxevDKsaraW68L75un215Ypl014fn3AgCCE6UOHSvzBOnsv0oF70ivzQybUSdNOWloYPzJlbkZ+u/HG3XqXR/ozeXbXMcCAEQISh063jGXS8f+VFr8kJT/d9dpOlRiXLR+NXm4Zk3PU0pCjK55dLGufXSxtjP+BADQwSh16Bwn3SYNP096+3+kFS+5TtPhju6Xopd/eKx+cfoQvbt6h075y/v67/yNjD8BAHQYSh06h8cjffteqe846flpUtFi14k6XEyUR9NPyNIbNxyvkX2TdesLy3TR/fO0bscu19EAAGGIUofOs3/USbd06YmLpLKNrhN1ioG+RD121QT9+cKjtGb7bp35t4/019lrtLe+wXU0AEAYodShcyX6AqNOGmoDo072lLtO1CmMMZoytp/m/GySTh/RU3+dvVZn3f2RFm5g/AkAoH1Q6tD5ug+WLvqvVLourEedNMXXNU53f2e0HrpynPbUNmjKffP0y1lLVcn4EwDAEaLUwY2Bx0uT75YK35Ne/WlYjzppyolDe+itnxyvHxw7UE8s2KRT/vK+3li21XUsAEAIi5hSZ4yZbIy5v6KiwnUU7Df6Uun4mdInj0hz/+Y6TadLjIvWbWcP06zpefJ2jdO1//1E0x5ZpG0VjD8BALSesRF2hWTs2LF20aJFrmNgP2ul566Slj0rTfmPNPzbrhM5UdfQqH9/tF7/7+01iony6MbTh+jSCQPk8RjX0QAADhljFltrx7bkuRFzpQ5Byhjp3HukfhOlWddImxe6TuRETJRH107y662fHK9R/ZJ124vLNeX/5mnNdsafAABahlIH92LipYsfk7r1kp64WCrb4DqRMwO8ifrvDyboL1NGqaB4t866+0Pd9dZq1dQx/gQA0DxKHYJDok+69BmpsV56LHJGnTTFGKMLxvTVnJ9O0lkje+nud9bpzLs/1MeFpa6jAQCCGKUOwcM3KHDFbmeh9PTlUn2t60ROebvG6a8Xj9Z/vj9etfWNuuj++br5+c9VsYfxJwCAb6LUIbhkHCud+w9p/QfSqz+JuFEnTZk0uLve+snxuvq4gXpq4Wadctf7em3pVkXaIScAQPModQg+oy6WJt0offpf6aO7XKcJCgmx0frlWcP04oxj1aNbnKY/9omufmSxtpTvcR0NABAkKHUITifcLI2cIs25Q1r2vOs0QWNk32S9OCNPt5w5VB+tK9apd72v/+RvUEMjV+0AINJR6hCc9o866Z8jzbpW2rzAdaKgER3l0bTj/Xrrhkk6ZkCqfvXScl14X75Wbat0HQ0A4BClDsErOk66+HEpuW9g1MnO9a4TBZX+3gQ98v3xumvqKG0oqdLZd3+kO99k/AkARCpKHYJbQlpg1IltlB6bIu0pc50oqBhjdP4xfTXnZyfonFG99Y931+mMv32oD9cWq5ElWQCIKNwmDKFhY770yLlSvwnSZc9L0bGuEwWlD9cW65ZZS7V55x75usbppKHdddLQHjp2UHd1jYt2HQ8A0EqtuU0YpQ6h4/Onpeevlo6+NLDfznBf1KbsqW3Q68u26p1VO/T+mmLtqqlXTJTRxEyvThzSQydn99AAb6LrmACAFqDUNYNSF+Le+6P03u+kk26Vjp/pOk3Qq2to1OKNZXpn1Q7NWbldBcVVkqTM7ok6eWgPnTi0h8ZlpCkmip0YABCMKHXNoNSFOGsDp2E/f1K64N/SyAtdJwopG0ur9M6qHXpn1Q59XLhTtQ2N6hYXreMHB5ZpTxjSXd6uca5jAgD2odQ1g1IXBur3So+eJxUtlK54Weo/0XWikLR7b70+Wluid1ft0Durd6h4114ZIx3dL+XAVbxhvZJkWOYGAGcodc2g1IWJ6p3Sv08NfL9qtuT1u04U0hobrZZvqdScVdv17qodWlJUIUnqlRyvE4f20ElDeigvy6cusVGOkwJAZKHUNYNSF0ZKC6R/nRIYe/KDtwPf0S527KrRe6uL9c7KHfpwbbGqahsUF+1Rjt974Cpe39QE1zEBIOxR6ppgjJksaXJWVtbVa9eudR0H7WXTfOk/k6W+46TLZwUGFqNd7a1v0ML1ZZqzarveWbVDG0urJUlD0rvppOweOmloD43ul6JoDlsAQLuj1DWDK3VhaOmz0nM/kI66WDrvPkaddCBrrQpLqvTOysBhi4Ubdqq+0SolIUaT9h22mDS4u1ISmCMIAO2hNaWOaaQIfSMvDNxC7N3fSGmZ0gk3uk4Utowx8nfvKn/3rrr6+ExV1tTpwzUlmrNqu95bXawXP9sij5HGDkjTiUMDM/EG9ejKYQsA6ARcqUN4sFZ6Ybq05HHp/Aeko6a6ThRxGhqtlhSV691VOzRn5Q6t2FopSeqb2kUnDQ0s007M9Co+hsMWANBSLL82g1IXxuprpf+eL23+WPrui9KAXNeJItrWij16d1Wx3lm1XR+tK1FNXaO6xEQpL8unk7N76MQhPdQzOd51TAAIapS6ZlDqwtyeMulfp0rVJdJVcxh1EiRq6ho0r7D0wF68L8r3SJKG9046cBVvVN8UeTws0wLAwSh1zaDURYCd6wOjTuKTAsWOUSdBxVqrNdt377uzxXYt3limRiv5usZq0uDAPrzjBvnULT7GdVQAcI5S1wxKXYTYvEB6+Gyp+2Bp6NmSNytw1S7NHyh7CBplVbX6YG2x5qzcoffXFKtiT52iPUbjB6YduIqX2b2r65gA4ASlrhmUugiy8mXpzV9K5ZskHfS/867pgZKXlrmv7O37ShvInDvH6hsa9cmm8gNX8dZs3y1JyvAm6KSh6To5u4fGZaQpNpqZeAAiA6WuGZS6CFRXI5Wtl0rXHfRVGPhetePL5xmPlNwvcEXvQNnb93NyP8nDqc3Otnlntd5dHdiHl19Qqtr6RnWNi9Zxg3w6cWjgsEX3bhRxAOGLUtcMSh2+oqYicLux0oJAydu573tpgbS38svnRcVKqQO/WvT2f3XtwcDjTlBdW6+560oPXMXbXrlXkjSqX4pOGhLYize8dxIz8QCEFUpdMyh1aBFrparir13d21f+dhZKDXu/fG5sN8n7taXc/fv3uqS4+xvCmLVWK7ZWBk7Trt6hzzaXy1qpR7c4nZydrlOH9VCu38dMPAAhj1LXDEodjlhjg1RR9GXRO3B1b11g/55t/PK5Cb5vLuXu378X08Xd3xBmSnbv1furizV75XZ9sKZYVbUN6hITpeMG+XTKsHSdNLSHfF1ZpgUQeih1zaDUoUPV75XKNhx0Ze+g77u3HfREIyX3/eZSrtcvJfeXoriDX1vtrW/Q/MKdmr1iu+as3K4tFTUyRhrdL0WnDEvXqdnpyuLWZQBCBKWuGZQ6OLN310F79wq/vLpXsk7aW/Hl8zwxUmpG0/v3uvVk/14r7F+mnb1ih2av3K6lXwT+nQd4E3RKdrpOyU7XuIxURUdxmhZAcKLUNYNSh6BjrVRd+rW9ewct7dbXfPncmMQm9u/tG8/CkOXD2lqxR3NWBgpe/rpS1TY0KrlLjE4c0l0nZ6dr0pDuSmLoMYAgQqlrBqUOIaWxUar84qCTuQft3yvbKNmGL5/bJe3LktfnGGnM91jGbUbV3np9uLZEs1du1zurdmhnVa2iPUYTM706JbuHTs5OV7+0BNcxAUQ4Sl0zKHUIG/W1UvnGrxa9/V+7tkoZx0kXPhgYuYJmNTRafbqpTG+v3K7ZK7aroLhKkjS0ZzedOiywTDuyTzL3pgXQ6Sh1zaDUISIseVJ6+QapS6o09RGp3zjXiUJKYfFuzVm5Q2+v3K5FG3aq0Urdu8XplOweOiU7XXlZjEsB0Dkodc2g1CFibFsqPXWZVPGFdPrvpXFXcciiDcqqavXemh2avSJwb9rde+sVH+PRcYO669TsdJ04lLtaAOg4lLpmUOoQUfaUSc9fI619Uxr1Hems/9/enYdHXZ77H3/fWcnGErKggLKEgIpLq7jhVgEXChW1VluXHnuUVqyttZ7WtrZaa3/V1mo9rVqp2lq17oq74nJEUVGprQoCCauAJiSsWcg6z++P55uN4siSmW8y83ld11zBycx37rkM4TPPcj83QobWie2qxpZW3l6+gZeCadq2dikHDe3PxH2KmbRvMaPULkVEupFC3XaY2VRgaklJyYXl5eVhlyMSP5EIvPY7ePU3UDwWzrzHNz+W3dLWLqVtN+0Ha3y7lL3yg3Yp+xYxblg+6WqXIiK7QaEuCo3USdIqfxEevQBwcNodUHpC2BUllIrNDby82I/gvbFsPU0tEfr2SeNLY/xO2mNLC+mXpXYpIrJzFOqiUKiTpLZhBTx0LlQsgGN/7G8pGknqbm3tUl4O2qWsD9qlHDYiv73psdqliMiOUKiLQqFOkl7zVnj6Mnj/H1AyCU6bqcbFMdQacfx79UZeDE61WLquFvDtUvw0bTEHqF2KiHwGhbooFOpE8KdYzL8Lnvsx9N3Tr7Pb48Cwq0oKK6rreHlRJS9+VMn8VRtpjTgK8zKZMKajXUpWhtqliIinUBeFQp1IJ2vmw4PnwtYNMOUmOOgbYVeUVDbVN/HqkipeXFTJnCUd7VKOKilk0r5FHD+mWO1SRMCfoFNdBqMmhV1J3CnURaFQJ7KN2ip45HxY+Toc8o1rECYAACAASURBVN++p12agkS8NbVEeHvFel76qJKXFq1j7aatXdqlTNynmNJitUuRJLNhBbz+e3j/foi0+Gbq+54SdlVxpVAXhUKdyHa0tsAr18AbN8PgQ/wvzn6Dw64qaTnnWPRpDS8tquTlRZW8H7RLGZqf5fvh7VPMuOFqlyIJbMMKeP0G+Pf9kJIGB38TVr8Nm9fAjHlJdfyhQl0UCnUiUXz0BMyaAWl94Iy/wvBjwq5IgMotDe398OYuraapJUJenzS+NLqICfsUcVxpEf2y1S5FEsCG5fBaMDKXkgYH/xccdalf+7tuMdx+DJRMhLPuS5oTchTqolCoE/kcVWXw4NmwfilMvBqO/F7S/PLsDeqbfLuUlz7qaJcCMGRAFqOL8ygdlMeYQXmUFucxojCHzDRtupBeYP2yYJr1AUhN92Fu/KXQd4+uj3vzjzD7Sph2W9KsAVaoi0KhTmQHNNbAExf7kbt9vgLTboXMvLCrkm34dimbmLd8PUsqalhSUcOyqlpaIv73emqKMbwgh9GD8nzgK/aBb2h+NqlqoSI9wfpl8NoN8MGDPswd8i0Y/33IG7T9x0da4W9ToHIBXPQm9B8a33pDoFAXhUKdyA5yzn8qfukqGFgCZ94LhaPDrko+R1NLhJXr61hcUUNZRQ1LKmsoq6zh4w31tP2675OewqiijpBXGoS+4r6Z2ogh8VG91B9f+OFDkJrhN2mN/95nh7nONqyA28bDkEPg3FkJ30BdoS4KhTqRnbTiNXj4fGhpgFNugf2mhV2R7IL6phbKK2tZUulH9MqCr+tqGtsf0y8rPZjCzWV0cR6jB/VldHGe1utJ96kuD8Lcw5CaCeP+2y/xyCveuevMvwue/gGc/Ds4bHpsau0hFOqiUKgT2QWb18LD34Q17/pfwBOugtS0sKuSbrCxrql9NK9tCndJZQ01DS3tjynum9kxqlecx+hBeYwqylOTZNlx1eUw57ew4JGOMDf++7u+i9U5uO+rsPIN+M5cKCjp3np7EIW6KBTqRHZRSyO88FN49w4YdjR89a+QWxh2VRIDzjkqtjR0CXlllTWUV9bS2BIB/N6ZvfOz20Ne27q9YQU5arUiHarK4LXfwoJH/a76tpG57mhJsuUTuPVwKCiF859P2A+aCnVRKNSJ7KZ/3w9PXwpZ+b6f3dBxYVckcdIacaxaX0dZZY1fsxeM7q2oriPYm0FGagojCv3mjNLivGAaN4/B/bN0vm0yqVoSjMw9CulZMO6CIMx18wfBDx6Gxy7wswdHX9a91+4hFOqiUKgT6QaffgAPnuM/KZ98nV/krAX2SauhuZVlVbVByKtlScUWyiprWbtpa/tjcjJSGdUp5LWFPh2DlmDWLQ5G5h6D9Gw4NAhzOQWxeT3n/NKQxc/C9Fdh0NjYvE6IFOqiUKgT6SZbN8Jj06F8Nhz4DZhyo/9ELhLY0tDsN2d0GtVbUlnDhqC3HsDAnIz2KdyOr7nk9dHmjF5l3SI/MrfwcR/mDpsOR1wCOQNj/9p16/00bG4xXPgKpGXE/jXjSKEuCoU6kW4UifhP5a9e5z8hf+0eyB8edlXSgznnqK5t+o+NGWWVNdQ3tbY/bnD/LEqLc/0O3EG5lBbnMbIwlz7p2pzRo1R+5H8HLJwFGTlw6HQ44rvxCXOdLXkO7j8Ljv4hTPhFfF87xhTqolCoE4mBstl+XQsGp98BoyaFXZH0MpGIY+2mre0hr210b1lVLc2tHc2Uhw3M7rJeb1RxHnsPzNbmjHirXAhzrvcNyjNy4bBv+zCXnR9eTbMuhvf/Ad+anVBrfRXqolCoE4mRDSvgwXN9p/fjroBjfpTwTUEl9ppbI6ysruuyMaOssoZVnZopp6caIwpyKSnOpbQoj1HFuYwqytVO3FioWODD3KInISMvCHMXhxvm2jRsgduOhLRM+PbrkJEddkXdQqEuCoU6kRhqqvcNQT94AEadAKfNhKwBYVclCai+qYWl62opr6ylfF0t5ZU1lK+rZfXGjrCXFhyTVlqcR0lRLqOK/TTusIE5ZKQp7O2Uig+DMPeUD3OHfwcOn9Ezwlxny+fA378Ch34bJv827Gq6hUJdFAp1IjHmHMy/E567AvoN9uvs9jgg7KokSWxt8jtxy9fVUFbpQ9/SdV1H9tJSjGEFOYwq8iN6o4r96N7wghwy07Rmr4tPP/BhbvHTkNkXDvsOHH5RzwtznT37I3jndjjvCRhxXNjV7DaFuigU6kTiZPU78NB5fpfs1JvhwLPCrkiSWFvbFT+y5wPf0nW1rFrf0WMvNcXYe2A2o4py20f3SovzGF6Qk3wbND593+9mXfw0ZPYLRuYu6h0j7031cPvRvmH6RW9An35hV7RbFOqiUKgTiaPadfDIt2Dl67756Im/Sbh2A9K7NTS3sryqjvJ1Ne2Br3xdLavW19MapL0Ug2EDc7pM4ZYU5SbmbtxP34dXr4clz/gwd8QMPzqX1T/synbO6nfhrhN8u6Vpt4RdzW5RqItCoU4kzlpb4OWr4c0/wpBx/hSKvnuGXZVIVI0trayorvNBL1ivV1ZZw8ptwt5e+dmUFPneen6Dhm+90uvOxf3kX35kbsmzfmTr8Iv9JojeFuY6e/kaeP338PUHYPTJYVezyxTqtsPMpgJTS0pKLiwvLw+7HJHks3AWPHGxb1D81b/C8KPDrkhkpzW1RFgZHJXWPrJXWcuK6jpagrBnBkMHZFNanNsR+IryGFmUQ3ZGDzufdO17fs1c2fM+zB3xXR/mevmUJQAtTfCX46G2EmbMi3/vvG6iUBeFRupEQlS1BB44GzYsh0m/9P+A6HgxSQBNLRFWra9rH9ErX1fL0spalld39NkzgyEDshjV3nYlj1FFuZQU5ZKTGeewt/affpq1/AXo0z8Ic9MTI8x1VrEAZh4HYybDGXf3yt83CnVRKNSJhKxhix+xW/Qk7DsNTvkTZOaFXZVITDS3Rli1vr59Cret/cryqjqaWiPtj2s7QWNUpw0aJUW55HZ32FvzT5hznT/eL2uA7zF36LehT9/ufZ2e5PUb4eVfwml3wAFnhF3NTlOoi0KhTqQHcA7e/F946WoYOArOvBcKS8OuSiRuWlojrNpQ395ypSzot7esqpamlq5hryRovVJanEdJ0Fh5p8/GXTPfH+e39MUgzH3XH+mVyGGuTaQV7joJqpf4adhetqZXoS4KhTqRHmT5HL87tqURpt0K+34l7IpEQtXSGmH1xq0dI3uVPvAtq6qlsVPYK8zLZGRhDiMKcxlRkMPIQr8bd/CALFJTOk0xrn7Xj8wtfQmy8uHIS+DQC5NvdHz9MrhtPAwbD2c/0qumYRXqolCoE+lhNq/x/ezW/hPGfx+O/wWk9rDF5CIha4041mysD0b0/PTt8qpallfXsam+uf1xGWkpDBuYzYTcVZxRex8jNs+jJXMAzYdfQtaR05MvzHX29kx47n9gyk1wyLfCrmaHKdRFoVAn0gO1NMLzV8D8u2D4MXD6XZBbGHZVIr3ChromllcFo3nL3+KLK2cydut8Nrg8bm+Zwj2tk6inDwW5GYwoyGVkUQ4jCnIZEYz0DR2QRVoynJEbicA90/xU9EVvQP7wsCvaIQp1USjUifRg/7oPnrkMsgf6fnZDduj3mIh8PM+vmVv+f5BdAOO/R/MXz+fj2pSOUb2qOpYFo3sb6pran5qeauw9MIcRBcF0bmHbdG4O/bMTrFn45jVw6xFQPBb+62lI6fn9BBXqolCoE+nhPn0fHjwHairg5Ovh4PN71foXkbha9Ra8+htYMQdyCuHI78G4/4aMnKhP21TfxLIg7C3rNJW7an1dewsWgPycjCDs+aDXFvr2ys8mvbeO7v37HzDrIjjhWr/GsIdTqItCoU6kF6jfAI9d6Bd3H3Q2fPn3vmmxiHir3gzC3Gs+zI3/vl8n9jlh7vO0tEZYs3GrH9GrqmN5dVvoq6O6trH9cWkpxl752YwIRvTapnJHFuaSn9PDR/ec8/0yl74E354DRfuEXVFUCnVRKNSJ9BKRVt/pfs71MOgAOPMeGDAs7KpEwvXJv+DFXwRhrqhTmMuO+Utv3trcPo27vLpjOndldX2Xnnv9s9O7TOWOKMilpCiHvfJzyEjrIaN7tVVw62HQbwhc8DKk7mSLmDhSqItCoU6kl1nyPDw+HTA4/U4YNTHsikTir2ELvHItvPsXv+b0qMvg4P+KS5j7PK0Rx9qNW1nWKei1hb91NR2je6kpxtABWZ1G93Lbw19BbgYW72UWHz0JD50Lx14BX/pJfF97JyjURaFQJ9ILbVgOD54HlQvgSz+Foy+HlB7yiV8klpyDj57wu8NrKmDcBTDh573mOK+ahuYuI3ttoW9FdV2Xvnt5fdKCNXvB2r2CHEYW5bL3wGwy02K4meGx6fDhI3DBSzD4i7F7nd2gUBeFQp1IL9VUD0//AD54AEpPglNvh6z+YVclEjsbV8Gzl/sjvQbtD1NuhiEHh11Vt4hEHGs3bWV59TY7c6vqqNjS0P64FIMhA7IZUZjDsIE5FOZldtxy/df8nIxd37SxdZPfDZuZ59fX9cC1uwp1USjUifRizsG7d8DzP/FrYc68FwaNDbsqke7V2gxv/QlevR4sBY7/mT+fNUmactc1trCi2oe8ZZ3asXy8oZ7axpbtPic/J4PC3EwK8oKvQeBr+9r25/ycjK4nbgAsfRnuPc0fnXbir+PwDneOQl0UCnUiCWD1O/4Uiq2bYOrNcOCZYVck0j0+ngdPXQpVi2DMFN/Wp9+QsKvqMbY2tVJd20hVbSNVNf5WXdv1a9v3Gpoj//H8FIP8nM5BL4PCvEymrbmBMWsfZcGkf5BZcjSFuZn0y0onZdsAGAKFuigU6kQSRO06ePh8WDXXH0x+wq8hrYe3UhD5LPUb4KWr4L2/Q98hMPl3MGZy2FX1Ws456ppaqQ5CXnWnsNcR/pr8/TWNpLXW81zGFRiOk5uuo44s0lKMgk6jf51H/rYdAezbJy1mGz12JtQlx1iuiCSe3CI47wn/D+Fbf/JNi8+4G/ruEXZlIjvOOfjgQXjhZ7B1o2+Ge+wVkJkbdmW9mpmRm5lGbmYawwqi9+5zzrGloYWasgEMfvw0nh3zAi+X/LTrCGBtIx99uoX1tU20RP5zMCwjLSWY/s2kMBj96/jvjq+FeZnkZMYuemmkTkR6v4WPw6yL/Z/3PMgvKi8e678WjoH0PuHWJ7I91eX+WLwVr8HgQ2DqH/zPrIRn9s/hzf+Fsx+BUZP+49uRiGPT1uaO0b7tTPv6+5pYX9fI9iJWVnpql6nfbUf9Om8C6ZOequnXaBTqRBJU1RJ4+3ao+BAqF0Jznb/fUqFwdEfIGzTWNzPOKQi3XklezQ0w9yaYeyOkZcHEq/xxeGrTE77mBph5nB81nfEWZOfv8qVaI44NdU3tgS/aVPDG+ubtXiMvM40F15ykUPdZFOpEkkAkAhtXQMUHULEgCHoLYMvajsfkDuoU8vaH4v1h4MheccC39GLL5/jWPBuWwdivwon/D/KKw65KOvv0ffjL8bDvNPjqnXF5yebWCOtrm7Y78vfLU8Yq1H0WhTqRJFa/wQe8tpBX8SFULYZI0CYhLQuK9+sa9Ir30/om2X21VTD7Z3793IDh/jzjkglhVyWfZc5v4f9+DWf8DfY7NdRSNP0ahUKdiHTR0uinbjsHvYoPoWFT8ACD/OEdIa9tdK/vYIj3sUbS+0Qi8N7dfkNPUz0c9QM4+rIe2eRWOmltgTsnwcaVMGNeqKOpCnVRKNSJyOdyDjav6RryKj70U7ptsgYE6/QO6Ah6BaPVVkU6VC70U62r34a9j4IpN0FhadhVyY6qKoPbj4YRx8HXHwjtQ5xamoiI7A4z6D/U30af3HF/Y43/h7rzFO78O6ElONYoJd3vtu2yVm/sbi22ll6oqQ7mXA9v3QKZfWHabXDg1zWy29sUlsKEq+CFn8C/74MvnBN2RZ9LI3UiIrsj0grrl/lNGe0jewugtqLjMX2HdA15g/b366q02zHxlL0Az1wOmz/2IWDSrxTqe7NIBO6e6jdPzHgT+u8V9xI0/RqFQp2IxEVtFVR+2BHyKj6E6jJwrf77Gbl+E0Z7q5X9oWhfyMgOt27ZNVs+ged+DIue9NPwU/8Aex8ZdlXSHTauhNvGw55fgPOejPuHMYW6KBTqRCQ0zQ3+TM+KBV2ncBu3+O9bCuSP7Ah5bbfcYk3d9VSRVnhnJrxyrd9FfeyP4IhLtLYy0fzzbnjqe3DS9XD4d+L60lpTJyLSE6X38Z/29/xCx33OwaZVXfvprZ0PCx/reEx2QdfGycVjoWAUpKbH/z1Ih7XvwdOX+qm5kokw+Qa/U1oSzxfPg8XP+F3MJRP8378eSCN1IiI90dZNflNG5YKOJsrrFkFro/9+aiYUBZsy9jgISk8MZb1PUmrY4kfm3v0L5BTCSdf5XmYaTU1sNRVw6+GQPwK+NRtS4zMupunXKBTqRKTXam2B9eVd26xUfAj11f77gw6AMVNgzJf9ej2FjO7lHHz0BDx/hf8HftwFMOHn0Kdf2JVJvCx4FB75Fhx/JRzzP3F5SYW67TCzqcDUkpKSC8vLy8MuR0Skezjnd98uecZPD61+B3AwYFhHwBt6mI4/210bV8Gzl0P5bD86OuVmGHJw2FVJGB4+HxY9BRe+AnscEPOXU6iLQiN1IpLQaiqh7Dkf8Ja/Cq1NkD3Q99sbM8U3UtVpBjuutRne+hO8er3fyHL8lXDo9LhNvUkPVL/BT8NmD4Tpr0JaZkxfTqEuCoU6EUkajTWw9CUf8MpmQ+NmSM/2C73HTIFRJ6iHWjQfz4OnLvU7lsdMgZOvh35Dwq5KeoKyF+AfX/PHvk28OqYvpd2vIiICmXl+Af9+p0JLE6ya6wPe4mf89JGlwrDxPrCMnuxP0BA/EvPSVfDe36HfUDjrfhgzOeyqpCcpPRG+cC68cTOUngx7HRZ2RYBG6kREkk8kAp/8CxY/7QNe9RJ//x4HdqzDK9o3+TZaOAcfPAgv/Ay2boQjZsCxV0BmbtiVSU/UsMU3JU5Ng+/MhYycmLyMpl+jUKgTEdlGdXnHCN6ad0nKjRbV5fDMZbDiNRgyDqbc5DdEiESz4nW4ewqMuxC+fENMXkKhLgqFOhGRKGoqYEmw0WLFnGCjRQGMPikxN1o0N8Dcm2DujZCWBROvgoPP17m8suOe/wnMuxXOnQUjv9Ttl1eoi0KhTkRkBzVs6dhoUT7bH2eWntOx0aL0BMgaEHaVu275HHj6B7BhGex/Bpzwa8grDrsq6W2at8Ltx0BTHVz0JmT179bLK9RFoVAnIrILWppg5esd07S1FcFGi6OCadrJvWdnaG0VzP6ZXz+XPwK+/HsYeXzYVUlvtvafcMckOOBrcOqfu/XSCnVRKNSJiOymz9xocVCnjRb79LyNFpEIvHe339naVO/bURx9WWJNJ0t4Xvk1vPZbOPM+2GdKt11WoS4KhToRkW7WZaPFO/6+AcN9uBszBYYeGv5Gi8qFfqp19duw91F+I0Rhabg1SWJpaYI7JsCWT2DGPMgt7JbLKtRFoVAnIhJDn7nRovOJFn3iV09THcy5Ht66xZ/ResK1cODXe94ooiSGyo9g5rG+j93X7umWnzOFuigU6kRE4qRhCyx9seNEi6aa+G60KHsBnrkcNn8MXzgHJv1KJ2hI7L1xM7z4Czh1Jhx45m5fTqEuCoU6EZEQtDR22mjxrN9okZIGe4/v/o0WWz6B534Mi56EwjF+qnXvI7vn2iKfJ9IKf50M6xbBjLeg3+DdupxCXRQKdSIiIYtE4JP3Om20KPP37+5Gi0grvDMTXrkWIi1w7I/giEsgLaP734NINOuXwZ+Pgr0Oh3Me261pWIW6KBTqRER6mKoyWNL5RAt2fqPF2vfg6Uvh0/ehZCJMvgHyh8e+dpHP8u4d8MwPfcuccRfs8mUU6qJQqBMR6cFqKmDJsz7gLZ8DkeboGy0atviRuXf/AjmFcNJ1sN+p2ggh4XMO7j0NPp7nz4YdOHKXLqNQF4VCnYhIL/FZGy1GTfQBz1Jg9pU+CB56IRx/pd/hKtJTbF4Ltx7hlxOc/+wutfbZmVCXttNXFxERiYc+fWHs6f627UaLj57wjxl0AJx1Hww+ONxaRban32CY/Dt4fDq8+Uc46tKYvpxG6kREpHdp22ix5RMYPRlSNT4hPZhz8NC5vsXO9FeheL+devrOjNSl7EJ5IiIi4UlJgSGHwL5fUaCTns8MpvzBLw14/Dv+5IkYUagTERERiaWcAph6M1R8AK/9LmYvo1AnIiIiEmtjvgwHfgNe/z2s/WdMXkKhTkRERCQeTvoN5A3y07DNW7v98gp1IiIiIvGQ1R9OucWfovLyNd1+eYU6ERERkXgZ+SUYdyHMuxVWvN6tl1aoExEREYmnSb+E/BEwa4Zvst1NFOpERERE4ikjB069HbasgRd+2m2XVagTERERibehh8L478O/7vGNibuBQp2IiIhIGI77CRSPhScvgfoNu305hToRERGRMKRlwql/9oHumR/u9uUU6kRERETCMmh/OO4KWPgYLHh0ty6lUCciIiISpvGXwuBD/GhdTcUuX0ahTkRERCRMqWl+Gra5wa+vc26XLqNQJyIiIhK2glEw8Woonw3v/X2XLqFQJyIiItITHDodhh3te9dtXLnTT1eoExEREekJUlJg2q2AwayLIRLZuafHpioRERER2Wn994KTr4NVc+HtP+/UUxXqRERERHqSg86G0pPh5V/u1NMU6kRERER6EjOYejOkZ+/U0xTqRERERHqavGKYctNOPSVpQp2ZTTWzmZs3bw67FBEREZHPt9+0nXp40oQ659xTzrnp/fr1C7sUERERkW6XNKFOREREJJEp1ImIiIgkAIU6ERERkQSgUCciIiKSABTqRERERBKAQp2IiIhIAlCoExEREUkACnUiIiIiCUChTkRERCQBKNSJiIiIJACFOhEREZEEoFAnIiIikgAU6kREREQSgEKdiIiISAJQqBMRERFJAAp1IiIiIglAoU5EREQkASjUiYiIiCQAhToRERGRBKBQJyIiIpIAFOpEREREEoBCnYiIiEgCMOdc2DXElZnVAEvCriMEBUB12EWEQO87ueh9Jxe97+SSrO97tHMub0cemBbrSnqgJc65Q8IuIt7MbL7ed/LQ+04uet/JRe87uZjZ/B19rKZfRURERBKAQp2IiIhIAkjGUDcz7AJCovedXPS+k4ved3LR+04uO/y+k26jhIiIiEgiSsaROhEREZGEo1AnIiIikgCSJtSZWb6ZPW5mdWa2ysy+EXZN8WBm3zWz+WbWaGZ/C7ueeDCzTDO7M/j/XGNm/zazk8OuKx7M7F4z+9TMtphZmZldEHZN8WRmo8yswczuDbuWeDCzV4P3WxvckqYHp5mdZWaLgt/py8zs6LBriqVO/4/bbq1m9sew64oHMxtmZs+a2UYzqzCzP5lZwrdkM7N9zOwVM9tsZkvN7NTPe07ShDrgFqAJKAbOBm4zs/3CLSkuPgGuBe4Ku5A4SgNWA8cC/YArgYfMbFiINcXLb4Bhzrm+wFeAa83s4JBriqdbgHfDLiLOvuucyw1uo8MuJh7MbBJwPXA+kAccAywPtagY6/T/OBcYBGwFHg65rHi5FVgH7AEchP/dPiPUimIsCK1PAE8D+cB04F4zK432vKQIdWaWA5wO/Nw5V+ucmws8CZwbbmWx55x7zDk3C1gfdi3x4pyrc85d7Zxb6ZyLOOeeBlYACR9unHMLnXONbf8Z3EaGWFLcmNlZwCbg5bBrkZj7JXCNc25e8Hd8rXNubdhFxdHp+JDzetiFxMlw4CHnXINzrgJ4Hkj0QZkxwJ7ATc65VufcK8AbfE5uSYpQB5QCLc65sk73vU/i/1AIYGbF+J+BhWHXEg9mdquZ1QOLgU+BZ0MuKebMrC9wDXBZ2LWE4DdmVm1mb5jZcWEXE2tmlgocAhQGU1Jrgum4rLBri6NvAn93ydO+4g/AWWaWbWaDgZPxwS7ZGDA22gOSJdTlAlu2uW8zftheEpiZpQP3AXc75xaHXU88OOdm4H+2jwYeAxqjPyMh/Aq40zm3JuxC4uzHwAhgML6X1VNmlugjs8VAOvBV/M/4QcAX8MssEp6Z7Y2ffrw77Fri6DX8IMwWYA0wH5gVakWxtwQ/Gvs/ZpZuZifg/79nR3tSsoS6WqDvNvf1BWpCqEXixMxSgHvwaym/G3I5cRUM188FhgAXhV1PLJnZQcBE4Kawa4k359zbzrka51yjc+5u/PTM5LDrirGtwdc/Ouc+dc5VAzeS+O+7zbnAXOfcirALiYfg9/jz+A+oOUABMAC/pjJhOeeagWnAl4EK4IfAQ/hQ+5mSJdSVAWlmNqrTfQeSJNNxycjMDLgT/6n+9OAvSDJKI/HX1B0HDAM+NrMK4HLgdDN7L8yiQuLwUzQJyzm3Ef8PW+epx2SZhgQ4j+QapcsH9gL+FHx4WQ/8lSQI8c65D5xzxzrnBjrnTsSPyr8T7TlJEeqcc3X4lH+NmeWY2XjgFPwoTkIzszQz6wOkAqlm1icZtoIDtwH7AFOdc1s/78GJwMyKgjYPuWaWamYnAl8n8TcOzMQH14OC25+BZ4ATwywq1sysv5md2PZ32szOxu8CTYa1Rn8FLgl+5gcAP8DvEkxoZnYkfqo9WXa9EozErgAuCn7O++PXFH4QbmWxZ2YHBH+/s83scvzu379Fe05ShLrADCALP0d9P3CRcy4ZRuquxE9XXAGcE/w5odeeBGtOvo3/B76iU1+ns0MuLdYcfqp1DbARuAG41Dn3ZKhVxZhzrt45V9F2wy+3aHDOVYVdW4yl49sVVQHVwCXAtG02hCWqX+Fb15QBi4B/Ab8OtaL4+CbwmHMu2ZYOnQachP9ZXwo044N8ojsXv9ltHTABFx7GYAAAAqZJREFUmNSpu8F26exXERERkQSQTCN1IiIiIglLoU5EREQkASjUiYiIiCQAhToRERGRBKBQJyIiIpIAFOpEREREEoBCnYhInJnZMDNzSdIIXETiRKFOREREJAEo1ImIiIgkAIU6ERHAzPY0s0fNrMrMVpjZ94L7rzazR8zsQTOrMbP3zOzATs/bx8xeNbNNZrbQzL7S6XtZZvZ7M1tlZpvNbK6ZZXV62bPN7GMzqzazn8Xx7YpIAlKoE5GkZ2YpwFPA+/gD0ycAl5rZicFDTsEfop4P/AOYZWbpZpYePG82UIQ/f/U+MxsdPO8G4GDgyOC5PwIinV76KGB08Hq/MLN9YvYmRSTh6exXEUl6ZnYY8LBzbq9O9/0EKAVWASc55w4P7k8B1gJfCx76MLCncy4SfP9+YAlwDVAHHO6ce3+b1xsGrACGOufWBPe9A9zonHsgRm9TRBKcdl6JiMDewJ5mtqnTfanA6/hQt7rtTudcxMzWAHsGd61uC3SBVfjRvgKgD7AsyutWdPpzPZC7y+9ARJKepl9FRHxoW+Gc69/pluecmxx8f2jbA4ORuiHAJ8FtaHBfm73wI3nVQAMwMi7vQESSnkKdiAi8A9SY2Y+DzQ2pZjbWzMYF3z/YzE4L+spdCjQC84C38SNsPwrW2B0HTAUeCEbv7gJuDDZhpJrZEWaWGfd3JyJJQaFORJKec64VmAIchF/rVg3cAfQLHvIEcCawETgXOM051+yca8KHuJOD59wKnOecWxw873LgQ+BdYANwPfq9KyIxoo0SIiJRmNnVQIlz7pywaxERiUafGEVEREQSgEKdiIiISALQ9KuIiIhIAtBInYiIiEgCUKgTERERSQAKdSIiIiIJQKFOREREJAEo1ImIiIgkAIU6ERERkQTw/wGFw8sWyFttcAAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, "metadata": { @@ -1620,6 +1668,23 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the model with the smallest mape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "best_epoch = np.argmin(np.array(history.history['val_loss']))+1\n", + "model.load_weights(\"model_{:02d}.h5\".format(best_epoch))" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1630,13 +1695,26 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -1678,7 +1756,7 @@ "2014-11-01 04:00:00 2,419.00" ] }, - "execution_count": 37, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1698,13 +1776,26 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -1715,23 +1806,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", "
2014-11-01 00:00:000.16-1.39
2014-11-01 01:00:000.14-1.53
2014-11-01 02:00:000.13-1.61
2014-11-01 03:00:000.12-1.63
2014-11-01 04:00:000.14-1.56
\n", @@ -1739,14 +1830,14 @@ ], "text/plain": [ " load\n", - "2014-11-01 00:00:00 0.16\n", - "2014-11-01 01:00:00 0.14\n", - "2014-11-01 02:00:00 0.13\n", - "2014-11-01 03:00:00 0.12\n", - "2014-11-01 04:00:00 0.14" + "2014-11-01 00:00:00 -1.39\n", + "2014-11-01 01:00:00 -1.53\n", + "2014-11-01 02:00:00 -1.61\n", + "2014-11-01 03:00:00 -1.63\n", + "2014-11-01 04:00:00 -1.56" ] }, - "execution_count": 38, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1765,7 +1856,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1788,22 +1879,22 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.44],\n", - " [0.46],\n", - " [0.46],\n", + "array([[0.29],\n", + " [0.27],\n", + " [0.25],\n", " ...,\n", - " [0.51],\n", - " [0.45],\n", - " [0.41]], dtype=float32)" + " [0.56],\n", + " [0.3 ],\n", + " [0.16]], dtype=float32)" ] }, - "execution_count": 40, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1822,13 +1913,26 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -1844,35 +1948,35 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1881,14 +1985,14 @@ ], "text/plain": [ " timestamp h prediction actual\n", - "0 2014-11-01 09:00:00 t+1 3,411.42 3,436.00\n", - "1 2014-11-01 10:00:00 t+1 3,462.32 3,464.00\n", - "2 2014-11-01 11:00:00 t+1 3,468.06 3,439.00\n", - "3 2014-11-01 12:00:00 t+1 3,388.74 3,407.00\n", - "4 2014-11-01 13:00:00 t+1 3,386.48 3,389.00" + "0 2014-11-01 09:00:00 t+1 3,474.14 3,436.00\n", + "1 2014-11-01 10:00:00 t+1 3,464.54 3,464.00\n", + "2 2014-11-01 11:00:00 t+1 3,451.99 3,439.00\n", + "3 2014-11-01 12:00:00 t+1 3,392.20 3,407.00\n", + "4 2014-11-01 13:00:00 t+1 3,373.44 3,389.00" ] }, - "execution_count": 41, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1911,22 +2015,30 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def mape(predictions, actuals):\n", + " \"\"\"Mean absolute percentage error\"\"\"\n", + " return ((predictions - actuals).abs() / actuals).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "0.01260933761669605" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE: 1.13%\n" + ] } ], "source": [ - "mape(eval_df['prediction'], eval_df['actual'])" + "print(\"MAPE: {:.2f}%\".format(100* mape(eval_df['prediction'], eval_df['actual'])))" ] }, { @@ -1938,14 +2050,14 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, "metadata": { @@ -1955,7 +2067,8 @@ } ], "source": [ - "eval_df[eval_df.timestamp<'2014-11-08'].plot(x='timestamp', y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8))\n", + "eval_df[eval_df.timestamp<'2014-11-08'] \\\n", + " .plot(x='timestamp', y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8))\n", "plt.xlabel('timestamp', fontsize=12)\n", "plt.ylabel('load', fontsize=12)\n", "plt.show()" @@ -1970,20 +2083,29 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ + "from glob import glob\n", + "\n", "for m in glob('model_*.h5'):\n", " os.remove(m)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "dnntutorial", + "display_name": "Python 3.6", "language": "python", - "name": "dnntutorial" + "name": "dftf2" }, "language_info": { "codemirror_mode": { @@ -1995,7 +2117,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/2_RNN.ipynb b/2_RNN.ipynb index b3df85a..3f4acc6 100644 --- a/2_RNN.ipynb +++ b/2_RNN.ipynb @@ -18,13 +18,6 @@ "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Please run this notebook after completing 0_data_setup notebook." - ] - }, { "cell_type": "code", "execution_count": 1, @@ -32,28 +25,29 @@ "outputs": [], "source": [ "import os\n", - "import warnings\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", "import datetime as dt\n", - "from collections import UserDict\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "from IPython.display import Image\n", + "#from collections import UserDict\n", + "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", - "from common.utils import load_data, mape\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", "\n", + "import pandas as pd\n", "pd.options.display.float_format = '{:,.2f}'.format\n", - "np.set_printoptions(precision=2)\n", - "warnings.filterwarnings(\"ignore\")" + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import tensorflow as tf\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Load the data from csv into a Pandas dataframe" + "Load the data from csv into a Pandas dataframe. Make sure to first complete the [0_data_setup](0_data_setup.ipynb) notebook." ] }, { @@ -125,8 +119,8 @@ } ], "source": [ - "data_dir = 'data/'\n", - "energy = load_data(data_dir)[['load']]\n", + "file_name = os.path.join('data', 'energy.parquet')\n", + "energy = pd.read_parquet(file_name)[['load']]\n", "energy.head()" ] }, @@ -138,7 +132,10 @@ "\n", "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", "\n", - "We will allocate the period 1st November 2014 to 31st December 2014 to the test set. The period 1st September 2014 to 31st October is allocated to validation set. All other time periods are available for the training set." + "We will allocate data as follows:\n", + "* November 1, 2014 to December 31, 2014: **test** set. \n", + "* September 1, 2014 to October 31, 2014: **validation** set. \n", + "* Everything up to August 31, 2014: **training** set." ] }, { @@ -158,20 +155,24 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "energy[energy.index < valid_start_dt][['load']].rename(columns={'load':'train'}) \\\n", - " .join(energy[(energy.index >=valid_start_dt) & (energy.index < test_start_dt)][['load']] \\\n", + "energy[:valid_start_dt][['load']] \\\n", + " .rename(columns={'load':'train'}) \\\n", + " .join(energy[valid_start_dt:test_start_dt][['load']] \\\n", " .rename(columns={'load':'validation'}), how='outer') \\\n", - " .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n", + " .join(energy[test_start_dt:][['load']] \\\n", + " .rename(columns={'load':'test'}), how='outer') \\\n", " .plot(y=['train', 'validation', 'test'], figsize=(15, 8), fontsize=12)\n", "plt.xlabel('timestamp', fontsize=12)\n", "plt.ylabel('load', fontsize=12)\n", @@ -186,34 +187,15 @@ "\n", "For this example, we will set *T=6*. This means that the input for each sample is a vector of the prevous 6 hours of the energy load. The choice of *T=6* was arbitrary but should be selected through experimentation.\n", "\n", - "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)" + "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)\n", + "\n", + "![One Step Forecast](./images/one_step_forecast.png)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('./images/one_step_forecast.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, "outputs": [], "source": [ "T = 6\n", @@ -227,7 +209,7 @@ "Our data preparation for the training set will involve the following steps:\n", "\n", "1. Filter the original dataset to include only that time period reserved for the training set\n", - "2. Scale the time series such that the values fall within the interval (0, 1)\n", + "2. Scale the time series such that the values are centered with unit variance\n", "3. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n", "4. Discard any samples with missing values\n", "5. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras" @@ -242,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -273,64 +255,64 @@ "
\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
02014-11-01 09:00:00t+13,411.423,474.143,436.00
12014-11-01 10:00:00t+13,462.323,464.543,464.00
22014-11-01 11:00:00t+13,468.063,451.993,439.00
32014-11-01 12:00:00t+13,388.743,392.203,407.00
42014-11-01 13:00:00t+13,386.483,373.443,389.00
load_t-3load_t-2load_t-1load_tload_t-0
2012-01-01 05:00:000.150.180.220.180.140.130.130.15-1.50-1.31-1.07-1.32-1.52-1.59-1.59-1.50
2012-01-01 06:00:000.180.230.180.140.130.130.150.18-1.31-1.03-1.32-1.52-1.59-1.59-1.50-1.31
2012-01-01 07:00:000.230.290.140.130.130.150.180.23-1.03-0.69-1.52-1.59-1.59-1.50-1.31-1.03
2012-01-01 08:00:000.290.350.130.130.150.180.230.29-0.69-0.36-1.59-1.59-1.50-1.31-1.03-0.69
2012-01-01 09:00:000.350.370.130.150.180.230.290.35-0.36-0.24-1.59-1.50-1.31-1.03-0.69-0.36
\n", @@ -338,34 +320,36 @@ ], "text/plain": [ " load_original y_t+1 load_t-5 load_t-4 load_t-3 \\\n", - "2012-01-01 05:00:00 0.15 0.18 0.22 0.18 0.14 \n", - "2012-01-01 06:00:00 0.18 0.23 0.18 0.14 0.13 \n", - "2012-01-01 07:00:00 0.23 0.29 0.14 0.13 0.13 \n", - "2012-01-01 08:00:00 0.29 0.35 0.13 0.13 0.15 \n", - "2012-01-01 09:00:00 0.35 0.37 0.13 0.15 0.18 \n", + "2012-01-01 05:00:00 -1.50 -1.31 -1.07 -1.32 -1.52 \n", + "2012-01-01 06:00:00 -1.31 -1.03 -1.32 -1.52 -1.59 \n", + "2012-01-01 07:00:00 -1.03 -0.69 -1.52 -1.59 -1.59 \n", + "2012-01-01 08:00:00 -0.69 -0.36 -1.59 -1.59 -1.50 \n", + "2012-01-01 09:00:00 -0.36 -0.24 -1.59 -1.50 -1.31 \n", "\n", - " load_t-2 load_t-1 load_t \n", - "2012-01-01 05:00:00 0.13 0.13 0.15 \n", - "2012-01-01 06:00:00 0.13 0.15 0.18 \n", - "2012-01-01 07:00:00 0.15 0.18 0.23 \n", - "2012-01-01 08:00:00 0.18 0.23 0.29 \n", - "2012-01-01 09:00:00 0.23 0.29 0.35 " + " load_t-2 load_t-1 load_t-0 \n", + "2012-01-01 05:00:00 -1.59 -1.59 -1.50 \n", + "2012-01-01 06:00:00 -1.59 -1.50 -1.31 \n", + "2012-01-01 07:00:00 -1.50 -1.31 -1.03 \n", + "2012-01-01 08:00:00 -1.31 -1.03 -0.69 \n", + "2012-01-01 09:00:00 -1.03 -0.69 -0.36 " ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. Get the train data from the correct data range\n", - "train = energy.copy()[energy.index < valid_start_dt][['load']]\n", + "train = energy.copy()[:valid_start_dt][['load']]\n", "\n", - "# 2. Scale data to be in range (0, 1). \n", + "# 2. Scale data. \n", "# This transformation should be calibrated on the training set only. \n", "# This is to prevent information from the validation or test sets \n", "# leaking into the training data.\n", - "scaler = MinMaxScaler()\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "scaler = StandardScaler()\n", "train['load'] = scaler.fit_transform(train)\n", "\n", "# 3. Shift the dataframe to create the input samples.\n", @@ -374,12 +358,7 @@ "for t in range(1, T+1):\n", " train_shifted[str(T-t)] = train_shifted['load'].shift(T-t, freq='H')\n", "y_col = 'y_t+1'\n", - "X_cols = ['load_t-5',\n", - " 'load_t-4',\n", - " 'load_t-3',\n", - " 'load_t-2',\n", - " 'load_t-1',\n", - " 'load_t']\n", + "X_cols = [f'load_t-{T-t}' for t in range(1, T+1)]\n", "train_shifted.columns = ['load_original']+[y_col]+X_cols\n", "\n", "# 4.Discard any samples with missing values\n", @@ -391,12 +370,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now convert the target and input features into numpy arrays. X needs to be in the **shape (samples, time steps, features)**. Here we have 23370 samples, 6 time steps and 1 feature (load)." + "Now convert the target and input features into numpy arrays. X needs to be in the **shape (samples, time steps, features)**.
\n", + "Here we have 23,370 samples, 6 time steps and 1 feature (load).\n", + "\n", + "![RNN Data Prep](./images/rnn_data_prep.png)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -407,28 +389,7 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('./images/rnn_data_prep.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -445,16 +406,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(23370,)" + "(23371,)" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -472,16 +433,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.18, 0.23, 0.29])" + "array([-1.31, -1.03, -0.69])" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -499,16 +460,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(23370, 6, 1)" + "(23371, 6, 1)" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -526,35 +487,35 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[[0.22],\n", - " [0.18],\n", - " [0.14],\n", - " [0.13],\n", - " [0.13],\n", - " [0.15]],\n", + "array([[[-1.07],\n", + " [-1.32],\n", + " [-1.52],\n", + " [-1.59],\n", + " [-1.59],\n", + " [-1.5 ]],\n", "\n", - " [[0.18],\n", - " [0.14],\n", - " [0.13],\n", - " [0.13],\n", - " [0.15],\n", - " [0.18]],\n", + " [[-1.32],\n", + " [-1.52],\n", + " [-1.59],\n", + " [-1.59],\n", + " [-1.5 ],\n", + " [-1.31]],\n", "\n", - " [[0.14],\n", - " [0.13],\n", - " [0.13],\n", - " [0.15],\n", - " [0.18],\n", - " [0.23]]])" + " [[-1.52],\n", + " [-1.59],\n", + " [-1.59],\n", + " [-1.5 ],\n", + " [-1.31],\n", + " [-1.03]]])" ] }, - "execution_count": 14, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -567,12 +528,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can sense check this against the first 3 records of the original dataframe:" + "We can sanity check this against the first 3 records of the original dataframe:" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -603,42 +564,42 @@ " load_t-3\n", " load_t-2\n", " load_t-1\n", - " load_t\n", + " load_t-0\n", " \n", " \n", " \n", " \n", " 2012-01-01 05:00:00\n", - " 0.15\n", - " 0.18\n", - " 0.22\n", - " 0.18\n", - " 0.14\n", - " 0.13\n", - " 0.13\n", - " 0.15\n", + " -1.50\n", + " -1.31\n", + " -1.07\n", + " -1.32\n", + " -1.52\n", + " -1.59\n", + " -1.59\n", + " -1.50\n", " \n", " \n", " 2012-01-01 06:00:00\n", - " 0.18\n", - " 0.23\n", - " 0.18\n", - " 0.14\n", - " 0.13\n", - " 0.13\n", - " 0.15\n", - " 0.18\n", + " -1.31\n", + " -1.03\n", + " -1.32\n", + " -1.52\n", + " -1.59\n", + " -1.59\n", + " -1.50\n", + " -1.31\n", " \n", " \n", " 2012-01-01 07:00:00\n", - " 0.23\n", - " 0.29\n", - " 0.14\n", - " 0.13\n", - " 0.13\n", - " 0.15\n", - " 0.18\n", - " 0.23\n", + " -1.03\n", + " -0.69\n", + " -1.52\n", + " -1.59\n", + " -1.59\n", + " -1.50\n", + " -1.31\n", + " -1.03\n", " \n", " \n", "\n", @@ -646,17 +607,17 @@ ], "text/plain": [ " load_original y_t+1 load_t-5 load_t-4 load_t-3 \\\n", - "2012-01-01 05:00:00 0.15 0.18 0.22 0.18 0.14 \n", - "2012-01-01 06:00:00 0.18 0.23 0.18 0.14 0.13 \n", - "2012-01-01 07:00:00 0.23 0.29 0.14 0.13 0.13 \n", + "2012-01-01 05:00:00 -1.50 -1.31 -1.07 -1.32 -1.52 \n", + "2012-01-01 06:00:00 -1.31 -1.03 -1.32 -1.52 -1.59 \n", + "2012-01-01 07:00:00 -1.03 -0.69 -1.52 -1.59 -1.59 \n", "\n", - " load_t-2 load_t-1 load_t \n", - "2012-01-01 05:00:00 0.13 0.13 0.15 \n", - "2012-01-01 06:00:00 0.13 0.15 0.18 \n", - "2012-01-01 07:00:00 0.15 0.18 0.23 " + " load_t-2 load_t-1 load_t-0 \n", + "2012-01-01 05:00:00 -1.59 -1.59 -1.50 \n", + "2012-01-01 06:00:00 -1.59 -1.50 -1.31 \n", + "2012-01-01 07:00:00 -1.50 -1.31 -1.03 " ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -681,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -718,54 +679,54 @@ " \n", " \n", " 2014-09-01 00:00:00\n", - " 0.28\n", - " 0.24\n", - " 0.61\n", - " 0.58\n", - " 0.51\n", - " 0.43\n", - " 0.34\n", - " 0.28\n", + " -0.74\n", + " -0.95\n", + " 1.16\n", + " 0.98\n", + " 0.59\n", + " 0.10\n", + " -0.37\n", + " -0.74\n", " \n", " \n", " 2014-09-01 01:00:00\n", - " 0.24\n", - " 0.22\n", - " 0.58\n", - " 0.51\n", - " 0.43\n", - " 0.34\n", - " 0.28\n", - " 0.24\n", + " -0.95\n", + " -1.07\n", + " 0.98\n", + " 0.59\n", + " 0.10\n", + " -0.37\n", + " -0.74\n", + " -0.95\n", " \n", " \n", " 2014-09-01 02:00:00\n", - " 0.22\n", - " 0.22\n", - " 0.51\n", - " 0.43\n", - " 0.34\n", - " 0.28\n", - " 0.24\n", - " 0.22\n", + " -1.07\n", + " -1.10\n", + " 0.59\n", + " 0.10\n", + " -0.37\n", + " -0.74\n", + " -0.95\n", + " -1.07\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " load y+1 load_t-5 load_t-4 load_t-3 load_t-2 \\\n", - "2014-09-01 00:00:00 0.28 0.24 0.61 0.58 0.51 0.43 \n", - "2014-09-01 01:00:00 0.24 0.22 0.58 0.51 0.43 0.34 \n", - "2014-09-01 02:00:00 0.22 0.22 0.51 0.43 0.34 0.28 \n", + " load y+1 load_t-5 load_t-4 load_t-3 load_t-2 \\\n", + "2014-09-01 00:00:00 -0.74 -0.95 1.16 0.98 0.59 0.10 \n", + "2014-09-01 01:00:00 -0.95 -1.07 0.98 0.59 0.10 -0.37 \n", + "2014-09-01 02:00:00 -1.07 -1.10 0.59 0.10 -0.37 -0.74 \n", "\n", " load_t-1 load_t-0 \n", - "2014-09-01 00:00:00 0.34 0.28 \n", - "2014-09-01 01:00:00 0.28 0.24 \n", - "2014-09-01 02:00:00 0.24 0.22 " + "2014-09-01 00:00:00 -0.37 -0.74 \n", + "2014-09-01 01:00:00 -0.74 -0.95 \n", + "2014-09-01 02:00:00 -0.95 -1.07 " ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -773,7 +734,7 @@ "source": [ "# 1. Get the validation data from the correct data range\n", "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", - "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load']]\n", + "valid = energy.copy()[look_back_dt:test_start_dt][['load']]\n", "\n", "# 2. Scale the series using the transformer fitted on the training set:\n", "valid['load'] = scaler.transform(valid)\n", @@ -791,13 +752,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# 5.Transform this Pandas dataframe into a numpy array\n", "y_valid = valid_shifted['y+1'].as_matrix()\n", - "X_valid = valid_shifted[['load_t-'+str(T-t) for t in range(1, T+1)]].as_matrix()\n", + "X_valid = valid_shifted[[f'load_t-{T-t}' for t in range(1, T+1)]].as_matrix()\n", "X_valid = X_valid.reshape(X_valid.shape[0], T, 1)" ] }, @@ -810,7 +771,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": { "scrolled": true }, @@ -818,95 +779,16 @@ { "data": { "text/plain": [ - "(1463,)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_valid.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.24, 0.22, 0.22])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_valid[:3]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1463, 6, 1)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_valid.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[0.61],\n", - " [0.58],\n", - " [0.51],\n", - " [0.43],\n", - " [0.34],\n", - " [0.28]],\n", - "\n", - " [[0.58],\n", - " [0.51],\n", - " [0.43],\n", - " [0.34],\n", - " [0.28],\n", - " [0.24]],\n", - "\n", - " [[0.51],\n", - " [0.43],\n", - " [0.34],\n", - " [0.28],\n", - " [0.24],\n", - " [0.22]]])" + "((1464,), (1464, 6, 1))" ] }, - "execution_count": 21, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "X_valid[:3]" + "y_valid.shape, X_valid.shape" ] }, { @@ -920,33 +802,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will implement a simple RNN forecasting model with the following structure:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('./images/one_step_univariate.png')" + "We will implement a simple RNN forecasting model with the following structure:\n", + "\n", + "![One Step Univariate](./images/one_step_univariate.png)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -965,7 +828,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -976,19 +839,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From /anaconda/envs/py35/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n" - ] - } - ], + "outputs": [], "source": [ "model = Sequential()\n", "model.add(GRU(LATENT_DIM, input_shape=(T, 1)))\n", @@ -1004,7 +857,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1013,13 +866,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Model: \"sequential_1\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", @@ -1047,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1056,7 +910,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 23, "metadata": { "scrolled": false }, @@ -1065,30 +919,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From /anaconda/envs/py35/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.cast instead.\n", - "Train on 23370 samples, validate on 1463 samples\n", + "Train on 23371 samples, validate on 1464 samples\n", "Epoch 1/10\n", - "23370/23370 [==============================] - 25s 1ms/step - loss: 0.0263 - val_loss: 0.0019\n", + "23371/23371 [==============================] - 3s 131us/step - loss: 0.1237 - val_loss: 0.0309\n", "Epoch 2/10\n", - "23370/23370 [==============================] - 6s 252us/step - loss: 0.0013 - val_loss: 8.3454e-04\n", + "23371/23371 [==============================] - 2s 103us/step - loss: 0.0232 - val_loss: 0.0165\n", "Epoch 3/10\n", - "23370/23370 [==============================] - 6s 251us/step - loss: 7.6025e-04 - val_loss: 5.6926e-04\n", + "23371/23371 [==============================] - 3s 114us/step - loss: 0.0166 - val_loss: 0.0146\n", "Epoch 4/10\n", - "23370/23370 [==============================] - 6s 253us/step - loss: 6.0118e-04 - val_loss: 5.4199e-04\n", + "23371/23371 [==============================] - 3s 117us/step - loss: 0.0155 - val_loss: 0.0139\n", "Epoch 5/10\n", - "23370/23370 [==============================] - 6s 253us/step - loss: 5.6709e-04 - val_loss: 5.2899e-04\n", + "23371/23371 [==============================] - 3s 110us/step - loss: 0.0151 - val_loss: 0.0134\n", "Epoch 6/10\n", - "23370/23370 [==============================] - 6s 254us/step - loss: 5.5480e-04 - val_loss: 4.9941e-04\n", + "23371/23371 [==============================] - 3s 110us/step - loss: 0.0147 - val_loss: 0.0133\n", "Epoch 7/10\n", - "23370/23370 [==============================] - 6s 253us/step - loss: 5.4845e-04 - val_loss: 6.2813e-04\n", + "23371/23371 [==============================] - 3s 112us/step - loss: 0.0145 - val_loss: 0.0127\n", "Epoch 8/10\n", - "23370/23370 [==============================] - 6s 252us/step - loss: 5.4434e-04 - val_loss: 5.2482e-04\n", + "23371/23371 [==============================] - 3s 112us/step - loss: 0.0143 - val_loss: 0.0123\n", "Epoch 9/10\n", - "23370/23370 [==============================] - 6s 253us/step - loss: 5.3995e-04 - val_loss: 5.1850e-04\n", + "23371/23371 [==============================] - 3s 113us/step - loss: 0.0141 - val_loss: 0.0121\n", "Epoch 10/10\n", - "23370/23370 [==============================] - 6s 253us/step - loss: 5.3340e-04 - val_loss: 4.7774e-04\n" + "23371/23371 [==============================] - 3s 116us/step - loss: 0.0139 - val_loss: 0.0127\n" ] } ], @@ -1104,19 +955,21 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnEAAAJTCAYAAABn6UAFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeZxcdZ3v//e3ll6qqtPppSorZKmCJGQnC0tcEJBRGHQcf4AjoIgmKjjo/O5w9efDx/x07tyZ8d4Z56cjCkEdFRFExIUBr6MILoCEJJCQQEjS2dfe0p3eu7rq/P441d3VSXfSnVTVOafq9Xw86lHVp6r6fJpx8O3nez7fYyzLEgAAALzF53QBAAAAmDhCHAAAgAcR4gAAADyIEAcAAOBBhDgAAAAPIsQBAAB4UMDpAgqtvr7emj17ttNlAAAAnNWmTZuaLcuKjvZeyYW42bNna+PGjU6XAQAAcFbGmP1jvcdyKgAAgAcR4gAAADyIEAcAAOBBJXdNHAAAyI1kMqlDhw6pt7fX6VI8r6KiQjNnzlQwGBz3dwhxAADgnBw6dEhVVVWaPXu2jDFOl+NZlmWppaVFhw4d0pw5c8b9PZZTAQDAOent7VVdXR0B7jwZY1RXVzfhjiYhDgAAnDMCXG6cyz9HQhwAAIAHEeIAAIBntbW16Rvf+MaEv3f99derra1twt+744479Pjjj0/4e/lAiAMAAJ41VogbGBg44/eefvppTZ48OV9lFQQhDgAAeNbnPvc5NTQ0aNmyZVq1apXe+ta36j3veY8uueQSSdJf/MVfaMWKFVq4cKHWr18/9L3Zs2erublZ+/bt04IFC7R27VotXLhQ1113nXp6esZ17meeeUbLly/X4sWLdeedd6qvr2+opksuuURLlizR3/7t30qSfvzjH2vRokVaunSp3va2t+Xkb2eLEQAAcN6+9OR2vX7kZE5/5yXTJ+n/vXHhGT/zz//8z9q2bZteffVVPffcc7rhhhu0bdu2oa06vvOd76i2tlY9PT1atWqV3v/+96uurm7E79i1a5ceeeQRPfjgg7r55pv1k5/8RLfddtsZz9vb26s77rhDzzzzjC6++GJ96EMf0je/+U3dfvvt+ulPf6odO3bIGDO0ZPv3f//3+tWvfqUZM2ac0zLuaOjEAQCAorF69eoRe6197Wtf09KlS3X55Zfr4MGD2rVr12nfmTNnjpYtWyZJWrFihfbt23fW87z55puaM2eOLr74YknShz/8Yf3+979XdXW1Kioq9NGPflRPPPGEQqGQJGnNmjW644479OCDDyqVSuXgL6UTBwAAcuBsHbNCCYfDQ6+fe+45/eY3v9GLL76oUCikq666atS92MrLy4de+/3+cS+njiYQCGjDhg165pln9Pjjj+vrX/+6fvvb3+r+++/XSy+9pKeeekorVqzQpk2bTusITvhc5/VtAAAAB1VVVamjo2PU99rb21VTU6NQKKQdO3boT3/6U87OO2/ePO3bt0+7d+9WIpHQQw89pLe//e3q7OxUd3e3rr/+eq1Zs0Zz586VJDU0NOiyyy7TZZddpl/+8pc6ePAgIQ4AAJSuuro6rVmzRosWLVJlZaWmTJky9N673vUu3X///VqwYIHmzZunyy+/PGfnraio0H/8x3/opptu0sDAgFatWqVPfOITam1t1Xvf+1719vbKsix95StfkSTde++92rVrlyzL0jXXXKOlS5eedw3Gsqzz/iVesnLlSmvjxo1OlwEAgOe98cYbWrBggdNlFI3R/nkaYzZZlrVytM8z2AAAAOBBLKcCAACc4u6779bzzz8/4tinP/1pfeQjH3GootMR4gAAAE5x3333OV3CWbGcCgAA4EGEOAAAAA8ixOXYpv0n9I5/eU7bDrc7XQoAAChihLgcq64MaG9zl3Y1jr7xIAAAQC4Q4nJsVl1YAZ/R7sZOp0sBAACniEQiY763b98+LVq0qIDVnB9CXI4F/T5dWBdSQ2OX06UAAIAiRojLg0Q0ot1NdOIAAMi3z33ucyO2A/niF7+of/iHf9A111yjSy+9VIsXL9bPf/7zCf/e3t5efeQjH9HixYu1fPlyPfvss5Kk7du3a/Xq1Vq2bJmWLFmiXbt2qaurSzfccIOWLl2qRYsW6Uc/+lHO/r4zYZ+4PIjHInr2zUYlU2kF/eRkAEAJ+OXnpGOv5fZ3Tl0svfufz/iRW265RZ/5zGd09913S5Iee+wx/epXv9I999yjSZMmqbm5WZdffrne8573yBgz7lPfd999Msbotdde044dO3Tddddp586duv/++/XpT39at956q/r7+5VKpfT0009r+vTpeuqppyRJ7e2FGW4kYeRBIhpRMmXpQGu306UAAFDUli9frsbGRh05ckRbtmxRTU2Npk6dqs9//vNasmSJrr32Wh0+fFjHjx+f0O/94x//qNtuu02SNH/+fM2aNUs7d+7UFVdcoX/8x3/Ul7/8Ze3fv1+VlZVavHixfv3rX+uzn/2s/vCHP6i6ujoff+pp6MTlQTxmXzTZ0NipeHTsCygBACgaZ+mY5dNNN92kxx9/XMeOHdMtt9yihx9+WE1NTdq0aZOCwaBmz56t3t7enJzrgx/8oC677DI99dRTuv766/XAAw/o6quv1ubNm/X000/rC1/4gq655hr93d/9XU7OdyZ04vIgHg1LEtfFAQBQALfccoseffRRPf7447rpppvU3t6uWCymYDCoZ599Vvv375/w73zrW9+qhx9+WJK0c+dOHThwQPPmzdOePXs0d+5c3XPPPXrve9+rrVu36siRIwqFQrrtttt07733avPmzbn+E0dFJy4PqiqCmjKpnAlVAAAKYOHChero6NCMGTM0bdo03Xrrrbrxxhu1ePFirVy5UvPnz5/w77zrrrv0yU9+UosXL1YgENB3v/tdlZeX67HHHtNDDz2kYDA4tGz78ssv695775XP51MwGNQ3v/nNPPyVpzOWZRXkRG6xcuVKa+PGjXk/z63f+pM6+1L6+d1r8n4uAACc8MYbb2jBggVOl1E0RvvnaYzZZFnWytE+z3JqnsSjEe1p7FSphWQAAFAYLKfmSSIWUUffgBo7+jRlUoXT5QAAgIzXXntNt99++4hj5eXleumllxyq6NwQ4vJkcCq1obGTEAcAgIssXrxYr776qtNlnDeWU/MkkdlmhAlVAEAx47Kh3DiXf46EuDyJVZUrUh5QQyMhDgBQnCoqKtTS0kKQO0+WZamlpUUVFRNbuWM5NU+MMYrHuIcqAKB4zZw5U4cOHVJTU5PTpXheRUWFZs6cOaHvEOLyKB4N64XdLU6XAQBAXgSDQc2ZM8fpMkoWy6l5lIhFdOxkrzp6k06XAgAAigwhLo8GJ1T3NHHnBgAAkFuEuDwamlBluAEAAOQYIS6PLqwNKeAzamC4AQAA5BghLo+Cfp9m14fpxAEAgJwjxOVZPBqmEwcAAHKOEJdniVhE+1u6lUylnS4FAAAUEUJcnsWjEQ2kLe1v6Xa6FAAAUEQIcXnGhCoAAMgHQlyezc3sFcd1cQAAIJcIcXkWKQ9oWnWFGujEAQCAHCLEFUA8GqETBwAAcooQVwCJWEQNTV2yLMvpUgAAQJEgxBVAPBpWZ9+Ajp/sc7oUAABQJAhxBRBnQhUAAOQYIa4AEkyoAgCAHCPEFUC0qlxVFQFCHAAAyBlCXAEYYxSPRlhOBQAAOUOIKxC2GQEAALlEiCuQRCyi4yf7dLI36XQpAACgCBDiCiQeDUuS9jR1OVwJAAAoBoS4AkmwzQgAAMghT4c4Y0y1MWaDMabTGLPI6XrO5MLakIJ+w3VxAAAgJzwd4iR1S7pB0uNOF3I2Ab9Ps+vCdOIAAEBOeDrEWZaVtCyryek6xosJVQAAkCsFC3HGmE8ZYzYaY/qMMd895b1aY8xPjTFdxpj9xpgPFqquQkrEItrf0q3+gbTTpQAAAI8LFPBcRyT9g6Q/k1R5ynv3SeqXNEXSMklPGWO2WJa13RgzVdKjo/y+D1iWdSyfBedaPBZWKm3pQGuXErEqp8sBAAAeVrAQZ1nWE5JkjFkpaebgcWNMWNL7JS2yLKtT0h+NMb+QdLukz2WC2lWFqjOfElE7uO1u7CTEAQCA8+KGa+IuljRgWdbOrGNbJC0cz5eNMU9Luk7Sg8aYO8b4zLrMUu7GpibnLqGbm9krroG94gAAwHkq5HLqWCKSTp5yrF3SuFpVlmVdP47PrJe0XpJWrlxpTbTAXAmXBzS9uoIJVQAAcN7c0InrlDTplGOTJHU4UEvexWNMqAIAgPPnhhC3U1LAGHNR1rGlkrY7VE9exaMRNTR2yrIcawgCAIAiUMgtRgLGmApJfkl+Y0yFMSZgWVaXpCck/b0xJmyMWSPpvZIeKlRthRSPRdTVn9Kxk71OlwIAADyskJ24L0jqkfQ5SbdlXn8h895dsrcdaZT0iKRPWpZVlJ24RJR7qAIAgPNXyC1Gvijpi2O81yrpLwpVi5PiscyEamOn3npR1OFqAACAV7nhmriSEo2Ua1JFQLsZbgAAAOeBEFdgxhh7QrWRveIAAMC5I8Q5IBGN0IkDAADnhRDngHgsoqaOPrX3JJ0uBQAAeBQhzgGDE6ps+gsAAM4VIc4B8VgmxLHNCAAAOEeEOAdcUFOpMr+P6+IAAMA5I8Q5IOD3aXZ9iAlVAABwzghxDknEIlwTBwAAzhkhziHxaEQHWrvVN5ByuhQAAOBBhDiHJGIRpdKW9rd0O10KAADwIEKcQ+JRJlQBAMC5I8Q5ZG40LEnaTYgDAADngBDnkFBZQDMmVzLcAAAAzgkhzkHxGPdQBQAA54YQ56B4NKyGxi6l05bTpQAAAI8pmRBnjLnRGLO+vb3d6VKGJGIR9SRTOnqy1+lSAACAx5RMiLMs60nLstZVV1c7XcoQJlQBAMC5KpkQ50aJmB3imFAFAAATRYhzUF24TNWVQSZUAQDAhBHiHGSMsYcbCHEAAGCCCHEOS8Qi2t3Y5XQZAADAYwhxDotHI2ru7FN7d9LpUgAAgIcQ4hw2NNzAkioAAJgAQpzDhrYZIcQBAIAJIMQ57ILakMr8PvaKAwAAE0KIc5jfZzSnnglVAAAwMYQ4F7AnVAlxAABg/AhxLhCPhnWgtVt9AymnSwEAAB5BiHOBeCyitCXta+52uhQAAOARhDgXYEIVAABMFCHOBQZDHNfFAQCA8SLEuUBlmV8zJlfSiQMAAONGiHMJJlQBAMBEEOJcIh6NaE9Tl9Jpy+lSAACABxDiXCIRi6gnmdKR9h6nSwEAAB5AiHOJeDQsSWpo6nK4EgAA4AWEOJdIxJhQBQAA40eIc4nacJkmh4JMqAIAgHEhxLmEMUaJKBOqAABgfAhxLmJPqBLiAADA2ZVMiDPG3GiMWd/e3u50KWNKxCJq7uxXW3e/06UAAACXK5kQZ1nWk5Zlrauurna6lDHFY4MTqnTjAADAmZVMiPOCRLRKEhOqAADg7AhxLjKjplJlAR97xQEAgLMixLmI32c0tz5MJw4AAJwVIc5l4rEI18QBAICzIsS5TCIa0cHWbvUmU06XAgAAXIwQ5zLxWERpS9rXwnVxAABgbIQ4l0lEuYcqAAA4O0Kcy8yNhmWM1NBIJw4AAIyNEOcyFUG/ZtZUajfDDQAA4AwIcS4Uj0bUwHIqAAA4A0KcCyWiEe1p7lQ6bTldCgAAcClCnAvFYxH1JtM63NbjdCkAAMClCHEulIhlJlS5Lg4AAIyBEOdC8cw2I1wXBwAAxkKIc6HacJlqw2VqaGKbEQAAMDpCnEvFo2E6cQAAYEyEOJeKRyNq4Jo4AAAwBkKcSyViEbV09etEV7/TpQAAABcixLnU0HAD3TgAADAKQpxLDW0zwnVxAABgFIQ4l5o+uVLlAR+dOAAAMCpCnEv5fUZzoxE6cQAAYFSEOBeLR8PsFQcAAEZFiHOxRCyigye61ZtMOV0KAABwGUKci8WjEVmWtLeZbhwAABiJEOdiTKgCAICxlEyIM8bcaIxZ397e7nQp4zanPixj2CsOAACcrmRCnGVZT1qWta66utrpUsatIujXBTUhOnEAAOA0JRPivIoJVQAAMBpCnMslYhHtaepUKm05XQoAAHARQpzLxaMR9Q2kdaStx+lSAACAixDiXI4JVQAAMBpCnMvFo3aIY0IVAABkI8S5XE24THXhMjpxAABgBEKcB8SjETpxAABgBEKcB8RjETpxAABgBEKcB8SjYZ3oTqq1q9/pUgAAgEsQ4jyACVUAAHAqQpwHMKEKAABORYjzgBmTK1UR9NGJAwAAQwhxHuDzGc2tZ0IVAAAMI8R5RIIJVQAAkIUQ5xHxaESH23rU059yuhQAAOAChDiPSMQisixpTzPdOAAAQIjzjHgsLElqaOpyuBIAAOAGhDiPmF0Xls+wVxwAALAR4jyiIujXBbUhJlQBAIAkQpynJKIRNdCJAwAAIsR5SjwW0Z7mLqXSltOlAAAAhxHiPCQRjah/IK1DJ7qdLgUAADiMEOchwxOqLKkCAFDqCHEeEo9GJDGhCgAACHGeMjlUpvpImRoa2SsOAIBSR4jzmLnRCMupAACAEOc1iVhEu5s6ZVlMqAIAUMoIcR4Tj0bU1p1Ua1e/06UAAAAHEeI8JhFjuAEAABDiPCceHdxmhOEGAABKGSHOY6ZXV6oy6KcTBwBAiSuZEGeMudEYs769vd3pUs6Lz2c0NxpmQhUAgBJXMiHOsqwnLctaV11d7XQp5y0Ri9CJAwCgxJVMiCsm8WhEh9t61NOfcroUAADgEEKcBw1OqLKkCgBA6SLEedDgPVQJcQAAlC5CnAfNrg/JZ6QGrosDAKBkEeI8qDzg14W1IfaKAwCghBHiPIoJVQAAShshzqPi0Yj2NncplbacLgUAADiAEOdR8VhE/am0DrZ2O10KAABwACHOo5hQBQCgtBHiPCqRCXFcFwcAQGkixHlUdSio+kg5nTgAAEoUIc7DErEwnTgAAEoUIc7D4tGIGpq6ZFlMqAIAUGoIcR6WiEXU3pNUc2e/06UAAIACI8R5GBOqAACULkKchyViTKgCAFCqCHEeNq26QqEyP504AABKECHOw4wxike5hyoAAKWIEOdx8WhYe5q6nC4DAAAUGCHO4xKxiA639airb8DpUgAAQAER4jxucEJ1bzPdOAAASgkhzuOYUAUAoDQR4jxuVl1Yfp9hQhUAgBJDiPO4soBPs2pDdOIAACgxhLgiMDcaoRMHAECJIcQVgUQsor3NXRpIpZ0uBQAAFAghrgjEo2ElU5YOnuhxuhQAAFAghLgiwIQqAAClhxBXBOZm9orjujgAAEoHIa4IVFcGFa0qpxMHAEAJIcQViQQTqgAAlBRCXJGIx8JqaOyUZVlOlwIAAAqAEFckEtGITvYOqKmzz+lSAABAARDiikQ8M6Ha0NjlcCUAAKAQCHFFYmibEa6LAwCgJBDiisTUSRUKl/nVwIQqAAAlgRBXJIwxiseYUAUAoFSUTIgzxtxojFnf3t7udCl5E49G6MQBAFAiSibEWZb1pGVZ66qrq50uJW8SsYiOtPeqq2/A6VIAAECelUyIKwXxaFiStKeJCVUAAIodIa6IDE+odjhcCQAAyDdCXBG5sDYsv8+wVxwAACWAEFdEygI+zaoLaTfDDQAAFD1CXJGJR9lmBACAUkCIKzKJWET7Wro0kEo7XQoAAMgjQlyRiUcjSqYsHWjtdroUAACQR4S4IjM0ocp1cQAAFDVCXJGZm9krroG94gAAKGqEuCIzqSKoKZPK6cQBAFDkCHFFiAlVAACKHyGuCCViETU0dsqyLKdLAQAAeUKIK0LxaEQdfQNq6uhzuhQAAJAnhLgixIQqAADFjxBXhOJRO8RxXRwAAMWLEFeEpkwqV6Q8QCcOAIAiRogrQsYYxaNh9ooDAKCIEeKKVDwWoRMHAEARI8QVqXg0omMne9XZN+B0KQAAIA8IcUVqcEK1gW4cAABFiRBXpJhQBQCguBHiitSsupACPsN1cQAAFClCXJEK+n2aVReiEwcAQJEixBWxBBOqAAAULUJcEYtHI9rf0q1kKu10KQAAIMcIcUUsEYtoIG1pf0u306UAAIAcI8QVMSZUAQAoXoS4IhbP7BXHdXEAABQfQlwRi5QHNHVSBZ04AACKECGuyMVjYTU0dTldBgAAyDFCXJFLRCNqaOyUZVlOlwIAAHKIEFfk4rGIOvsG1NjR53QpAAAghwhxRS4RZbgBAIBiRIgrcoMTqgw3AABQXAhxRS5WVa6q8gCdOAAAigwhrsgZYzQ3FqETBwBAkSHElYBENEInDgCAIkOIKwHxWFjHT/apozfpdCkAACBHCHElIDF0D1U2/QUAoFgQ4krA0IQqS6oAABQNQlwJuLA2pKDfaDfDDQAAFA1CXAkI+n2aVRemEwcAQBEhxJWIRDRCJw4AgCJCiCsR8VhYB1q6lUylnS4FAADkwLhDnDHmHcaYOZnX04wx3zPG/IcxZmr+ykOuJGIRDaQt7W9hQhUAgGIwkU7cNySlMq//VVJQUlrS+lwXlQ/GmBuNMevb29udLsUR8cw2I7sbCXEAABSDwAQ+O8OyrAPGmICkP5M0S1K/pCN5qSzHLMt6UtKTK1euXOt0LU6ID+0Vx3VxAAAUg4mEuJPGmCmSFkl63bKsTmNMmeyOHFwuXB7QtOoKJlQBACgSEwlx/y7pZUllkj6TObZG0o5cF4X8SMSYUAUAoFiM+5o4y7K+LOlaSWssy3o0c/iwpI/lozDkXjwaUUNjpyzLcroUAABwnia0xYhlWTsty2qQ7GlVSdMsy3otL5Uh5+KxiLr6Uzp2stfpUgAAwHmayBYjvzPGrMm8/qykRyX90Bjz+XwVh9yKR8OSpAYmVAEA8LyJdOIWSfpT5vVaSe+QdLmkT+S6KORHIja4zUiHw5UAAIDzNZHBBp8kyxgTl2Qsy3pdkowxNXmpDDkXjZSrqiKghiY6cQAAeN1EQtwfJX1d0jRJP5WkTKBrzkNdyANjjD2hyjYjAAB43kSWU++Q1CZpq6QvZo7Nl/TV3JaEfIpHI2z4CwBAERh3J86yrBZJnz/l2FM5rwh5lYhF9PimQzrZm9SkCvZpBgDAqyYynRo0xnzJGLPHGNObef5S5q4N8Iih22+xpAoAgKdNZDn1f8ne7PcTkpZmnq+W9OU81IU8GZ5QJcQBAOBlExlsuEnS0syyqiS9aYzZLGmLpL/JeWXIiwtqKlXm9zGhCgCAx02kE2cmeBwuFPD7NLs+RCcOAACPm0iI+7GkJ40xf2aMWWCMeZekn2WOw0Pi0Yj2MKEKAICnTSTE/XdJv5F0n6RNkv5d0rOS7s1DXcijRCyi/a3d6h9IO10KAAA4R2e8Js4Yc/Uph57LPIwkK3PsLZJ+m+vCkD/xaESptKX9LV26aEqV0+UAAIBzcLbBhm+PcXwwwA2Gubk5qwh5lz2hSogDAMCbzhjiLMuaU6hCUDhz6sOSxJ0bAADwsIlcE4ciES4PaHp1BROqAAB4GCGuRMVjEfaKAwDAwwhxJSoejaihqVOWZZ39wwAAwHUIcSUqEYuouz+lo+29TpcCAADOASGuRMWj9oQqww0AAHgTIa5EZW8zAgAAvIcQV6LqI2WaVBGgEwcAgEcR4kqUMUaJWIROHAAAHkWIK2H2hCrbjAAA4EWEuBKWiEXU1NGn9p6k06UAAIAJIsSVMCZUAQDwLkJcCWNCFQAA7yLElbCZNZUq8/voxAEA4EGEuBIW8Ps0pz6sBjpxAAB4DiGuxMVjYSZUAQDwIEJciUtEI9rf0qW+gZTTpQAAgAkgxJW4eCyitCXtb+l2uhQAADABhLgSN7jNCBOqAAB4CyGuxM2NhiWJ4QYAADyGEFfiQmUBzZhcqd1sMwIAgKcQ4qB4LMJecQAAeAwhDkpEI2po7FI6bTldCgAAGCdCHBSPhdWTTOnoyV6nSwEAAONEiIMSTKgCAOA5hDgoHrNDHBOqAAB4ByEOqguXaXIoyIQqAAAeQoiDjDGKRyN04gAA8BBCHCRlJlTpxAEA4BmEOEiyJ1SbO/vV1t3vdCkAAGAcCHGQJCUGhxvoxgEA4AmEOEiS4tHBCdUuhysBAADjQYiDJGlmTUhlAR8TqgAAeAQhDpIkv89obn2YCVUAADyCEJdrhzdJ979FOrHf6UomLB6L0IkDAMAjCHG5FpkqHX9d2vhtpyuZsHg0ooOt3epNppwuBQAAnAUhLteqZ0gL/lza/H0p2eN0NROSiEWUtqR9LQw3AADgdoS4fFi9Tuo5IW37idOVTEg8GpbEhCoAAF5AiMuHWWuk2CXSSw9IluV0NeM2tz4iY6TdDDcAAOB6hLh8MMbuxh3bKh3c4HQ141ZZ5teMyZVs+AsAgAcQ4vJlyc1SebW0Yb3TlUxIPBqhEwcAgAeUTIgzxtxojFnf3t5emBOWhaXlt0mv/0zqOFaYc+ZAIhbRnuZOpdPeWQYGAKAUlUyIsyzrScuy1lVXVxfupKs+KqVT0qbvFu6c5ykejag3mdaRdm9N1gIAUGpKJsQ5oi4uXfROaeN3pIF+p6sZl0TMvocqS6oAALgbIS7fVq+TOo9LO550upJxGdpmpIltRgAAcDNCXL7Fr5Fq50oveWPAoS5SrppQkE4cAAAuR4jLN59PWrVWOvgn6egWp6sZl3g0wjYjAAC4HCGuEJZ9UAqGpA0POl3JuCRiETXQiQMAwNUIcYVQOVlacov02o+l7lanqzmreDSilq5+nejyxjAGAACliBBXKKvXSgO90isPOV3JWQ1OqLKkCgCAexHiCmXKQmn2W6WXv2XvHedi8SghDgAAtyPEFdLqtVLbAWnXfzldyRnNqKlUecDHhCoAAC5GiCukeTdIk2ZILz3gdCVn5PcZzakPs1ccAAAuRogrJH9AWnmntOdZqWmn09WcUSIWoRMHAICLEeIK7dIPS/4y+9o4F4tHIzp4olu9SXdfvwcAQKkixBVaJCot/Evp1R9KfR1OVzOmRCwiy5L2NrOkCgCAGxHinLB6ndTfIW151OlKxsSEKgAA7kaIc8LMFdKMFdKG9ZJlOV3NqOZGwzJGXBcHAIBLEeKcsnqd1LxT2vs7pysZVUXQr5k1lQZYVrYAACAASURBVEyoAgDgUoQ4pyx8nxSql15a73QlY0pEmVAFAMCtCHFOCZRLK+6Qdv5SOrHf6WpGFY9GtKepU+m0O5d8AQAoZYQ4J638iCQjbfyO05WMKhGLqG8grcNtPU6XAgAATkGIc1L1TGn+DdLm70tJ9wWleMyeUN3NhCoAAK5DiHPa6nVST6u07SdOV3KaxOA2I1wXBwCA6xDinDb7LVLsEvt+qi7bbqQmXKbacBl7xQEA4EKEOKcZI61eKx3bKh162elqTsOEKgAA7kSIc4PFN0vl1XY3zmXisTB7xQEA4EKEODcoj0jLb5Ne/5nUcczpakaIRyNq7epXa1e/06UAAIAshDi3WPVRKT0gbfqe05WMMDihynVxAAC4CyHOLeriUuKd9p5xqaTT1QwZnFDlujgAANyFEOcmq9dJncekN37hdCVDZkyuVEXQxzYjAAC4DCHOTRLXSjVzpA0POl3JEJ/PaG59hA1/AQBwGUKcm/h89nYjB16Ujm51upoh8ViEa+IAAHAZQpzbLLtVCoakDeudrmRIIhrRoRM96k2mnC4FAABkEOLcpnKytOQW6bUfS92tTlcjyd4rzrKkPewXBwCAaxDi3Gj1WmmgV3rlB05XIklKZLYZ4bo4AADcgxDnRlMWSrPeIr38LSnt/BLm7LqwjBETqgAAuAghzq1Wr5Xa9ku7/svpSlQR9OuCmhCdOAAAXIQQ51bz/1yaNMM1Aw6JWIROHAAALkKIcyt/QFr5Eanht1LzLqerUTwa1t7mLqXSltOlAAAAEeLc7dI7JH+ZKzb/TcQi6htI6/CJHqdLAQAAIsS5WyQqLfxL6dUfSn0djpYSz9xDlU1/AQBwB0Kc261eJ/V3SFsedbSMwRC3m+viAABwBUKc281cIU2/1F5StZy7Hq0mXKa6cBmdOAAAXIIQ5wWr10nNb0p7f+doGfFYhE4cAAAuQYjzgoXvk0L1jg84xKMROnEAALgEIc4LghXSig9Lbz4ttR1wrIxELKIT3Um1dPY5VgMAALAR4rxi5Z3288vfdqyEeDQsSWpo6nKsBgAAYCPEeUX1TGn+DdLm70tJZ/ZqS8SYUAUAwC0IcV6y+uNST6u07QlHTj+9ulKVQT/XxQEA4AKEOC+Z/RYpukDa8IAj2434fEZzo2E6cQAAuAAhzkuMkVavlY5ukQ697EgJTKgCAOAOhDivWXKLVF4tbVjvyOkTsYgOt/Wopz/lyPkBAICNEOc15RFp+a3S9p9JHccLfvp4NCLLkvY0040DAMBJhDgvWvUxKZ2UNn234KdmQhUAAHcgxHlRXVxKXCtt/I6UShb01LPrQ/IZqYEQBwCAowhxXrX641LnMemNJwt62vKAXwunV+uRlw/q+Mnegp4bAAAMI8R5VeJaqWa2I/dT/Zeblqqrb0Cf+MEm9Q0w4AAAgBMIcV7l80mr1koHXpCOvVbQU8+bWqV/uWmpXjnQpi/+YntBzw0AAGyEOC9bfqsUDDmy3cj1i6fprqviemTDQT380v6Cnx8AgFJHiPOyyhppyc3S1h9L3a0FP/1/u26erpoX1Rd/sV0b9xX+/AAAlDJCnNetWisN9Eiv/KDgp/b7jL56y3JNn1ypTz68mUEHAAAKiBDndVMXSbPWSC9/S0oXfsigOhTU+ttXMugAAECBEeKKwep1Utt+adevHTn9vKlV+srN9qDD3/1suyzLcqQOAABKCSGuGMy/Qaqa7tj9VCXpXYum6VPvSOhHGw/qBy8dcKwOAABKBSGuGPiD0so7pYZnpOZdjpXxN++8WO+YF9WXfrFdLzPoAABAXhHiisWKOyR/mX1tnEP8PqP/7wPLdUFtSJ/8wWYdbe9xrBYAAIodIa5YRKLSwvdJr/5Q6utwrIzqyqDW375CPf0D+sQPNqs3yaADAAD5QIgrJqvXSX0npS2POlrGRVOq9K83L9OWg236u59vY9ABAIA8IMQVkxkrpOnL7fupOhyc3rVoqv766oQe23hIP/gTd3QAACDXCHHFxBhp9cel5jelvb93uhr9zbUX6+r5MX3pyde1YS+DDgAA5BIhrtgsfJ8UqnN0u5FBPp/Rv92yTBfWhnTXw5t0pI1BBwAAcoUQV2yCFdKlH5befFpqc36/turKoNZ/aIV6k2l98gebGHQAACBHCHHFaNVH7eeN33G2joxEzL6jw5ZD7frCzxh0AAAgFwhxxah6pn0Xh03fk5LuuCn9dQun6p5rLtLjmw7p+y8y6AAAwPkixBWr1euknlZp20+crmTIZ665SNcuiOl//OfremlPi9PlAADgaYS4YjX7rVJ0gbThAce3Gxnk8xl95ZZlurAupLse3sygAwAA54EQV6yMkVavlY5ukQ5tdLqaIZMqglp/+0r1DaT18YcYdAAA4FwR4orZkluk8kmu2G4kWyIW0b/dskyvHW7X53/6GoMOAACcA0JcMSuPSMtulbb/VOo47nQ1I7zzkin6zLUX6YnNh/XdF/Y5XQ4AAJ5DiCt2q9dK6aS0+XtOV3Kae66+SO+8ZIr+4ak39GIDgw4AAEwEIa7Y1cWlxLX2nnGppNPVjODzGX3l5qWaXRfS3T/crMMMOgAAMG6EuFKwep3UcVR640mnKzlNVUVQ6z+0UsmBtD7+0EYGHQAAGCdCXClIXCvVzJY2POh0JaOKR+1Bh22HT+rzTzDoAADAeBDiSoHPL61aKx14QTr2mtPVjOraS6bob669WE+8clj/8fw+p8sBAMD1CHGlYvmtUqDStd04SfrrqxO67pIp+p9Pv6EXGpqdLgcAAFcjxJWKyhppyc3S1sek7lanqxmVz2f0rzcv1Zz6sD71w1d06ES30yUBAOBaJRPijDE3GmPWt7e3O12Kc1avkwZ6pFcfdrqSMVVVBLX+9hVKpuw7OvT0M+gAAMBoSibEWZb1pGVZ66qrq50uxTlTF0mz1kgvf0tKuzcczY1G9NUPLNPrR0/q/3liK4MOAACMomRCHDJWr5VO7JN2/drpSs7o6vlT9H9fe7F+9uoRffuPe50uBwAA1yHElZr5fy5VTXfd/VRHc/c7EvqzhVP0T7/coRd2M+gAAEA2Qlyp8QellXdKDc9IzbudruaM7EGHZZpbH9bdP9ysg60MOgAAMIgQV4pWfFjyBe1r41wuUh7Q+g+t1EDaYtABAIAshLhSFIlJC99nT6n2dTpdzVnNqQ/rax9YrjeOndRnf8KgAwAAEiGudF32canvpLT1UacrGZd3zI/pb6+bp19sOaJv/YFBBwAACHGlasYKafpy+w4OHuls3XVVXO9eNFX/9Ms39MddDDoAAEobIa5UGWNv/tu0Q9r7e6erGRdjjP7lpqVKxCL61CMMOgAAShshrpQt/EspVOeJ7UYGhcsDWn/7SqXTltYx6AAAKGGEuFIWrJAu/bD05tNS20Gnqxm32fVhfe2vlmvHsZP67ww6AABKFCGu1K28037e+B1n65igq+bZgw5PbjmiB/+wx+lyAAAoOEJcqZt8gTTvemnz96Rkr9PVTMhdV8V1/eKp+udf7tAfdjU5XQ4AAAVFiIM94NDdIm1/wulKJsQYo//9fy3VRbEqfeqHr+hAC4MOAIDSQYiDNOdtUnS+9NIDntluZFC4PKD1H1ohSVr30EZ19w84XBEAAIVBiENmu5G10tFXpUMbna5mwmbV2YMOO4936N7HGXQAAJQGQhxsSz4glU/y1HYj2d5+cVT3/tl8PbX1qB74PYMOAIDiR4iDrTwiLbtV2v5TqbPR6WrOySfePlc3LJmm//V/duj3Oxl0AAAUN0Ichq36mJROSpu+53Ql58QedFiii6dU6a8feUX7W7qcLgkAgLwhxGFYfUKKXyNt/LaUSjpdzTkJldl3dJCkdd/fpK4+Bh0AAMWJEIeRVq+TOo5KO/7T6UrO2YV1If37Xy3XrsYO3fv4FgYdAABFiRCHkS56p1QzW9rwoNOVnJe3XRzVZ981X0+/dkzf/F2D0+UAAJBzhDiM5PPb18btf146ts3pas7LurfN1Y1Lp+t//+pNPfemN4c1AAAYCyEOp1t2qxSo9Ox2I4OMMfry+xdr3pQq3fPIK9rXzKADAKB4EOJwulCttORmaetjUs8Jp6s5L6GygB780Er5fEbrHtrIoAMAoGgQ4jC61WulgR7plYedruS8XVAb0tf/6lLtbuxk0AEAUDQIcRjd1MXShVdKLz8opVNOV3Pe3nJRvT73bnvQ4RvPMegAAPA+QhzGtnqtdGKftPs3TleSE2vfOlfvWTpd//Jfb+rZHQw6AAC8jRCHsS24Uaqa5vkBh0H2oMMSLZg6Sfc8+or2MugAAPAwQhzG5g9KK++0O3EtxbEEWVnm1wO3r1DAZ7Tu+xvVyaADAMCjCHE4s0s/LPmCnt/8N9sFtSF9/YOXqqGpU3/7GIMOAABvIsThzKqmSAvfJ736sNTX6XQ1ObMmUa/PX79A/2f7Md337G6nywEAYMIIcTi71eukvpPS1h85XUlOffQtc/QXy6brX3+9U7/dcdzpcgAAmBBCHM5u5kpp2jJ7SbWIlh6NMfqnv7QHHT796Kva01Q8nUYAQPEjxOHsjLG7cU1vSPv+4HQ1OTVi0OGhTeroTTpdEgAA40KIw/gser9UWVs0241ku6A2pPs+eKn2Nnfpvz22Rel08XQbAQDFixCH8QlWSCs+LO14Smo76HQ1OXdlZtDhv14/rq8z6AAA8ABCHMZv5Z3288bvOFtHnty5Zrbet3yG/u03O/Wb1xl0AAC4W8DpAuAhky+U5l0vbf6e9PbP2t25ImIPOizWrsYOfez7G7Vg2iStidfpykSdVs+pU6Sc/3cBALiHKbWNTleuXGlt3LjR6TK8a8/vpO+/R3rn/5Cu+JTkK75mbktnnx7ZcEDP727RpgMn1D+QVsBntPSCyVoTr9MV8XpdOmuyygN+p0sFABQ5Y8wmy7JWjvoeIQ4TYlnSt6+TDm2QovOlK/9aWnyTFCh3urK86E2mtGn/CT2/u1nPN7TotUNtSltSRdCnVbNrdWW8XlfG67RoRrX8PuN0uQCAIkOIy0KIy4FUUtr2hPTC16Tj26SqadJln5BWfkSqqHa6urxq70lqw95WPb+7WS80NGvncXtvuUkVAV0+t05rEnaoS8QiMoZQBwA4P4S4LIS4HLIsqeG30vNflfb+TiqrsidYL79Lqp7hdHUF0djRqxcbWvTC7hY939CsQyd6JEmxqnJdGa/TlZlQN7Mm5HClAAAvIsRlIcTlyZFXpRf+Xdr+U3tz4MU32UutUxY6XVlBHWjp1gsN9tLriw3Nau7slyTNqgvpyni91iTqdMXcOtVFinP5GQCQW4S4LIS4PDuxX/rTN6XN35eSXVLiWunKe6Q5b7PDXQmxLEtvHu/QC7tb9EJDs/60p1WdfQOSpPlTq7QmYYc6Jl8BAGMhxGUhxBVId6u08dvSSw9IXU3StKXSmk9LC94r+UszsAyk0tp6uF0vNrTo+d3N2rjfnnz1+4yWzqzOXE/H5CsAYBghLgshrsCSvdLWR+2l1pbd9l5zV3xKWn6bVBZ2ujpHDU6+vtDQrOd3t2hrZvK1PJCZfE3UaU28nslXAChhhLgshDiHpNPSzl9Kz39NOvgnqbJGWvUxafXHpUjU6epc4WRvUi/tadULDc16YXeL3jzeIUmqGpx8jdvTr0y+AkDpIMRlIcS5wIGX7O1Jdjwl+cukZR+0hyDq4k5X5ipNHX16oaHZXn5taNbBVnvyNZqZfF0Tr9eVCSZfAaCYEeKyEOJcpHmX9OLXpVcfkVL90vwb7OvmLljtdGWudLC1O7M/nT0oMXLytW5o42EmXwGgeBDishDiXKizUdqwXtrwoNTbJl1wubTmHunidxflbb1ywbIs7TzeORTqXtrToo6sydfB7UxWz6lVVUXQ4WoBAOeKEJeFEOdifZ3SKz+QXrxPaj8g1V0kXfkpackHpGCF09W52kAqrdcOtw916V7eN3Ly9crM0uulF9aoIsjkKwB4BSEuCyHOA1ID0us/s6+bO7pFCsekyz4urfqoPRCBs+pNprR5/wk932B36rYealcqbak84NPK2TW6Ml6veDSs6ZMrNX1yperCZQxLAIALEeKyEOI8xLKkvb+3b+vV8IwUDEuXfki64i57qxKM28nepDbsadXzmUGJHcc6RrxfFvBpenXFUKibPrlSMyZXaFr14M8VCpWV5v5+AOAkQlwWQpxHHdtm7zW37XE73C18n33d3LSlTlfmSe3dSR080a0jbT060tajo+29Opx5faStV8c7enXqvxpqQkFNn1ypadV2wMsOfNMnVyhWVcF+dgCQY4S4LIQ4j2s/ZN/Wa9P3pP4Oae5V9m294leX3G298imZSutYe6+OtvfqSFtPVsAbDnwdvQMjvhPwGU2ZVKEZmVA3LaujNxj2JjFkAQATQojLQogrEj1t0qbv2oGu85g0ZbG919yiv5T8BIVCONmb1NG24ZB3tN3u4g0GvmPtvRpIj/z3S1V5QNOyQt1Q4Ku2X0+ZVKGyABPJADCIEJeFEFdkBvqk135sL7U27ZAmzbSvmbv0Q1J5ldPVlbRU2lJzZ9+ILt5gyBsMfK1d/SO+Y4wUqyq3Q161HfCyA9+06grVMoQBoIQQ4rIQ4opUOi3t/rV9W6/9f5TKq6VVd0qXfUKqmup0dRhDT39KR9ozy7RZXbwjWV29/oH0iO+UB3yZDt7ILt7gz9MnV7KNCoCiQYjLQogrAYc2SS98VXrjSckXkJbcbF83F53ndGWYIMuy1NrVP2KZ9tRBjMaOvtO+VxsuU6yqXHWRMtWEylQXLlNtuFy1Eft1TahMdZEy1WZeM5ABwK0IcVkIcSWkdY+9cfArD0sDPfYdINbcI114BUMQRaRvIKXj7X1Zy7T2NXpNHX1q7epXa1e/Wrr6TxvEGGSMNLkyqNpwWdajPBP8Rj4Gg195gE4fgMIgxGUhxJWgrmb7ll4b1ks9rdKMlXaYm//nko//Mi4V/QNpnejuHxHsWjszQS9zvKVz+P0T3f1Kj/Gvx0h5QDXh4IiwVxcuU03Wa/vZ7v6Fy/xcxwfgnBDishDiSlh/t/Tqw9KLX5dO7JNq50pXfEpa9kEpWOl0dXCZdNpSe0/SDntd/Wrt6lNLV79ODAbAruHgd6LbPnbq9XuDygI+1YZGdvOGlnkjWcu9mfA3uTIoH0u8AESIG4EQB6VT9vVyz39VOrJZCtVLq9dJqz4mheucrg4eZVmWuvpTau3sV0tXnx3sOkd2/k4NgJ19oy/x+oxUExplOXfwdaRctaEyTaoMaFJFUFUVAVVVBNmeBShChLgshDgMsSxp//P2ROuuX0mBSmn5bdIVd0u1c5yuDiWgbyClE11JtXT1jdrZa+0cDIB9OtGd1Inu/tPupJGtMuhXVUVAkyqDmpQJdiNfD4c++7j93uDriqCPZV/AZQhxWQhxGFXjDnuvua0/kqyUlHinNHOVNHWx/Zg0nWEIOC6VttSWdV1fR++ATvYm7eee5PDr3qRO9gyoozepk1nvJVNn/vd9wGfGDn2ZQDj6a/vzVeUBloGBHCPEZSHE4YxOHpVeul96/efSib3Dx0N1w4Fu6hL7ue4iyc9N4eENlmWpbyCdCXSDQS85dhDMfK4jEwhP9ibV3Z864zmMsYc+Rnb7xg59I1+zJAyMhhCXhRCHcevrkI5vl469Jh3baj8ff11KZfYl85dLUy4ZGeymLOROEShayVRanaOEvpNjhL6Rr+33xpr4HVQR9I24zs9+DqiqPKhI5vVgUMz+OfuzlUGmgVE8CHFZCHE4L6kBqWWXdHTrcLA79pq9dcmg2rlZwS4T7qqmshyLkpdOW+rqHxhlyXf05V87+A2os89+r7N3QF1n6QZKkt9nFCkfDHeBoUAYKQ8MBb+q8uFjVRX28Umn/Mx+gHADQlwWQhxyzrKkk0eGA91guBuxHFs/ynJsguVYYIJSaUudfSODXUfvgDpO+bmzzw6KnacGwb4BnewdGHM7mGxlAV8m7GXCX1Y3cCgEjugGjgyGg5/njiA4H2cKcfw3CHC+jJGqZ9iPee8aPt57Mms5dov9/NL9Uipz0/dAhRS7ZDjcTVtq/1wecebvADzA7zOqrgyqujIo6dz3d+wbSKmrL6WO0Tp+fQNDx7J/7uwd0MHW7uGf+waUOtv6sKRQmX/UZd9wWUCVZX5VBv1Dz6EyvyoyPw++DpUFTnuvMugnHIJOHFBQqaTUvHNk1+7oVqm3LfMBI9XFT+/aRaawHAu4jGVZ6kmmMtcJ2qGuMxP8OrKC34hgmL003DegnmRK3f0p9Y2jM3iqsoBPocEQmBXusgNh5VAQHHwvkHn2qTIYGAqLI76feV0eYMsZN2A5NQshDq5jWdLJw6cHu7b9w58JR0dfjuW2YUBRSKct9Q7Yga6nP6We5OnP3UM/D6inPz38OvNebzLrc6P8joFxdA2z+YyGAt3IIJj9HFBlmU+hssDIz2Tej2Q6kCMeFQEF/UwhjxfLqYCbGSNVz7Qf8949fLynLWs5NhPuXvyGlE7a7wcq7WnY7HA35RKpLOzM3wHgnPl8RqGygEJl+fuv5WQqPRT2Rgt63f0DdhDsT6k7mVJvf3ZwzDxnvtvS1a/uE/bx3qyAOV7lAZ+9pDxKwDv153C5fQ3iiPcyr8Nlpb03IZ04wEsG+k9fjj22Veptz3zA2B26U7t2VVMcLRtA8bMsS73J9FAg7OlPDQ2hdGVdR9jZO6DO/szzKe91DQ2tDIx7iTmc6fhlh71w2eAwyulBcPi9oMLl/qHXbr1jCZ04oFgEyqSpi+yH/so+ZllS+8GsYPeadHijtP2J4e+FY9K0JcPhbspi+9Zi/qAjfwaA4mOMsZdYy/yqDZed9+9LptKnBbyOvrHDX/Z7LZ3dw6FxnAMovsxm1VUVmXBXHlAkcycS++dgpgM4/HrelCrNm+rc3qCEOMDrjJEmX2g/5t8wfLynTTq+bWTX7oXfDS/HGr/9ndq5pz9qZkmBcmf+HgCQFPT7NDlUpsmh8wuEg3cr6Thr+EtmJpaHX7f3JHX4RLe6+oa7itk+eVVcn33X/POq73wQ4oBiVTlZmv0W+zFooF9qflM6tk1qbZBa99iPrRulvvasLxup+gK7W3dawJstlYUK/dcAwDkxxqgiaA9nRKvO73+cDm5YbYe6pKoqnF3NIMQBpSRQNrykms2ypJ4Tw6Fu8NHSYN9HNvuOFJJUNT0T6k4NeXO47RiAouXzmcxef0FJFU6XQ4gDIHtJNlRrP2aOcv1szwmpdW8m3O0dDnk7fyV1NY78bDh2erCri0s1c+zuIAAgJwhxAM6uskaaUSPNuPT09/o6Rga7waC35zlpyw9HfjZUN/o1eLVz7XO4cDIMANyKEAfg/JRX2ZOv05ac/l5/t3Ri3+nLtPtflLY+JilrYqyieuyAF44S8ADgFIQ4APlTFrI3IJ5yyenvDfRJJ/afHvAOb5a2/0yysjYOLYuMPmRRO1eKTJV87P4OoPQQ4gA4I1AuRS+2H6dKJaW2A6cv0x5/Xdrx9PA2KZJ954qhgJcV9CJTpGDIvoNFMCQFK+nmASgqhDgA7uMP2sMQdfHT30unpPZDmWDXMBz0WnZLu34tpfrG+KUmE+pCI8NdWUgKhk85XjnKsbE+m3nmPrYACowQB8BbfH57M+KaWVL8HSPfS6eljiN2qOtqlpLd9nV5ya7Mc7fU33XKc7c9fTv0fubz6YHRzz8Wf/kZQl7meLDyzEFwrKDoL6OLCOA0hDgAxcPnk6pn2o/zlUqODHpDQXC0QHiG493NUtspxwd6J1aL8Z8e8iqq7U5l/cVSdJ5Uf5FUfSHXBwIlhBAHAKPxB+197fKxt106bYe5cQXBMd7vbpV2/KfU3TL8ewMVUt1FdqAbDHb18+ywF6zM/d8BwFGEOAAoNJ9PKo/Yj/PV1SI17xz5OLxJ2v5TDW/hkrm/bnSe3bkbfETn2Rs8A/AkQhwAeFm4TgpfIc26YuTxZI9927TmN6XmXVJT5nnv70cu54bqTg92LM0CnkCIA4BiFKyUpi6yH9nSKan9YFawy3TvxrU0e7FUl2BpFnAJQhwAlBKfX6qZbT8ueufI985nabb+YrsrCKBgCHEAAFuul2brM5s5szQL5AUhDgBwZrlcmh0MdizNAueNEAcAODe5WprNHqion8fSLDBOhDgAQO5NdGl23x9GLs1W1trhsHqmVH3B8CbOgz+H67mLBQqvv0tq3CE1brfv5Tz7LdKCP3esHEIcAKBwxlyaTUvtB0YuzbYflJp2SLt/Y29wnC1QIU2aIU2+YJSgd4H9XrCicH8Xiks6Zd++7/h2qfH14efWvRrqJAcq7etACXEAgJLm8429NGtZ9v1t2w9K7Ycyj4NSW+bnXb+ROo+d/jvD0axgdyHdPJzOsqTOxuHOWuPr0vFt9v+QGOwMG59UO1easkhacosUu0SastD+z6rP72j5hDgAgLsZY99ZIlQrTVs6+mcG+qSTR7JC3qHh0Ne0U9r9zNjdvFM7eZMvoJtXjEYshW4f7q5lD+GEY9KUS6SVH7WfY5dI0fn2PYtdiBAHAPC+QLlUO8d+jGasbt7g64ZnpI5jGh66yBjRzRvt2rwo3Ty3SQ3YS6EjumvbpRP7NPR/32DIDmfz3m132Aa7a+F6JyufMEIcAKD4jaub1y91HBk95DXvknb/Vkp2jfyOv/zMIa96Btuo5ItlSZ3Hs65be90Obo07pFSf/Rnjk2rj0rQl0tK/Gu6u1cwpir0LCXEAAEhSoGz4urzRDHXzRlmybT8kNfxW6jiq07p5ofqRy7SDIS8yRaqYLFVUS5WT7eVdunqj6+u0h1yyl0GPb5d6Woc/E5liB7TVazOdtcxSaBGHaEIcAADjMaKbt2T0zwz020FuRMg7eOZu3iB/mR3oBoPd4KMy++fJpxzP+tkfGQFOvQAACAtJREFUzN/fXiiDS6HHt43srp3YN/yZYEiKLbCnQmMLM921hSW5tyAhDgCAXAmUSTWz7MdoLEvqbbMna7uapN52++fedvvRk/W6t01q2z98PJ0887mD4XGEvurR3yufVNjlRcuyr0E89bq1pjdHLoXWJaRpy6Rltw531ybPLoql0FwgxAEAUCjGSJU19mMiLMveKPlsoW/wvZ42e1q38Y3MsZM6bZl3ZGFSxaSzdPvGCoST7SXLsZaC+zrtOk4NbCOWQqfaAW3O2+wBgykL7bt3MB18RoQ4AADczhh7m4uykDRp2sS/n05L/R2jhL6xwmC7vaw5eHysJeBBvuDpAc/3/7d3/6F+1XUcx5+v7Q6b0zmHOtrSLUxFCpzNyH5YQoU/yIQtSloW/RVKhoUY0Q/WpD+EWkH+CGlJlGmuH1YWEgRR/mEq1grRRFvTWaOtnM7pdLp3f5wzdpOwe2/fe0+fu+cDDvfyOedw3ofPvee+vp/POeeOdSNru7Ye3G7egn4q9IIuqB14KvTwxZM/JxniJEma9ebMORiypuLFff8e8iYSBl94Dpa9Hk6/+OBToYuWOxU6QoY4SZL08ubO696h1th71GY747AkSVKDDHGSJEkNMsRJkiQ1yBAnSZLUIEOcJElSgwxxkiRJDTLESZIkNcgQJ0mS1CBDnCRJUoMMcZIkSQ0yxEmSJDXIECdJktQgQ5wkSVKDDHGSJEkNMsRJkiQ1yBAnSZLUIEOcJElSgwxxkiRJDTLESZIkNcgQJ0mS1CBDnCRJUoMMcZIkSQ0yxEmSJDUoVTV0DTMqyQ5g6wwc6hhg5wwcR9PD/muffdg++7Bt9t9oLK+qY//TikMuxM2UJPdW1RlD16Gpsf/aZx+2zz5sm/03/ZxOlSRJapAhTpIkqUGGuOlzw9AF6H9i/7XPPmyffdg2+2+aeU+cJElSgxyJkyRJapAhTpIkqUGGuBFLsjjJj5LsSbI1yQeGrkkTk+SwJBv7ftud5PdJzhu6Lk1NkpOS7E3ynaFr0eQluSjJA/219JEkZw1dkyYmyYokP0/yRJLtSa5JMjZ0XbORIW70rgWeB5YAa4Hrk7x22JI0QWPAY8DbgaOAzwK3JlkxYE2aumuBe4YuQpOX5F3A1cBHgCOBtwF/HrQoTcZ1wN+BVwIr6a6plw5a0SxliBuhJAuANcDnqurpqroT+Alw8bCVaSKqak9Vrauqv1TV/qq6HdgCrBq6Nk1OkouAXcAvh65FU/IFYH1V3dX/Lj5eVY8PXZQm7NXArVW1t6q2A3cADmZMA0PcaJ0MvFBVD41r24w/vE1KsoSuT+8fuhZNXJKFwHrgk0PXoslLMhc4Azg2ycNJtvXTcfOHrk0T9lXgoiSHJ1kGnEcX5DRihrjROgJ46iVtT9JNB6ghSeYBNwHfqqoHh65Hk3IVsLGqtg1diKZkCTAPeC9wFt103Ol0tzeoDb+mG7x4CtgG3AvcNmhFs5QhbrSeBha+pG0hsHuAWjRFSeYA36a7t/FjA5ejSUiyEngn8JWha9GUPdt//VpV/a2qdgIbgPMHrEkT1F8/7wB+CCwAjgGOprvHUSNmiButh4CxJCeNazsNp+OakSTARrrRgDVVtW/gkjQ5ZwMrgEeTbAeuANYkuW/IojRxVfUE3ejN+DfR+1b6diwGTgCuqarnquofwI0YwqeFIW6EqmoP3aeP9UkWJHkLcCHdqI7acD1wKnBBVT373zbW/50bgBPppuBWAl8HfgacM2RRmrQbgcuSHJfkaOATwO0D16QJ6EdOtwCXJBlLsgj4MPCHYSubnQxxo3cpMJ/u8eqbgUuqypG4BiRZDnyU7o//9iRP98vagUvTBFXVM1W1/cBCd4vD3qraMXRtmpSr6F4P8xDwAPA74IuDVqTJWA2cC+wAHgb20QVxjZj/O1WSJKlBjsRJkiQ1yBAnSZLUIEOcJElSgwxxkiRJDTLESZIkNcgQJ0mS1CBDnCTNsCQrklSSsaFrkdQuQ5wkSVKDDHGSJEkNMsRJEpBkaZIfJNmRZEuSj/ft65J8P8n3kuxOcl+S08btd2qSXyXZleT+JO8Zt25+ki8n2ZrkySR3Jpk/7rBrkzyaZGeSz8zg6UqaBQxxkg55SeYAPwU2A8uAdwCXJzmn3+RCYBOwGPgucFuSeUnm9fv9AjgOuAy4Kckp/X5fAlYBb+73vRLYP+7QbwVO6Y/3+SSnTttJSpp1/N+pkg55Sd4IbKqqE8a1fRo4GdgKnFtVZ/btc4DHgff1m24CllbV/n79zcCfgPXAHuDMqtr8kuOtALYAx1fVtr7tbmBDVd0yTacpaZbxyShJguXA0iS7xrXNBX5DF+IeO9BYVfuTbAOW9k2PHQhwva10o3nHAK8AHnmZ424f9/0zwBFTPgNJhxynUyWpC2lbqmrRuOXIqjq/X3/8gQ37kbhXAX/tl+P7tgNOoBup2wnsBU6ckTOQdMgxxEkS3A3sTvKp/mGEuUlel+QN/fpVSVb373W7HHgOuAv4Ld0I2pX9PXJnAxcAt/Sjc98ENvQPTcxN8qYkh8342UmalQxxkg55VfUi8G5gJd29ajuBbwBH9Zv8GHg/8ARwMbC6qvZV1fN0oe28fp/rgA9V1YP9flcAfwTuAf4JXI3XXUkj4oMNkvQykqwDXlNVHxy6Fkkaz0+EkiRJDTLESZIkNcjpVEmSpAY5EidJktQgQ5wkSVKDDHGSJEkNMsRJkiQ1yBAnSZLUIEOcJElSg/4FEGlnhDcj/74AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1139,12 +992,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Data preperation - test set" + "Data preparation - test set" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1167,47 +1020,27 @@ "# 5.Transform this Pandas dataframe into a numpy array\n", "y_test = test_shifted['y_t+1'].as_matrix()\n", "X_test = test_shifted[['load_t-'+str(T-t) for t in range(1, T+1)]].as_matrix()\n", - "X_test = X_test.reshape(X_test.shape[0], T, 1)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1458,)" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_test.shape" + "X_test = X_test.reshape(X_test.shape[0], T, 1)" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(1458, 6, 1)" + "((1458,), (1458, 6, 1))" ] }, - "execution_count": 33, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "X_test.shape" + "y_test.shape, X_test.shape" ] }, { @@ -1219,22 +1052,22 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.21],\n", - " [0.3 ],\n", - " [0.38],\n", + "array([[-0.99],\n", + " [-0.52],\n", + " [-0.12],\n", " ...,\n", - " [0.54],\n", - " [0.46],\n", - " [0.42]], dtype=float32)" + " [ 0.72],\n", + " [ 0.28],\n", + " [ 0.18]], dtype=float32)" ] }, - "execution_count": 34, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1253,7 +1086,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1288,35 +1121,35 @@ " 0\n", " 2014-11-01 05:00:00\n", " t+1\n", - " 2,673.13\n", + " 2,745.46\n", " 2,714.00\n", " \n", " \n", " 1\n", " 2014-11-01 06:00:00\n", " t+1\n", - " 2,947.12\n", + " 3,014.80\n", " 2,970.00\n", " \n", " \n", " 2\n", " 2014-11-01 07:00:00\n", " t+1\n", - " 3,208.74\n", + " 3,237.91\n", " 3,189.00\n", " \n", " \n", " 3\n", " 2014-11-01 08:00:00\n", " t+1\n", - " 3,337.19\n", + " 3,347.28\n", " 3,356.00\n", " \n", " \n", " 4\n", " 2014-11-01 09:00:00\n", " t+1\n", - " 3,466.88\n", + " 3,479.99\n", " 3,436.00\n", " \n", " \n", @@ -1325,20 +1158,20 @@ ], "text/plain": [ " timestamp h prediction actual\n", - "0 2014-11-01 05:00:00 t+1 2,673.13 2,714.00\n", - "1 2014-11-01 06:00:00 t+1 2,947.12 2,970.00\n", - "2 2014-11-01 07:00:00 t+1 3,208.74 3,189.00\n", - "3 2014-11-01 08:00:00 t+1 3,337.19 3,356.00\n", - "4 2014-11-01 09:00:00 t+1 3,466.88 3,436.00" + "0 2014-11-01 05:00:00 t+1 2,745.46 2,714.00\n", + "1 2014-11-01 06:00:00 t+1 3,014.80 2,970.00\n", + "2 2014-11-01 07:00:00 t+1 3,237.91 3,189.00\n", + "3 2014-11-01 08:00:00 t+1 3,347.28 3,356.00\n", + "4 2014-11-01 09:00:00 t+1 3,479.99 3,436.00" ] }, - "execution_count": 35, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n", + "eval_df = pd.DataFrame(predictions, columns=[f't+{t}' for t in range(1, HORIZON+1)])\n", "eval_df['timestamp'] = test_shifted.index\n", "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n", "eval_df['actual'] = np.transpose(y_test).ravel()\n", @@ -1355,34 +1188,21 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# %load -s mape common/utils.py\n", - "def mape(predictions, actuals):\n", - " \"\"\"Mean absolute percentage error\"\"\"\n", - " return ((predictions - actuals).abs() / actuals).mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, + "execution_count": 29, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "0.015229265571591601" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE: 1.40%\n" + ] } ], "source": [ - "mape(eval_df['prediction'], eval_df['actual'])" + "from common.utils import mape\n", + "\n", + "print(\"MAPE: {:.2f}%\".format(100* mape(eval_df['prediction'], eval_df['actual'])))" ] }, { @@ -1394,17 +1214,19 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1414,13 +1236,20 @@ "plt.ylabel('load', fontsize=12)\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.5", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "dftf2" }, "language_info": { "codemirror_mode": { @@ -1432,7 +1261,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/3_RNN_encoder_decoder.ipynb b/3_RNN_encoder_decoder.ipynb index e787b22..408fba3 100644 --- a/3_RNN_encoder_decoder.ipynb +++ b/3_RNN_encoder_decoder.ipynb @@ -21,33 +21,33 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", - "import warnings\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", "import datetime as dt\n", - "from collections import UserDict\n", - "from IPython.display import Image\n", + "from common.utils import TimeSeriesTensor, create_evaluation_df\n", + "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", - "from common.utils import load_data, mape, TimeSeriesTensor, create_evaluation_df\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", "\n", + "import pandas as pd\n", "pd.options.display.float_format = '{:,.2f}'.format\n", - "np.set_printoptions(precision=2)\n", - "warnings.filterwarnings(\"ignore\")" + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import tensorflow as tf\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Load data into Pandas dataframe" + "Load the data from csv into a Pandas dataframe. Make sure to first complete the [0_data_setup](0_data_setup.ipynb) notebook." ] }, { @@ -125,16 +125,15 @@ } ], "source": [ - "energy = load_data('data/')\n", + "file_name = os.path.join('data', 'energy.parquet')\n", + "energy = pd.read_parquet(file_name)\n", "energy.head()" ] }, { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "valid_start_dt = '2014-09-01 00:00:00'\n", @@ -154,35 +153,31 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "train = energy.copy()[energy.index < valid_start_dt][['load', 'temp']]" + "train = energy.copy()[:valid_start_dt]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Scale data to be in range (0, 1). This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data." + "Scale data. This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data." ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.preprocessing import StandardScaler\n", "\n", - "y_scaler = MinMaxScaler()\n", + "y_scaler = StandardScaler()\n", "y_scaler.fit(train[['load']])\n", "\n", - "X_scaler = MinMaxScaler()\n", + "X_scaler = StandardScaler()\n", "train[['load', 'temp']] = X_scaler.fit_transform(train)" ] }, @@ -207,13 +202,11 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n", - "train_inputs = TimeSeriesTensor(train, 'load', HORIZON, {'X':(range(-T+1, 1), ['load', 'temp'])})" + "train_inputs = TimeSeriesTensor(train, 'load', HORIZON, tensor_structure)" ] }, { @@ -273,116 +266,116 @@ " \n", " \n", " 2012-01-01 05:00:00\n", - " 0.18\n", - " 0.23\n", - " 0.29\n", - " 0.22\n", - " 0.18\n", - " 0.14\n", - " 0.13\n", - " 0.13\n", - " 0.15\n", - " 0.42\n", - " 0.43\n", - " 0.40\n", - " 0.41\n", - " 0.42\n", - " 0.41\n", + " -1.31\n", + " -1.03\n", + " -0.69\n", + " -1.07\n", + " -1.32\n", + " -1.52\n", + " -1.59\n", + " -1.59\n", + " -1.50\n", + " -0.81\n", + " -0.77\n", + " -0.91\n", + " -0.86\n", + " -0.81\n", + " -0.84\n", " \n", " \n", " 2012-01-01 06:00:00\n", - " 0.23\n", - " 0.29\n", - " 0.35\n", - " 0.18\n", - " 0.14\n", - " 0.13\n", - " 0.13\n", - " 0.15\n", - " 0.18\n", - " 0.43\n", - " 0.40\n", - " 0.41\n", - " 0.42\n", - " 0.41\n", - " 0.40\n", + " -1.03\n", + " -0.69\n", + " -0.36\n", + " -1.32\n", + " -1.52\n", + " -1.59\n", + " -1.59\n", + " -1.50\n", + " -1.31\n", + " -0.77\n", + " -0.91\n", + " -0.86\n", + " -0.81\n", + " -0.84\n", + " -0.91\n", " \n", " \n", " 2012-01-01 07:00:00\n", - " 0.29\n", - " 0.35\n", - " 0.37\n", - " 0.14\n", - " 0.13\n", - " 0.13\n", - " 0.15\n", - " 0.18\n", - " 0.23\n", - " 0.40\n", - " 0.41\n", - " 0.42\n", - " 0.41\n", - " 0.40\n", - " 0.39\n", + " -0.69\n", + " -0.36\n", + " -0.24\n", + " -1.52\n", + " -1.59\n", + " -1.59\n", + " -1.50\n", + " -1.31\n", + " -1.03\n", + " -0.91\n", + " -0.86\n", + " -0.81\n", + " -0.84\n", + " -0.91\n", + " -0.96\n", " \n", " \n", " 2012-01-01 08:00:00\n", - " 0.35\n", - " 0.37\n", - " 0.37\n", - " 0.13\n", - " 0.13\n", - " 0.15\n", - " 0.18\n", - " 0.23\n", - " 0.29\n", - " 0.41\n", - " 0.42\n", - " 0.41\n", - " 0.40\n", - " 0.39\n", - " 0.39\n", + " -0.36\n", + " -0.24\n", + " -0.23\n", + " -1.59\n", + " -1.59\n", + " -1.50\n", + " -1.31\n", + " -1.03\n", + " -0.69\n", + " -0.86\n", + " -0.81\n", + " -0.84\n", + " -0.91\n", + " -0.96\n", + " -0.96\n", " \n", " \n", " 2012-01-01 09:00:00\n", - " 0.37\n", - " 0.37\n", - " 0.37\n", - " 0.13\n", - " 0.15\n", - " 0.18\n", - " 0.23\n", - " 0.29\n", - " 0.35\n", - " 0.42\n", - " 0.41\n", - " 0.40\n", - " 0.39\n", - " 0.39\n", - " 0.43\n", + " -0.24\n", + " -0.23\n", + " -0.22\n", + " -1.59\n", + " -1.50\n", + " -1.31\n", + " -1.03\n", + " -0.69\n", + " -0.36\n", + " -0.81\n", + " -0.84\n", + " -0.91\n", + " -0.96\n", + " -0.96\n", + " -0.74\n", " \n", " \n", "\n", "
" ], "text/plain": [ - "tensor target X \\\n", - "feature y load temp \n", - "time step t+1 t+2 t+3 t-5 t-4 t-3 t-2 t-1 t t-5 t-4 \n", - "2012-01-01 05:00:00 0.18 0.23 0.29 0.22 0.18 0.14 0.13 0.13 0.15 0.42 0.43 \n", - "2012-01-01 06:00:00 0.23 0.29 0.35 0.18 0.14 0.13 0.13 0.15 0.18 0.43 0.40 \n", - "2012-01-01 07:00:00 0.29 0.35 0.37 0.14 0.13 0.13 0.15 0.18 0.23 0.40 0.41 \n", - "2012-01-01 08:00:00 0.35 0.37 0.37 0.13 0.13 0.15 0.18 0.23 0.29 0.41 0.42 \n", - "2012-01-01 09:00:00 0.37 0.37 0.37 0.13 0.15 0.18 0.23 0.29 0.35 0.42 0.41 \n", + "tensor target X \\\n", + "feature y load \n", + "time step t+1 t+2 t+3 t-5 t-4 t-3 t-2 t-1 t \n", + "2012-01-01 05:00:00 -1.31 -1.03 -0.69 -1.07 -1.32 -1.52 -1.59 -1.59 -1.50 \n", + "2012-01-01 06:00:00 -1.03 -0.69 -0.36 -1.32 -1.52 -1.59 -1.59 -1.50 -1.31 \n", + "2012-01-01 07:00:00 -0.69 -0.36 -0.24 -1.52 -1.59 -1.59 -1.50 -1.31 -1.03 \n", + "2012-01-01 08:00:00 -0.36 -0.24 -0.23 -1.59 -1.59 -1.50 -1.31 -1.03 -0.69 \n", + "2012-01-01 09:00:00 -0.24 -0.23 -0.22 -1.59 -1.50 -1.31 -1.03 -0.69 -0.36 \n", "\n", - "tensor \n", - "feature \n", - "time step t-3 t-2 t-1 t \n", - "2012-01-01 05:00:00 0.40 0.41 0.42 0.41 \n", - "2012-01-01 06:00:00 0.41 0.42 0.41 0.40 \n", - "2012-01-01 07:00:00 0.42 0.41 0.40 0.39 \n", - "2012-01-01 08:00:00 0.41 0.40 0.39 0.39 \n", - "2012-01-01 09:00:00 0.40 0.39 0.39 0.43 " + "tensor \n", + "feature temp \n", + "time step t-5 t-4 t-3 t-2 t-1 t \n", + "2012-01-01 05:00:00 -0.81 -0.77 -0.91 -0.86 -0.81 -0.84 \n", + "2012-01-01 06:00:00 -0.77 -0.91 -0.86 -0.81 -0.84 -0.91 \n", + "2012-01-01 07:00:00 -0.91 -0.86 -0.81 -0.84 -0.91 -0.96 \n", + "2012-01-01 08:00:00 -0.86 -0.81 -0.84 -0.91 -0.96 -0.96 \n", + "2012-01-01 09:00:00 -0.81 -0.84 -0.91 -0.96 -0.96 -0.74 " ] }, "execution_count": 7, @@ -404,13 +397,11 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", - "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load', 'temp']]\n", + "valid = energy.copy()[look_back_dt:test_start_dt][['load', 'temp']]\n", "valid[['load', 'temp']] = X_scaler.transform(valid)\n", "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)" ] @@ -426,34 +417,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will implement a RNN forecasting model with the following structure:" + "We will implement a RNN forecasting model with the following structure:\n", + "\n", + "![Simple Encoder-Decoder](./images/simple_encoder_decoder.png)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('./images/simple_encoder_decoder.png')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, "outputs": [ { "name": "stderr", @@ -471,10 +443,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "LATENT_DIM = 5\n", @@ -484,40 +454,14 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(GRU(LATENT_DIM, input_shape=(T, 2)))\n", - "model.add(RepeatVector(HORIZON))\n", - "model.add(GRU(LATENT_DIM, return_sequences=True))\n", - "model.add(TimeDistributed(Dense(1)))\n", - "model.add(Flatten())" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "model.compile(optimizer='RMSprop', loss='mse')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Model: \"sequential_1\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", @@ -539,15 +483,22 @@ } ], "source": [ + "model = Sequential()\n", + "model.add(GRU(LATENT_DIM, input_shape=(T, 2)))\n", + "model.add(RepeatVector(HORIZON))\n", + "model.add(GRU(LATENT_DIM, return_sequences=True))\n", + "model.add(TimeDistributed(Dense(1)))\n", + "model.add(Flatten())\n", + "\n", + "model.compile(optimizer='RMSprop', loss='mse')\n", + "\n", "model.summary()" ] }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)" @@ -555,67 +506,73 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Train on 23368 samples, validate on 1461 samples\n", - "Epoch 1/50\n", - "23368/23368 [==============================] - 4s 185us/step - loss: 0.0217 - val_loss: 0.0053\n", - "Epoch 2/50\n", - "23368/23368 [==============================] - 3s 129us/step - loss: 0.0050 - val_loss: 0.0042\n", - "Epoch 3/50\n", - "23368/23368 [==============================] - 3s 147us/step - loss: 0.0044 - val_loss: 0.0039\n", - "Epoch 4/50\n", - "23368/23368 [==============================] - 3s 129us/step - loss: 0.0041 - val_loss: 0.0035\n", - "Epoch 5/50\n", - "23368/23368 [==============================] - 3s 134us/step - loss: 0.0039 - val_loss: 0.0033\n", - "Epoch 6/50\n", - "23368/23368 [==============================] - 3s 138us/step - loss: 0.0037 - val_loss: 0.0032\n", - "Epoch 7/50\n", - "23368/23368 [==============================] - 3s 127us/step - loss: 0.0035 - val_loss: 0.0029\n", - "Epoch 8/50\n", - "23368/23368 [==============================] - 3s 130us/step - loss: 0.0034 - val_loss: 0.0029\n", - "Epoch 9/50\n", - "23368/23368 [==============================] - 4s 156us/step - loss: 0.0033 - val_loss: 0.0037\n", - "Epoch 10/50\n", - "23368/23368 [==============================] - 3s 123us/step - loss: 0.0033 - val_loss: 0.0029\n", - "Epoch 11/50\n", - "23368/23368 [==============================] - 4s 177us/step - loss: 0.0032 - val_loss: 0.0026\n", - "Epoch 12/50\n", - "23368/23368 [==============================] - 5s 195us/step - loss: 0.0032 - val_loss: 0.0032\n", - "Epoch 13/50\n", - "23368/23368 [==============================] - 4s 167us/step - loss: 0.0032 - val_loss: 0.0027\n", - "Epoch 14/50\n", - "23368/23368 [==============================] - 3s 135us/step - loss: 0.0032 - val_loss: 0.0028\n", - "Epoch 15/50\n", - "23368/23368 [==============================] - 4s 174us/step - loss: 0.0031 - val_loss: 0.0041\n", - "Epoch 16/50\n", - "23368/23368 [==============================] - 4s 172us/step - loss: 0.0031 - val_loss: 0.0034\n" + "Train on 23369 samples, validate on 1462 samples\n", + "Epoch 1/10\n", + "23369/23369 [==============================] - 4s 174us/step - loss: 0.4248 - val_loss: 0.1697\n", + "Epoch 2/10\n", + "23369/23369 [==============================] - 3s 137us/step - loss: 0.1307 - val_loss: 0.0983\n", + "Epoch 3/10\n", + "23369/23369 [==============================] - 3s 141us/step - loss: 0.1017 - val_loss: 0.0844\n", + "Epoch 4/10\n", + "23369/23369 [==============================] - 4s 153us/step - loss: 0.0937 - val_loss: 0.0775\n", + "Epoch 5/10\n", + "23369/23369 [==============================] - 4s 150us/step - loss: 0.0882 - val_loss: 0.0732\n", + "Epoch 6/10\n", + "23369/23369 [==============================] - 4s 155us/step - loss: 0.0832 - val_loss: 0.0693\n", + "Epoch 7/10\n", + "23369/23369 [==============================] - 4s 154us/step - loss: 0.0791 - val_loss: 0.0648\n", + "Epoch 8/10\n", + "23369/23369 [==============================] - 4s 159us/step - loss: 0.0759 - val_loss: 0.0646\n", + "Epoch 9/10\n", + "23369/23369 [==============================] - 4s 160us/step - loss: 0.0732 - val_loss: 0.0625\n", + "Epoch 10/10\n", + "23369/23369 [==============================] - 4s 159us/step - loss: 0.0709 - val_loss: 0.0616\n" ] - }, + } + ], + "source": [ + "history = model.fit(\n", + " train_inputs['X'],\n", + " train_inputs['target'],\n", + " batch_size=BATCH_SIZE,\n", + " epochs=EPOCHS,\n", + " validation_data=(valid_inputs['X'], \n", + " valid_inputs['target']),\n", + " callbacks=[earlystop],\n", + " verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { "data": { + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "model.fit(train_inputs['X'],\n", - " train_inputs['target'],\n", - " batch_size=BATCH_SIZE,\n", - " epochs=EPOCHS,\n", - " validation_data=(valid_inputs['X'], valid_inputs['target']),\n", - " callbacks=[earlystop],\n", - " verbose=1)" + "plot_df = pd.DataFrame.from_dict({'train_loss':history.history['loss'], 'val_loss':history.history['val_loss']})\n", + "plot_df.plot(logy=True, figsize=(10,10), fontsize=12)\n", + "plt.xlabel('epoch', fontsize=12)\n", + "plt.ylabel('loss', fontsize=12)\n", + "plt.show()" ] }, { @@ -627,10 +584,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", @@ -641,10 +596,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, + "execution_count": 16, + "metadata": {}, "outputs": [], "source": [ "predictions = model.predict(test_inputs['X'])" @@ -652,22 +605,22 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.24, 0.31, 0.39],\n", - " [0.31, 0.39, 0.46],\n", - " [0.4 , 0.46, 0.51],\n", + "array([[-0.99, -0.3 , 0.17],\n", + " [-0.5 , 0.09, 0.23],\n", + " [-0.07, 0.23, 0.29],\n", " ...,\n", - " [0.62, 0.57, 0.51],\n", - " [0.58, 0.54, 0.48],\n", - " [0.54, 0.5 , 0.46]], dtype=float32)" + " [ 1.09, 0.66, 0.3 ],\n", + " [ 0.72, 0.28, -0.05],\n", + " [ 0.6 , 0.35, 0.18]], dtype=float32)" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -676,9 +629,16 @@ "predictions" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combine predictions with actual values, then unpivot for easier analysis. " + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -713,35 +673,35 @@ " 0\n", " 2014-11-01 05:00:00\n", " t+1\n", - " 2,748.30\n", + " 2,745.18\n", " 2,714.00\n", " \n", " \n", " 1\n", " 2014-11-01 06:00:00\n", " t+1\n", - " 2,994.21\n", + " 3,025.00\n", " 2,970.00\n", " \n", " \n", " 2\n", " 2014-11-01 07:00:00\n", " t+1\n", - " 3,267.59\n", + " 3,266.59\n", " 3,189.00\n", " \n", " \n", " 3\n", " 2014-11-01 08:00:00\n", " t+1\n", - " 3,405.73\n", + " 3,354.95\n", " 3,356.00\n", " \n", " \n", " 4\n", " 2014-11-01 09:00:00\n", " t+1\n", - " 3,537.20\n", + " 3,476.51\n", " 3,436.00\n", " \n", " \n", @@ -750,14 +710,14 @@ ], "text/plain": [ " timestamp h prediction actual\n", - "0 2014-11-01 05:00:00 t+1 2,748.30 2,714.00\n", - "1 2014-11-01 06:00:00 t+1 2,994.21 2,970.00\n", - "2 2014-11-01 07:00:00 t+1 3,267.59 3,189.00\n", - "3 2014-11-01 08:00:00 t+1 3,405.73 3,356.00\n", - "4 2014-11-01 09:00:00 t+1 3,537.20 3,436.00" + "0 2014-11-01 05:00:00 t+1 2,745.18 2,714.00\n", + "1 2014-11-01 06:00:00 t+1 3,025.00 2,970.00\n", + "2 2014-11-01 07:00:00 t+1 3,266.59 3,189.00\n", + "3 2014-11-01 08:00:00 t+1 3,354.95 3,356.00\n", + "4 2014-11-01 09:00:00 t+1 3,476.51 3,436.00" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -769,47 +729,144 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
timestamphpredictionactual
02014-11-01 05:00:00t+12,745.182,714.00
14562014-11-01 05:00:00t+23,136.852,970.00
29122014-11-01 05:00:00t+33,407.853,189.00
12014-11-01 06:00:00t+13,025.002,970.00
14572014-11-01 06:00:00t+23,362.983,189.00
29132014-11-01 06:00:00t+33,439.713,356.00
\n", + "
" + ], "text/plain": [ - "h\n", - "t+1 0.02\n", - "t+2 0.04\n", - "t+3 0.06\n", - "Name: APE, dtype: float64" + " timestamp h prediction actual\n", + "0 2014-11-01 05:00:00 t+1 2,745.18 2,714.00\n", + "1456 2014-11-01 05:00:00 t+2 3,136.85 2,970.00\n", + "2912 2014-11-01 05:00:00 t+3 3,407.85 3,189.00\n", + "1 2014-11-01 06:00:00 t+1 3,025.00 2,970.00\n", + "1457 2014-11-01 06:00:00 t+2 3,362.98 3,189.00\n", + "2913 2014-11-01 06:00:00 t+3 3,439.71 3,356.00" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n", - "eval_df.groupby('h')['APE'].mean()" + "eval_df[eval_df.timestamp<=pd.to_datetime('2014-11-01 06:00:00')].sort_values('timestamp')" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.04260425015099984" + "h\n", + "t+1 0.01\n", + "t+2 0.03\n", + "t+3 0.05\n", + "Name: APE, dtype: float64" ] }, - "execution_count": 22, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "mape(eval_df['prediction'], eval_df['actual'])" + "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n", + "eval_df.groupby('h')['APE'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE: 3.11%\n" + ] + } + ], + "source": [ + "from common.utils import mape\n", + "\n", + "print(\"MAPE: {:.2f}%\".format(100* mape(eval_df['prediction'], eval_df['actual'])))" ] }, { @@ -821,24 +878,33 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "plot_df = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+1')][['timestamp', 'actual']]\n", "for t in range(1, HORIZON+1):\n", - " plot_df['t+'+str(t)] = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+'+str(t))]['prediction'].values\n", + " plot_df[f't+{t}'] = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h==f't+{t}')]['prediction'].values\n", "\n", "fig = plt.figure(figsize=(15, 8))\n", "ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n", @@ -851,13 +917,20 @@ "ax.legend(loc='best')\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.5", + "display_name": "Python 3.6", "language": "python", - "name": "python3" + "name": "dftf2" }, "language_info": { "codemirror_mode": { @@ -869,7 +942,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/4_ES_RNN.ipynb b/4_ES_RNN.ipynb index 9fad754..ea4020f 100644 --- a/4_ES_RNN.ipynb +++ b/4_ES_RNN.ipynb @@ -9,13 +9,15 @@ "In this notebook, we demonstrate how to:\n", "- prepare time series data for training a RNN forecasting model\n", "- get data in the required shape for the keras API\n", - "- implement an ES-RNN model in keras to predict the next 3 steps ahead (time *t+1* to *t+3*) in the time series. This model uses recent values of load as the model input. The model will be trained to output a vector, the elements of which are ordered predictions for future time steps.\n", + "- implement an Exponential Smoothing RNN (ES-RNN)1,2 model in keras to predict the next 3 steps ahead (time *t+1* to *t+3*) in the time series. This model uses recent values of load as the model input. The model will be trained to output a vector, the elements of which are ordered predictions for future time steps.\n", "- enable early stopping to reduce the likelihood of model overfitting\n", "- evaluate the model on a test dataset\n", "\n", - "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load.\n", + "The data in this example is taken from the GEFCom2014 forecasting competition3. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load.\n", "\n", - "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." + "1Slawek Smyl, Jai Ranganathan, and Andrea Pasqua. \"M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model\", 2018. URL https://eng.uber.com/m4-forecasting-competition/.
\n", + "2Andrew Redd and Kaung Khin and Aldo Marini, \"Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm\", Jul 2019, URL https://arxiv.org/abs/1907.03329.
\n", + "3Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." ] }, { @@ -25,27 +27,31 @@ "outputs": [], "source": [ "import os\n", - "import warnings\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", "import datetime as dt\n", "from collections import UserDict, deque\n", - "from IPython.display import Image\n", + "from common.utils import TimeSeriesTensor, create_evaluation_df\n", + "\n", + "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", - "from common.utils import load_data, mape, TimeSeriesTensor, create_evaluation_df\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", "\n", + "import pandas as pd\n", "pd.options.display.float_format = '{:,.2f}'.format\n", - "np.set_printoptions(precision=2)\n", - "warnings.filterwarnings(\"ignore\")" + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import tensorflow as tf\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Load data into Pandas dataframe" + "Load the data from csv into a Pandas dataframe. Make sure to first complete the [0_data_setup](0_data_setup.ipynb) notebook." ] }, { @@ -123,7 +129,8 @@ } ], "source": [ - "energy = load_data('data/')\n", + "file_name = os.path.join('data', 'energy.parquet')\n", + "energy = pd.read_parquet(file_name)\n", "energy.head()" ] }, @@ -144,11 +151,14 @@ "metadata": {}, "outputs": [], "source": [ - "valid_start_dt = '2014-08-30 08:00:00'\n", - "test_start_dt = '2014-10-31 11:00:00'\n", - "test_end_dt = '2014-12-30 18:00:00'\n", + "# Note: the dates below are chosen to ensure that the number of samples in each of the splits are integer multiples\n", + "# of the training batch size (48). This is a limitation of the ES-RNN implementation below.\n", + "\n", + "valid_start_dt = '2014-08-30 13:00:00'\n", + "test_start_dt = '2014-10-31 15:00:00'\n", + "test_end_dt = '2014-12-31 04:00:00'\n", "\n", - "T = 6\n", + "T = 12\n", "HORIZON = 3" ] }, @@ -165,14 +175,14 @@ "metadata": {}, "outputs": [], "source": [ - "train = energy.copy()[energy.index < valid_start_dt][['load']]" + "train = energy.copy()[:valid_start_dt][['load']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Scale data to be in range (0, 1). This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data." + "Scale data. This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data." ] }, { @@ -181,9 +191,9 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.preprocessing import StandardScaler\n", "\n", - "scaler = MinMaxScaler()\n", + "scaler = StandardScaler()\n", "scaler.fit(train[['load']])\n", "train[['load']] = scaler.transform(train)" ] @@ -243,18 +253,24 @@ " \n", " tensor\n", " target\n", - " X\n", + " X\n", " \n", " \n", " feature\n", " y\n", - " load\n", + " load\n", " \n", " \n", " time step\n", " t+1\n", " t+2\n", " t+3\n", + " t-11\n", + " t-10\n", + " t-9\n", + " t-8\n", + " t-7\n", + " t-6\n", " t-5\n", " t-4\n", " t-3\n", @@ -265,52 +281,77 @@ " \n", " \n", " \n", - " 2012-01-01 05:00:00\n", - " 0.18\n", - " 0.23\n", - " 0.29\n", - " 0.22\n", - " 0.18\n", - " 0.14\n", - " 0.13\n", - " 0.13\n", - " 0.15\n", + " 2012-01-01 11:00:00\n", + " -0.22\n", + " -0.28\n", + " -0.33\n", + " -1.07\n", + " -1.32\n", + " -1.52\n", + " -1.59\n", + " -1.59\n", + " -1.50\n", + " -1.31\n", + " -1.03\n", + " -0.69\n", + " -0.36\n", + " -0.24\n", + " -0.23\n", " \n", " \n", - " 2012-01-01 06:00:00\n", - " 0.23\n", - " 0.29\n", - " 0.35\n", - " 0.18\n", - " 0.14\n", - " 0.13\n", - " 0.13\n", - " 0.15\n", - " 0.18\n", + " 2012-01-01 12:00:00\n", + " -0.28\n", + " -0.33\n", + " -0.30\n", + " -1.32\n", + " -1.52\n", + " -1.59\n", + " -1.59\n", + " -1.50\n", + " -1.31\n", + " -1.03\n", + " -0.69\n", + " -0.36\n", + " -0.24\n", + " -0.23\n", + " -0.22\n", " \n", " \n", - " 2012-01-01 07:00:00\n", - " 0.29\n", - " 0.35\n", - " 0.37\n", - " 0.14\n", - " 0.13\n", - " 0.13\n", - " 0.15\n", - " 0.18\n", - " 0.23\n", + " 2012-01-01 13:00:00\n", + " -0.33\n", + " -0.30\n", + " 0.31\n", + " -1.52\n", + " -1.59\n", + " -1.59\n", + " -1.50\n", + " -1.31\n", + " -1.03\n", + " -0.69\n", + " -0.36\n", + " -0.24\n", + " -0.23\n", + " -0.22\n", + " -0.28\n", " \n", " \n", "\n", "
" ], "text/plain": [ - "tensor target X \n", - "feature y load \n", - "time step t+1 t+2 t+3 t-5 t-4 t-3 t-2 t-1 t\n", - "2012-01-01 05:00:00 0.18 0.23 0.29 0.22 0.18 0.14 0.13 0.13 0.15\n", - "2012-01-01 06:00:00 0.23 0.29 0.35 0.18 0.14 0.13 0.13 0.15 0.18\n", - "2012-01-01 07:00:00 0.29 0.35 0.37 0.14 0.13 0.13 0.15 0.18 0.23" + "tensor target X \\\n", + "feature y load \n", + "time step t+1 t+2 t+3 t-11 t-10 t-9 t-8 t-7 t-6 \n", + "2012-01-01 11:00:00 -0.22 -0.28 -0.33 -1.07 -1.32 -1.52 -1.59 -1.59 -1.50 \n", + "2012-01-01 12:00:00 -0.28 -0.33 -0.30 -1.32 -1.52 -1.59 -1.59 -1.50 -1.31 \n", + "2012-01-01 13:00:00 -0.33 -0.30 0.31 -1.52 -1.59 -1.59 -1.50 -1.31 -1.03 \n", + "\n", + "tensor \n", + "feature \n", + "time step t-5 t-4 t-3 t-2 t-1 t \n", + "2012-01-01 11:00:00 -1.31 -1.03 -0.69 -0.36 -0.24 -0.23 \n", + "2012-01-01 12:00:00 -1.03 -0.69 -0.36 -0.24 -0.23 -0.22 \n", + "2012-01-01 13:00:00 -0.69 -0.36 -0.24 -0.23 -0.22 -0.28 " ] }, "execution_count": 7, @@ -332,7 +373,7 @@ "output_type": "stream", "text": [ "y_train shape: (23328, 3)\n", - "x_train shape: (23328, 6, 1)\n" + "x_train shape: (23328, 12, 1)\n" ] } ], @@ -341,71 +382,6 @@ "print(\"x_train shape: \", train_inputs['X'].shape)" ] }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[0.22],\n", - " [0.18],\n", - " [0.14],\n", - " [0.13],\n", - " [0.13],\n", - " [0.15]],\n", - "\n", - " [[0.18],\n", - " [0.14],\n", - " [0.13],\n", - " [0.13],\n", - " [0.15],\n", - " [0.18]],\n", - "\n", - " [[0.14],\n", - " [0.13],\n", - " [0.13],\n", - " [0.15],\n", - " [0.18],\n", - " [0.23]]])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_inputs['X'][:3]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.18, 0.23, 0.29],\n", - " [0.23, 0.29, 0.35],\n", - " [0.29, 0.35, 0.37]])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_inputs['target'][:3]" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -415,14 +391,26 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y_valid shape: (1488, 3)\n", + "x_valid shape: (1488, 12, 1)\n" + ] + } + ], "source": [ "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", - "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load']]\n", + "valid = energy.copy()[look_back_dt:test_start_dt][['load']]\n", "valid[['load']] = scaler.transform(valid)\n", - "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)" + "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)\n", + "\n", + "print(\"y_valid shape: \", valid_inputs['target'].shape)\n", + "print(\"x_valid shape: \", valid_inputs['X'].shape)" ] }, { @@ -436,59 +424,40 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will implement ES-RNN forecasting model with the following structure:" + "We will implement ES-RNN forecasting model with the following structure:\n", + "\n", + "![ES-RNN](./images/es_rnn.png)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "Image('./images/es_rnn.png')" + "LATENT_DIM = 5 # number of units in the RNN layer\n", + "BATCH_SIZE = 48 # number of samples per mini-batch\n", + "EPOCHS = 20 # maximum number of times the training algorithm will cycle through all samples\n", + "m = 24 # seasonality length" ] }, { - "cell_type": "code", - "execution_count": 13, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], "source": [ - "from keras.models import Model\n", - "from keras.layers import Input, GRU, Dense, Lambda\n", - "from keras.callbacks import EarlyStopping" + "Check that train and validation set lenghts are multiple of BATCH_SIZE" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "LATENT_DIM = 5 # number of units in the RNN layer\n", - "BATCH_SIZE = 48 # number of samples per mini-batch\n", - "EPOCHS = 10 # maximum number of times the training algorithm will cycle through all samples\n", - "m = 24 # seasonality length" + "n = train_inputs['X'].shape[0]\n", + "assert n % BATCH_SIZE == 0, f'Train set size, {n:,} is not a multiple of batch size ({BATCH_SIZE})'\n", + "n = valid_inputs['X'].shape[0]\n", + "assert n % BATCH_SIZE == 0, f'Validation set size, {n:,} is not a multiple of batch size ({BATCH_SIZE})'" ] }, { @@ -502,18 +471,26 @@ "\n", "\n", "There are 3 methods you need to implement in your custom layer:\n", - "- build(input_shape): this is where you will define your weights.\n", - "- call(x): this is where the layer's logic lives.\n", - "- compute_output_shape(input_shape): in case your layer modifies the shape of its input, you should specify here the shape transformation logic. \n", + "- `build(input_shape)`: this is where you will define your weights.\n", + "- `call(x)`: this is where the layer's logic lives.\n", + "- `compute_output_shape(input_shape)`: in case your layer modifies the shape of its input, you should specify here the shape transformation logic. \n", "\n", "You can check [Keras documentation](https://keras.io/layers/writing-your-own-keras-layers/) for more details about creating custom layer." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], "source": [ "from keras import backend as K\n", "from keras.layers import Layer\n", @@ -530,7 +507,7 @@ " \n", " super(ES, self).__init__(**kwargs)\n", "\n", - " # initialization of the learned parameters of exponential smoothing\n", + " # initialization of the trainable parameters of exponential smoothing\n", " def build(self, input_shape):\n", " self.alpha = self.add_weight(name='alpha', shape=(1,),\n", " initializer='uniform', trainable=True)\n", @@ -632,38 +609,14 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "model_input = Input(shape=(None, 1))\n", - "[normalized_input, denormalization_coeff] = ES(HORIZON, m, BATCH_SIZE, T)(model_input)\n", - "gru_out = GRU(LATENT_DIM)(normalized_input)\n", - "model_output_normalized = Dense(HORIZON)(gru_out)\n", - "model_output = Denormalization()([model_output_normalized, denormalization_coeff])\n", - "model = Model(inputs=model_input, outputs=model_output)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "model.compile(optimizer='RMSprop', loss='mse')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Model: \"model_1\"\n", "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", @@ -686,21 +639,34 @@ } ], "source": [ + "from keras.models import Model\n", + "from keras.layers import Input, GRU, Dense, Lambda\n", + "from keras.callbacks import EarlyStopping\n", + "\n", + "model_input = Input(shape=(None, 1))\n", + "[normalized_input, denormalization_coeff] = ES(HORIZON, m, BATCH_SIZE, T)(model_input)\n", + "gru_out = GRU(LATENT_DIM)(normalized_input)\n", + "model_output_normalized = Dense(HORIZON)(gru_out)\n", + "model_output = Denormalization()([model_output_normalized, denormalization_coeff])\n", + "model = Model(inputs=model_input, outputs=model_output)\n", + "\n", + "model.compile(optimizer='RMSprop', loss='mse')\n", + "\n", "model.summary()" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=20)" + "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": { "scrolled": false }, @@ -710,48 +676,88 @@ "output_type": "stream", "text": [ "Train on 23328 samples, validate on 1488 samples\n", - "Epoch 1/10\n", - "23328/23328 [==============================] - 8s 341us/step - loss: 0.0324 - val_loss: 0.0194\n", - "Epoch 2/10\n", - "23328/23328 [==============================] - 4s 153us/step - loss: 0.0029 - val_loss: 0.0184\n", - "Epoch 3/10\n", - "23328/23328 [==============================] - 4s 156us/step - loss: 0.0023 - val_loss: 0.0163\n", - "Epoch 4/10\n", - "23328/23328 [==============================] - 4s 155us/step - loss: 0.0019 - val_loss: 0.0140\n", - "Epoch 5/10\n", - "23328/23328 [==============================] - 4s 157us/step - loss: 0.0016 - val_loss: 0.0120\n", - "Epoch 6/10\n", - "23328/23328 [==============================] - 4s 154us/step - loss: 0.0015 - val_loss: 0.0103\n", - "Epoch 7/10\n", - "23328/23328 [==============================] - 4s 154us/step - loss: 0.0014 - val_loss: 0.0091\n", - "Epoch 8/10\n", - "23328/23328 [==============================] - 4s 156us/step - loss: 0.0013 - val_loss: 0.0081\n", - "Epoch 9/10\n", - "23328/23328 [==============================] - 4s 154us/step - loss: 0.0012 - val_loss: 0.0074\n", - "Epoch 10/10\n", - "23328/23328 [==============================] - 4s 151us/step - loss: 0.0012 - val_loss: 0.0068\n" + "Epoch 1/20\n", + "23328/23328 [==============================] - 7s 307us/step - loss: 2.0763 - val_loss: 1.8113\n", + "Epoch 2/20\n", + "23328/23328 [==============================] - 4s 153us/step - loss: 4.8601 - val_loss: 0.8136\n", + "Epoch 3/20\n", + "23328/23328 [==============================] - 3s 142us/step - loss: 11.0271 - val_loss: 1.9408\n", + "Epoch 4/20\n", + "23328/23328 [==============================] - 3s 143us/step - loss: 1.2623 - val_loss: 0.8588\n", + "Epoch 5/20\n", + "23328/23328 [==============================] - 4s 160us/step - loss: 97.8346 - val_loss: 2.6712\n", + "Epoch 6/20\n", + "23328/23328 [==============================] - 4s 161us/step - loss: 18.8151 - val_loss: 0.6894\n", + "Epoch 7/20\n", + "23328/23328 [==============================] - 4s 161us/step - loss: 1.3860 - val_loss: 1.9493\n", + "Epoch 8/20\n", + "23328/23328 [==============================] - 4s 156us/step - loss: 0.5103 - val_loss: 4.4826\n", + "Epoch 9/20\n", + "23328/23328 [==============================] - 4s 165us/step - loss: 0.9354 - val_loss: 1.0524\n", + "Epoch 10/20\n", + "23328/23328 [==============================] - 4s 152us/step - loss: 2.3594 - val_loss: 0.3860\n", + "Epoch 11/20\n", + "23328/23328 [==============================] - 4s 151us/step - loss: 0.3068 - val_loss: 0.5479\n", + "Epoch 12/20\n", + "23328/23328 [==============================] - 4s 154us/step - loss: 0.2905 - val_loss: 4.2013\n", + "Epoch 13/20\n", + "23328/23328 [==============================] - 4s 165us/step - loss: 0.2622 - val_loss: 0.3126\n", + "Epoch 14/20\n", + "23328/23328 [==============================] - 4s 172us/step - loss: 9.4153 - val_loss: 0.3014\n", + "Epoch 15/20\n", + "23328/23328 [==============================] - 4s 163us/step - loss: 0.2758 - val_loss: 0.3793\n", + "Epoch 16/20\n", + "23328/23328 [==============================] - 4s 162us/step - loss: 0.2449 - val_loss: 0.2848\n", + "Epoch 17/20\n", + "23328/23328 [==============================] - 4s 162us/step - loss: 0.2313 - val_loss: 0.2628\n", + "Epoch 18/20\n", + "23328/23328 [==============================] - 4s 162us/step - loss: 7.9480 - val_loss: 0.2735\n", + "Epoch 19/20\n", + "23328/23328 [==============================] - 4s 160us/step - loss: 0.1915 - val_loss: 0.3899\n", + "Epoch 20/20\n", + "23328/23328 [==============================] - 4s 156us/step - loss: 0.3765 - val_loss: 0.3595\n", + "CPU times: user 2min 32s, sys: 40.3 s, total: 3min 12s\n", + "Wall time: 1min 20s\n" ] - }, + } + ], + "source": [ + "%%time\n", + "history = model.fit(\n", + " train_inputs['X'],\n", + " train_inputs['target'],\n", + " batch_size=BATCH_SIZE,\n", + " shuffle=False,\n", + " epochs=EPOCHS,\n", + " validation_data=(valid_inputs['X'], valid_inputs['target']),\n", + " callbacks=[earlystop],\n", + " verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { "data": { + "image/png": "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\n", "text/plain": [ - "" + "
" ] }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "model.fit(train_inputs['X'],\n", - " train_inputs['target'],\n", - " batch_size=BATCH_SIZE,\n", - " shuffle=False,\n", - " epochs=EPOCHS,\n", - " validation_data=(valid_inputs['X'], valid_inputs['target']),\n", - " callbacks=[earlystop],\n", - " verbose=1)" + "plot_df = pd.DataFrame.from_dict({'train_loss':history.history['loss'], 'val_loss':history.history['val_loss']})\n", + "plot_df.plot(logy=True, figsize=(10,10), fontsize=12)\n", + "plt.xlabel('epoch', fontsize=12)\n", + "plt.ylabel('loss', fontsize=12)\n", + "plt.show()" ] }, { @@ -763,19 +769,35 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", "test = energy.copy()[test_start_dt:test_end_dt][['load']]\n", "test[['load']] = scaler.transform(test)\n", "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check that the test set lenght is a multiple of BATCH_SIZE" + ] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "n = test_inputs['X'].shape[0]\n", + "assert n % BATCH_SIZE == 0, f'Test set size, {n:,} is not a multiple of batch size ({BATCH_SIZE})'" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -784,22 +806,22 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.54, 0.69, 0.72],\n", - " [0.48, 0.61, 0.63],\n", - " [0.46, 0.55, 0.57],\n", + "array([[-1.31, -1.23, -0.97],\n", + " [-1.12, -1.03, -0.83],\n", + " [-0.67, -0.65, -0.57],\n", " ...,\n", - " [0.58, 0.59, 0.68],\n", - " [0.6 , 0.69, 0.77],\n", - " [0.69, 0.78, 0.76]], dtype=float32)" + " [-0.35, -0.58, -0.7 ],\n", + " [-0.61, -0.71, -0.69],\n", + " [-0.59, -0.51, -0.38]], dtype=float32)" ] }, - "execution_count": 23, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -808,9 +830,16 @@ "predictions" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Combine predictions with actual values, then unpivot for easier analysis. " + ] + }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -843,38 +872,38 @@ " \n", " \n", " 0\n", - " 2014-10-31 16:00:00\n", + " 2014-11-01 02:00:00\n", " t+1\n", - " 3,717.48\n", - " 3,437.00\n", + " 2,561.88\n", + " 2,382.00\n", " \n", " \n", " 1\n", - " 2014-10-31 17:00:00\n", + " 2014-11-01 03:00:00\n", " t+1\n", - " 3,543.91\n", - " 3,466.00\n", + " 2,671.57\n", + " 2,419.00\n", " \n", " \n", " 2\n", - " 2014-10-31 18:00:00\n", + " 2014-11-01 04:00:00\n", " t+1\n", - " 3,483.05\n", - " 3,374.00\n", + " 2,928.42\n", + " 2,520.00\n", " \n", " \n", " 3\n", - " 2014-10-31 19:00:00\n", + " 2014-11-01 05:00:00\n", " t+1\n", - " 3,409.18\n", - " 3,315.00\n", + " 3,157.77\n", + " 2,714.00\n", " \n", " \n", " 4\n", - " 2014-10-31 20:00:00\n", + " 2014-11-01 06:00:00\n", " t+1\n", - " 3,340.66\n", - " 3,142.00\n", + " 3,295.44\n", + " 2,970.00\n", " \n", " \n", "\n", @@ -882,14 +911,14 @@ ], "text/plain": [ " timestamp h prediction actual\n", - "0 2014-10-31 16:00:00 t+1 3,717.48 3,437.00\n", - "1 2014-10-31 17:00:00 t+1 3,543.91 3,466.00\n", - "2 2014-10-31 18:00:00 t+1 3,483.05 3,374.00\n", - "3 2014-10-31 19:00:00 t+1 3,409.18 3,315.00\n", - "4 2014-10-31 20:00:00 t+1 3,340.66 3,142.00" + "0 2014-11-01 02:00:00 t+1 2,561.88 2,382.00\n", + "1 2014-11-01 03:00:00 t+1 2,671.57 2,419.00\n", + "2 2014-11-01 04:00:00 t+1 2,928.42 2,520.00\n", + "3 2014-11-01 05:00:00 t+1 3,157.77 2,714.00\n", + "4 2014-11-01 06:00:00 t+1 3,295.44 2,970.00" ] }, - "execution_count": 24, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -908,20 +937,20 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "h\n", - "t+1 0.03\n", - "t+2 0.08\n", - "t+3 0.11\n", + "t+1 0.04\n", + "t+2 0.06\n", + "t+3 0.08\n", "Name: APE, dtype: float64" ] }, - "execution_count": 25, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -940,22 +969,21 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "0.07506279816956267" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE: 5.75%\n" + ] } ], "source": [ - "mape(eval_df['prediction'], eval_df['actual'])" + "from common.utils import mape\n", + "\n", + "print(\"MAPE: {:.2f}%\".format(100* mape(eval_df['prediction'], eval_df['actual'])))" ] }, { @@ -967,12 +995,19 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -999,13 +1034,20 @@ "ax.legend(loc='best')\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python (dlts)", + "display_name": "Python 3.6", "language": "python", - "name": "dlts" + "name": "dftf2" }, "language_info": { "codemirror_mode": { @@ -1017,7 +1059,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/Quiz_CNN.ipynb b/Quiz_1_CNN.ipynb similarity index 74% rename from Quiz_CNN.ipynb rename to Quiz_1_CNN.ipynb index 76f2277..303a493 100644 --- a/Quiz_CNN.ipynb +++ b/Quiz_1_CNN.ipynb @@ -4,14 +4,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Dilated CNN model\n", + "# Practice: Dilated CNN model\n", "\n", - "In this notebook, we demonstrate how to:\n", - "- prepare time series data for training a Convolutional Neural Network (CNN) forecasting model\n", - "- get data in the required shape for the keras API\n", - "- implement a CNN model in keras to predict 3 steps ahead (time *t+1* to *t+1*) in the time series\n", - "- enable early stopping to reduce the likelihood of model overfitting\n", - "- evaluate the model on a test dataset\n", + "In this notebook, we work through implementing a CNN for forecasting. The notebook covers:\n", + "- preparing time series data for training a Convolutional Neural Network (CNN) forecasting model\n", + "- getting data in the required shape for the keras API\n", + "- implementing a CNN model in keras to predict 3 steps ahead (time *t+1* to *t+1*) in the time series\n", + "- enabling early stopping to reduce the likelihood of model overfitting\n", + "- evaluating the model on a test dataset\n", "\n", "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load data only.\n", "\n", @@ -24,29 +24,27 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", - "import warnings\n", "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import datetime as dt\n", - "from collections import UserDict\n", - "from IPython.display import Image\n", - "from sklearn.preprocessing import MinMaxScaler\n", "%matplotlib inline\n", "\n", - "from common.utils import load_data, mape, TimeSeriesTensor, create_evaluation_df\n", - "\n", + "import pandas as pd\n", "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "import numpy as np\n", "np.set_printoptions(precision=2)\n", - "warnings.filterwarnings(\"ignore\")" + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import tensorflow as tf\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Load the data from csv into a Pandas dataframe" + "Load the data from `data/energy.parquet` into a Pandas dataframe" ] }, { @@ -55,8 +53,14 @@ "metadata": {}, "outputs": [], "source": [ - "data_dir = 'data/'\n", - "energy = load_data(data_dir)\n", + "import os\n", + "\n", + "# Insert code START\n", + "file_name = None\n", + "energy = None\n", + "# Insert code END\n", + "\n", + "assert energy.shape == (26304, 2)\n", "energy.head()" ] }, @@ -64,11 +68,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Create train, validation and test sets\n", - "\n", - "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", - "\n", - "We will allocate the period 1st November 2014 to 31st December 2014 to the test set. The period 1st September 2014 to 31st October is allocated to validation set. All other time periods are available for the training set." + "Plot energy load and temperature in first week of July 2014" ] }, { @@ -77,10 +77,7 @@ "metadata": {}, "outputs": [], "source": [ - "valid_start_dt = '2014-09-01 00:00:00'\n", - "test_start_dt = '2014-11-01 00:00:00'\n", - "\n", - "energy.plot(y=['load', 'temp'], subplots=True, figsize=(15, 8), fontsize=12)\n", + "energy['2014-07-01':'2014-07-07'].plot(y=['load', 'temp'], subplots=True, figsize=(15, 8), fontsize=12)\n", "plt.show()" ] }, @@ -88,7 +85,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Load and temperature in first week of July 2014" + "## Create train, validation and test sets\n", + "\n", + "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", + "\n", + "Allocate the period 1st November 2014 to 31st December 2014 to the test set, and the period 1st September 2014 to 31st October to the validation set. All other time periods are designated for the training set." ] }, { @@ -97,8 +98,19 @@ "metadata": {}, "outputs": [], "source": [ - "energy['2014-07-01':'2014-07-07'].plot(y=['load', 'temp'], subplots=True, figsize=(15, 8), fontsize=12)\n", - "plt.show()" + "# Insert code START\n", + "valid_start_dt = None\n", + "test_start_dt = None\n", + "# Insert code END\n", + "\n", + "train = energy.copy()[:valid_start_dt]\n", + "valid = energy.copy()[valid_start_dt:test_start_dt]\n", + "test = energy.copy()[test_start_dt:]\n", + "\n", + "assert train.index.max() == pd.to_datetime('2014-08-31 23:00:00')\n", + "assert valid.index.min() == pd.to_datetime('2014-09-01 00:00:00')\n", + "assert valid.index.max() == pd.to_datetime('2014-10-31 23:00:00')\n", + "assert test.index.min() == pd.to_datetime('2014-11-01 00:00:00')" ] }, { @@ -135,16 +147,17 @@ "metadata": {}, "outputs": [], "source": [ - "# Create training dataset with load and temp features\n", - "train = energy.copy()[energy.index < valid_start_dt][['load', 'temp']]\n", + "from sklearn.preprocessing import MinMaxScaler\n", "\n", - "# Fit a scaler for the y values\n", + "# Fit a scaler for the y values (to use to unscale predictions)\n", "y_scaler = MinMaxScaler()\n", "y_scaler.fit(train[['load']])\n", "\n", "# Also scale the input features data (load and temp values)\n", "X_scaler = MinMaxScaler()\n", - "train[['load', 'temp']] = X_scaler.fit_transform(train)" + "train[['load', 'temp']] = X_scaler.fit_transform(train)\n", + "valid[['load', 'temp']] = X_scaler.transform(valid)\n", + "test[['load', 'temp']] = X_scaler.transform(test)" ] }, { @@ -171,6 +184,8 @@ "metadata": {}, "outputs": [], "source": [ + "from common.utils import TimeSeriesTensor\n", + "\n", "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n", "train_inputs = TimeSeriesTensor(dataset=train,\n", " target='load',\n", @@ -178,64 +193,21 @@ " tensor_structure=tensor_structure,\n", " freq='H',\n", " drop_incomplete=True)\n", - "train_inputs.dataframe.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "\n", + "\n", "X_train = train_inputs['X']\n", - "y_train = train_inputs['target']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_train.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_train[:3]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "X_train.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "X_train[:3]" + "y_train = train_inputs['target']\n", + "\n", + "assert y_train.shape == (23350, 3)\n", + "assert X_train.shape == (23350, 24, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Data preparation - validation set" + "#### Data preparation - validation set\n", + "Create a `TimeSeriesTensor` from the validation dataset." ] }, { @@ -244,37 +216,23 @@ "metadata": {}, "outputs": [], "source": [ - "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", - "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load', 'temp']]\n", - "valid[['load', 'temp']] = X_scaler.transform(valid)\n", + "# In order to allow T lags, we need to add the last T-1 train samples to the validation set\n", + "valid = pd.concat([train.iloc[-(T-1):], valid])\n", + "\n", + "# Create TimeSeriesTensor\n", "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)\n", "y_valid = valid_inputs['target']\n", - "X_valid = valid_inputs['X']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "y_valid.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_valid.shape" + "X_valid = valid_inputs['X']\n", + "\n", + "assert y_valid.shape == (1461, 3)\n", + "assert X_valid.shape == (1461, 24, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Quiz: Implement multivariate CNN" + "## Implement a multivariate CNN" ] }, { @@ -313,10 +271,12 @@ "metadata": {}, "outputs": [], "source": [ - "LATENT_DIM = ?\n", - "KERNEL_SIZE = ?\n", - "BATCH_SIZE = ?\n", - "EPOCHS = ?" + "# Insert code START\n", + "LATENT_DIM = None\n", + "KERNEL_SIZE = None\n", + "BATCH_SIZE = None\n", + "EPOCHS = None\n", + "# Insert code END" ] }, { @@ -325,16 +285,11 @@ "metadata": {}, "outputs": [], "source": [ - "# Fill in your code to replace the question mark\n", - "# Hint: there is a parameter you need to add when stacking multiple RNN layers\n", + "# Fill in your code below\n", "model = Sequential()\n", - "?\n", - "?\n", - "?\n", - "?\n", - "?\n", - "?\n", - "?" + "# Insert code START\n", + "# Hint: 7 lines of model.add(...)\n", + "# Insert code END" ] }, { @@ -411,12 +366,16 @@ "metadata": {}, "outputs": [], "source": [ - "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", - "test = energy.copy()[test_start_dt:][['load', 'temp']]\n", - "test[['load', 'temp']] = X_scaler.transform(test)\n", + "# In order to allow T lags, we need to add the last T-1 validation samples to the test set\n", + "test = pd.concat([valid.iloc[-(T-1):], test])\n", + "\n", + "# Create TimeSeriesTensor\n", "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)\n", + "y_test = test_inputs['target']\n", "X_test = test_inputs['X']\n", - "y_test = test_inputs['target']" + "\n", + "assert y_test.shape == (1461, 3)\n", + "assert X_test.shape == (1461, 24, 2)" ] }, { @@ -425,11 +384,23 @@ "metadata": {}, "outputs": [], "source": [ + "from common.utils import create_evaluation_df\n", + "\n", + "# Make predictions\n", "predictions = model.predict(X_test)\n", + "\n", + "# Package for evaluation\n", "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n", "eval_df.head()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate MAPE for each horizon" + ] + }, { "cell_type": "code", "execution_count": null, @@ -437,7 +408,7 @@ "outputs": [], "source": [ "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n", - "eval_df.groupby('h')['APE'].mean()" + "eval_df.groupby('h')['APE'].mean() * 100" ] }, { @@ -461,13 +432,20 @@ "ax.legend(loc='best')\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "dnntutorial", + "display_name": "Python 3.6 (dltf)", "language": "python", - "name": "dnntutorial" + "name": "dltf" }, "language_info": { "codemirror_mode": { @@ -479,7 +457,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/Quiz_2_RNN.ipynb b/Quiz_2_RNN.ipynb new file mode 100644 index 0000000..f74ee8b --- /dev/null +++ b/Quiz_2_RNN.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Practice: One step multivariate RNN model\n", + "\n", + "In this notebook, we work through:\n", + "- preparing the time series data for training a RNN forecasting model\n", + "- get data in the required shape for the keras API\n", + "- implement a RNN model in keras to predict the next step ahead (time *t+1*) in the time series. This model uses recent values of temperature, as well as load, as the model input.\n", + "- enable early stopping to reduce the likelihood of model overfitting\n", + "- evaluate the model on a test dataset\n", + "\n", + "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load and temperature data.\n", + "\n", + "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import tensorflow as tf\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data from `data/energy.parquet` into a Pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Insert code START\n", + "file_name = None\n", + "energy = None\n", + "# Insert code END\n", + "assert energy.shape == (26304, 2)\n", + "energy.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create train, validation and test sets\n", + "\n", + "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", + "\n", + "We will allocate the period 1st November 2014 to 31st December 2014 to the test set. The period 1st September 2014 to 31st October is allocated to validation set. All other time periods are available for the training set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Insert code START\n", + "valid_start_dt = None\n", + "test_start_dt = None\n", + "# Insert code END\n", + "\n", + "train = energy.copy()[:valid_start_dt]\n", + "valid = energy.copy()[valid_start_dt:test_start_dt]\n", + "test = energy.copy()[test_start_dt:]\n", + "\n", + "assert train.index.max() == pd.to_datetime('2014-08-31 23:00:00')\n", + "assert valid.index.min() == pd.to_datetime('2014-09-01 00:00:00')\n", + "assert valid.index.max() == pd.to_datetime('2014-10-31 23:00:00')\n", + "assert test.index.min() == pd.to_datetime('2014-11-01 00:00:00')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preparation\n", + "\n", + "For this example, we will set *T=6*. This means that the input for each sample is a vector of the prevous 6 hours of the energy load. The choice of *T=6* was arbitrary but should be selected through experimentation.\n", + "\n", + "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "T = 6\n", + "HORIZON = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data preparation - training set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# Fit a scaler for the y values\n", + "y_scaler = MinMaxScaler()\n", + "y_scaler.fit(train[['load']])\n", + "\n", + "# Also scale the input features data (load and temp values)\n", + "X_scaler = MinMaxScaler()\n", + "train[['load', 'temp']] = X_scaler.fit_transform(train)\n", + "valid[['load', 'temp']] = X_scaler.transform(valid)\n", + "test[['load', 'temp']] = X_scaler.transform(test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the TimeSeriesTensor convenience class to:\n", + "1. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n", + "2. Discard any samples with missing values\n", + "3. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras\n", + "\n", + "The class takes the following parameters:\n", + "\n", + "- **dataset**: original time series\n", + "- **H**: the forecast horizon\n", + "- **tensor_structure**: a dictionary discribing the tensor structure in the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) }\n", + "- **freq**: time series frequency\n", + "- **drop_incomplete**: (Boolean) whether to drop incomplete samples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from common.utils import TimeSeriesTensor\n", + "\n", + "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n", + "train_inputs = TimeSeriesTensor(dataset=train,\n", + " target='load',\n", + " H=HORIZON,\n", + " tensor_structure=tensor_structure,\n", + " freq='H',\n", + " drop_incomplete=True)\n", + "\n", + "\n", + "X_train = train_inputs['X']\n", + "y_train = train_inputs['target']\n", + "\n", + "assert y_train.shape == (23370, 1)\n", + "assert X_train.shape == (23370, 6, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data preparation - validation set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to allow T lags, we need to add the last T-1 train samples to the validation set\n", + "valid = pd.concat([train.iloc[-(T-1):], valid])\n", + "\n", + "# Create TimeSeriesTensor\n", + "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)\n", + "y_valid = valid_inputs['target']\n", + "X_valid = valid_inputs['X']\n", + "\n", + "assert y_valid.shape == (1463, 1)\n", + "assert X_valid.shape == (1463, 6, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quiz: Implement multivariate RNN" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Multivariate, multilayer RNN](./images/one_step_RNN_multivariate_mutilayer.png)\n", + "\n", + "Implement your RNN model with the data prepared above and the following requirements:\n", + "1. Use 2 features: past load and temperature\n", + "2. Stack 2 GRU layers\n", + "3. 6 hidden units in the first GRU layer\n", + "4. 4 hidden units in the second GRU layer\n", + "5. 5 epochs\n", + "6. Batch size 32" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Insert code START\n", + "FIRST_LAYER_LATENT_DIM = None # number of units in the 1st RNN layer\n", + "SECOND_LAYER_LATENT_DIM = None # number of units in the 2nd RNN layer\n", + "BATCH_SIZE = None # number of samples per mini-batch\n", + "EPOCHS = None # maximum number of times the training algorithm will cycle through all samples\n", + "# Insert code END" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.models import Model, Sequential\n", + "from keras.layers import GRU, Dense\n", + "from keras.callbacks import EarlyStopping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fill in your code below and replace the question mark" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "# Fill in your code\n", + "# Hint: 3 model.add() statements.\n", + "# There is a parameter you need to add when stacking multiple RNN layers\n", + "\n", + "# Insert code START\n", + "# Insert code END" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you done, run the rest of the notebook to check if your model works." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer='RMSprop', loss='mse')\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specify the early stopping criteria. We **monitor** the validation loss (in this case the mean squared error) on the validation set after each training epoch. If the validation loss has not improved by **min_delta** after **patience** epochs, we stop the training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)\n", + "\n", + "history = model.fit(X_train,\n", + " y_train,\n", + " batch_size=BATCH_SIZE,\n", + " epochs=EPOCHS,\n", + " validation_data=(X_valid, y_valid),\n", + " callbacks=[earlystop],\n", + " verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_df = pd.DataFrame.from_dict({'train_loss':history.history['loss'], 'val_loss':history.history['val_loss']})\n", + "plot_df.plot(logy=True, figsize=(10,8), fontsize=12)\n", + "plt.xlabel('epoch', fontsize=12)\n", + "plt.ylabel('loss', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the test set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to allow T lags, we need to add the last T-1 validation samples to the test set\n", + "test = pd.concat([valid.iloc[-(T-1):], test])\n", + "\n", + "# Create TimeSeriesTensor\n", + "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)\n", + "y_test = test_inputs['target']\n", + "X_test = test_inputs['X']\n", + "\n", + "assert y_test.shape == (1463, 1)\n", + "assert X_test.shape == (1463, 6, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from common.utils import create_evaluation_df\n", + "\n", + "predictions = model.predict(X_test)\n", + "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n", + "eval_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from common.utils import mape\n", + "\n", + "print('MAPE: {:.2f}%'.format(100 * mape(eval_df['prediction'], eval_df['actual'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eval_df[eval_df.timestamp<'2014-11-08'].plot(x='timestamp', y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8), fontsize=12)\n", + "plt.xlabel('timestamp', fontsize=12)\n", + "plt.ylabel('load', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Quiz_3_RNN_encoder_decoder.ipynb b/Quiz_3_RNN_encoder_decoder.ipynb new file mode 100644 index 0000000..603a8bb --- /dev/null +++ b/Quiz_3_RNN_encoder_decoder.ipynb @@ -0,0 +1,454 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Practice: Multi step model (simple encoder-decoder)\n", + "\n", + "In this notebook, we walk through:\n", + "- preparing the time series data for training a RNN forecasting model\n", + "- getting data in the required shape for the keras API\n", + "- implement a RNN model in keras to predict the next 3 steps ahead (time *t+1* to *t+3*) in the time series. This model uses a simple encoder decoder approach in which the final hidden state of the encoder is replicated across each time step of the decoder. \n", + "- enable early stopping to reduce the likelihood of model overfitting\n", + "- evaluate the model on a test dataset\n", + "\n", + "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load.\n", + "\n", + "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import tensorflow as tf\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load data into Pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Insert code START\n", + "file_name = None\n", + "energy = None\n", + "# Insert code END\n", + "assert energy.shape == (26304, 2)\n", + "energy.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create train, validation and test sets\n", + "\n", + "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", + "\n", + "We will allocate the period 1st November 2014 to 31st December 2014 to the test set. The period 1st September 2014 to 31st October is allocated to validation set. All other time periods are available for the training set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Insert code START\n", + "valid_start_dt = None\n", + "test_start_dt = None\n", + "# Insert code END\n", + "\n", + "train = energy.copy()[:valid_start_dt]\n", + "valid = energy.copy()[valid_start_dt:test_start_dt]\n", + "test = energy.copy()[test_start_dt:]\n", + "\n", + "assert train.index.max() == pd.to_datetime('2014-08-31 23:00:00')\n", + "assert valid.index.min() == pd.to_datetime('2014-09-01 00:00:00')\n", + "assert valid.index.max() == pd.to_datetime('2014-10-31 23:00:00')\n", + "assert test.index.min() == pd.to_datetime('2014-11-01 00:00:00')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preparation\n", + "\n", + "For this example, we will set *T=6*. This means that the input for each sample is a vector of the prevous 6 hours of the energy load. The choice of *T=6* was arbitrary but should be selected through experimentation.\n", + "\n", + "*HORIZON=1* specifies that we have a forecasting horizon of 3 (*t+1*, *t+2*, *t+3*)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "T = 6\n", + "HORIZON = 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create training set:\n", + "\n", + "1. Scale (This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data.)\n", + "2. Create TiemSeriesTensor " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# Fit a scaler for the y values\n", + "y_scaler = MinMaxScaler()\n", + "y_scaler.fit(train[['load']])\n", + "\n", + "# Also scale the input features data (load and temp values)\n", + "X_scaler = MinMaxScaler()\n", + "train[['load', 'temp']] = X_scaler.fit_transform(train)\n", + "valid[['load', 'temp']] = X_scaler.transform(valid)\n", + "test[['load', 'temp']] = X_scaler.transform(test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from common.utils import TimeSeriesTensor\n", + "\n", + "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n", + "train_inputs = TimeSeriesTensor(dataset=train,\n", + " target='load',\n", + " H=HORIZON,\n", + " tensor_structure=tensor_structure,\n", + " freq='H',\n", + " drop_incomplete=True)\n", + "\n", + "\n", + "X_train = train_inputs['X']\n", + "y_train = train_inputs['target']\n", + "\n", + "assert y_train.shape == (23368, 3)\n", + "assert X_train.shape == (23368, 6, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Construct validation set \n", + "(keeping T hours from the training set in order to construct initial features)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to allow T lags, we need to add the last T-1 train samples to the validation set\n", + "valid = pd.concat([train.iloc[-(T-1):], valid])\n", + "\n", + "# Create TimeSeriesTensor\n", + "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)\n", + "y_valid = valid_inputs['target']\n", + "X_valid = valid_inputs['X']\n", + "\n", + "assert y_valid.shape == (1461, 3)\n", + "assert X_valid.shape == (1461, 6, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quiz: Implement an encoder-decoder RNN" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Encoder-Decoder](./images/encoder_decoder_multilayer.png)\n", + "\n", + "Implement your RNN model with the data prepared above and the following requirements:\n", + "1. Use 2 features: past load and temperature\n", + "2. Stack 2 **LSTM** layers for the encoder RNN\n", + "3. 12 hidden units in the first LSTM layer\n", + "4. 6 hidden units in the second LSTM layer\n", + "5. Repeat the vector output of the second encoder layer 3 times (one for each time step in the forecast horizon)\n", + "5. Add a decoder LSTM with 6 hidden units\n", + "6. 5 epochs\n", + "7. Batch size 32\n", + "\n", + "The model will have the following structure:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.models import Model, Sequential\n", + "from keras.layers import LSTM, Dense, RepeatVector, TimeDistributed, Flatten\n", + "from keras.callbacks import EarlyStopping" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Insert code BEGIN\n", + "ENCODER_LAYER_1_DIM = None\n", + "ENCODER_LAYER_2_DIM = None\n", + "DECODER_DIM = None\n", + "BATCH_SIZE = None\n", + "EPOCHS = None\n", + "# Insert code END" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "# Insert code BEGIN\n", + "# Hint: 5 model.add() statements. Use LSTM, RepeatVector and TimeDistributed\n", + "# Insert code END\n", + "model.add(Flatten())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer='RMSprop', loss='mse')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "history = model.fit(\n", + " train_inputs['X'],\n", + " train_inputs['target'],\n", + " batch_size=BATCH_SIZE,\n", + " epochs=EPOCHS,\n", + " validation_data=(valid_inputs['X'], valid_inputs['target']),\n", + " callbacks=[earlystop],\n", + " verbose=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_df = pd.DataFrame.from_dict({'train_loss':history.history['loss'], 'val_loss':history.history['val_loss']})\n", + "plot_df.plot(logy=True, figsize=(10,8), fontsize=12)\n", + "plt.xlabel('epoch', fontsize=12)\n", + "plt.ylabel('loss', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to allow T lags, we need to add the last T-1 validation samples to the test set\n", + "test = pd.concat([valid.iloc[-(T-1):], test])\n", + "\n", + "# Create TimeSeriesTensor\n", + "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)\n", + "y_test = test_inputs['target']\n", + "X_test = test_inputs['X']\n", + "\n", + "assert y_test.shape == (1461, 3)\n", + "assert X_test.shape == (1461, 6, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = model.predict(test_inputs['X'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from common.utils import create_evaluation_df\n", + "\n", + "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n", + "eval_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n", + "eval_df.groupby('h')['APE'].mean() * 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from common.utils import mape\n", + "\n", + "print('MAPE: {:.2f}%'.format(100 * mape(eval_df['prediction'], eval_df['actual'])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot actuals vs predictions at each horizon for first week of the test period. As is to be expected, predictions for one step ahead (*t+1*) are more accurate than those for 2 or 3 steps ahead" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_df = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+1')][['timestamp', 'actual']]\n", + "for t in range(1, HORIZON+1):\n", + " plot_df['t+'+str(t)] = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+'+str(t))]['prediction'].values\n", + "\n", + "fig = plt.figure(figsize=(15, 8))\n", + "ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(plot_df['timestamp'], plot_df['t+1'], color='blue', linewidth=4.0, alpha=0.75)\n", + "ax.plot(plot_df['timestamp'], plot_df['t+2'], color='blue', linewidth=3.0, alpha=0.5)\n", + "ax.plot(plot_df['timestamp'], plot_df['t+3'], color='blue', linewidth=2.0, alpha=0.25)\n", + "plt.xlabel('timestamp', fontsize=12)\n", + "plt.ylabel('load', fontsize=12)\n", + "ax.legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Quiz_RNN.ipynb b/Quiz_RNN.ipynb deleted file mode 100644 index 640134d..0000000 --- a/Quiz_RNN.ipynb +++ /dev/null @@ -1,831 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# One step multivariate model\n", - "\n", - "In this notebook, we demonstrate how to:\n", - "- prepare time series data for training a RNN forecasting model\n", - "- get data in the required shape for the keras API\n", - "- implement a RNN model in keras to predict the next step ahead (time *t+1*) in the time series. This model uses recent values of temperature, as well as load, as the model input.\n", - "- enable early stopping to reduce the likelihood of model overfitting\n", - "- evaluate the model on a test dataset\n", - "\n", - "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load and temperature data.\n", - "\n", - "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import datetime as dt\n", - "from collections import UserDict\n", - "from IPython.display import Image\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "%matplotlib inline\n", - "\n", - "from common.utils import load_data, mape, TimeSeriesTensor, create_evaluation_df\n", - "\n", - "pd.options.display.float_format = '{:,.2f}'.format\n", - "np.set_printoptions(precision=2)\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load the data from csv into a Pandas dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
loadtemp
2012-01-01 00:00:002,698.0032.00
2012-01-01 01:00:002,558.0032.67
2012-01-01 02:00:002,444.0030.00
2012-01-01 03:00:002,402.0031.00
2012-01-01 04:00:002,403.0032.00
\n", - "
" - ], - "text/plain": [ - " load temp\n", - "2012-01-01 00:00:00 2,698.00 32.00\n", - "2012-01-01 01:00:00 2,558.00 32.67\n", - "2012-01-01 02:00:00 2,444.00 30.00\n", - "2012-01-01 03:00:00 2,402.00 31.00\n", - "2012-01-01 04:00:00 2,403.00 32.00" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_dir = 'data/'\n", - "energy = load_data(data_dir)\n", - "energy.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create train, validation and test sets\n", - "\n", - "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", - "\n", - "We will allocate the period 1st November 2014 to 31st December 2014 to the test set. The period 1st September 2014 to 31st October is allocated to validation set. All other time periods are available for the training set." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3wAAAHICAYAAAAV0OhoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsnXeYFEX6x7+1gRwkiQFxRREUFQRMp2AAc5bzVDw9PcOpd2c4T8VAOAExez+zgAkFAU8QlSw5wxKWuORdWNLuwsImNs3U74+Znqnpru6ununZCft+noeHnU5T090V3sw45yAIgiAIgiAIgiCSj5RYN4AgCIIgCIIgCIKIDiTwEQRBEARBEARBJCkk8BEEQRAEQRAEQSQpJPARBEEQBEEQBEEkKSTwEQRBEARBEARBJCkk8BEEQRAEQRAEQSQpJPARBEEQBEEQBEEkKSTwEQRBEARBEARBJCkk8BEEQRAEQRAEQSQpJPARBEEQBEEQBEEkKWmxboBTWrduzTMyMmLdDIIgCIIgCIIgiJiwevXqQs55G5VjE07gy8jIQGZmZqybQRAEQRAEQRAEERMYY7mqx5JLJ0EQBEEQBEEQRJJCAh9BEARBEARBEESSQgIfQRAEQRAEQRBEkpJwMXwEQRAEQRAEQRAAUF1djby8PFRUVMS6KVGhQYMGaNeuHdLT08O+Bgl8BEEQBEEQBEEkJHl5eWjatCkyMjLAGIt1c1yFc47Dhw8jLy8PZ5xxRtjXIZdOgiAIgogir07egFs/WhzrZhAEQSQlFRUVaNWqVdIJewDAGEOrVq0itl6ShY8gCIIgosjYFXti3QSCIIikJhmFPQ03fhtZ+AiCIAgiSkxZty/WTSAIgiCiTJMmTVy5zpAhQ/Duu++6ci0RsvARBEEQhMtU1ngwLzsfz4xfF+umEARBEHUcsvARBEEQhMsM/Hkjnvh+TaybQRAEQdQinHO88MILOO+883D++edjwoQJAIDS0lL06dMH3bt3x/nnn48pU6YEzhk+fDg6deqEvn37YuvWrVFpF1n4CIIgCMJlsvYei3UTCIIg6hz/+XUTNu8vdvWa557SDINv7aJ07KRJk7Bu3TpkZWWhsLAQF110EXr37o02bdpg8uTJaNasGQoLC3HppZfitttuw5o1azB+/HisXbsWNTU16N69O3r06OFq+wGy8BEEQRCE61R5vLFuAkEQBFHLLF68GPfddx9SU1PRtm1bXHnllVi1ahU453jllVdwwQUXoG/fvti3bx8OHTqERYsW4c4770SjRo3QrFkz3HbbbVFpF1n4CIIgCMJlqmpI4CMIgqhtVC1x0YJzLt0+duxYFBQUYPXq1UhPT0dGRkag1EJtZBhVsvAxxuYzxioYY6X+f1uFff0ZY7mMsTLG2M+MsZbCvpaMscn+fbmMsf6665qeSxAEQRCJitmkTxAEQSQvvXv3xoQJE+DxeFBQUICFCxfi4osvxrFjx3DiiSciPT0d8+bNQ25ubuD4yZMn4/jx4ygpKcGvv/4alXY5cen8B+e8if9fJwBgjHUB8AWABwC0BVAO4FPhnE8AVPn33Q/gM/85KucSBEEQREKSzDWhCKKuwTnH6EW7UFRWFeumEHHOnXfeiQsuuABdu3bFNddcg7fffhsnnXQS7r//fmRmZqJnz54YO3YsOnfuDADo3r077rnnHnTr1g39+vVDr169otIupqKFZIzNB/A953y0bvsbADI45/39n88EsAVAKwBeAEUAzuOcb/Pv/w7APs75AKtzOeclZm3p2bMnz8zMdPxDCYIgCKK2uOKtucgrOh6yLefNm2PUGoIgImHtniLc+elS9Ol8Ir586KJYN4fQsWXLFpxzzjmxbkZUkf1GxthqznlPlfOdWPhGMMYKGWNLGGNX+bd1AZClHcA53wmfRe9s/z+PJuz5yfKfY3cuQRAEQSQsZOAjiOTB6zeOHCknCx+RmKgmbXkJwGb4BLJ7AfzKGOsGoAkAfe7pYwCaAvBY7IPNuSEwxh4H8DgAtG/fXrHJBEEQBBEbGEjiI4hkQXPRptBcIlFRsvBxzldwzks455Wc828BLAFwE4BSAM10hzcDUGKzDwr7xe8fyTnvyTnv2aZNG5UmEwRBEETMIAsfQRAEES+EW4ePA2AANgHoqm1kjHUAUB/ANv+/NMZYR+G8rv5zYHMuQRAEQSQsJO8RRPIQiWVv/tZ8vPS/9e41hpCSzJmR3fhttgIfY+wExtj1jLEGjLE0xtj9AHoDmAlgLIBbGWO9GGONAbwOYJLfGlgGYBKA1xljjRljlwO4HcB3/kubnhvxryKIOCS/pMLR8fuPHsfm/cVRag1BEARBENHmoa9XYULm3qQWSGJNgwYNcPjw4aS8x5xzHD58GA0aNIjoOioxfOkAhgHoDF9cXjaAOzjnWwGAMfYEfMJbKwC/A3hYOPcpAF8ByAdwGMCTnPNN/h+wyeZcgkgaVucWod9nS/F/93bD7d1OVTrnD2/OBUCZ/QgiXtlZUIrnJqzDxL9dhgbpqSH7UsinkyCSjki6dZXHi/ppqfYHEo5p164d8vLyUFBQEOumRIUGDRqgXbt2EV3DVuDjnBcAMM1ByzkfB2Ccyb4jAO4I51yCSCY27/flJxq7Yo+ywEcQRHzT570FAIAlOwrR55y2MW4NUVvM2XIIBSWVuPdiSiJHqFPt4aivmiqRcER6ejrOOOOMWDcjrgk3ho8gCAdoGb5W7j6Co5TWmSAShnnZ+ThiUmz5zDaNAQAtG9erzSYRMeaRbzMxYNKGWDeDSDCS0d2QSBxI4COIWkB07yqpqIlhSwiCUOXY8Wo8/M0qdB86GxXVHsP+Jg3SAfiymOmhpR1BEEDQDdRLgwIRQ0jgIwgbPl+wE10GzYjoGimC3z+F9hBEYlBZExTyMnOKYtgSgiBiye7CsoivQRY+IpaQwEcQNrw5PRtlVUbtvhNECx8lcyCIBEFYn3klizWtJ9M6LvHZuO8Ysg9SVmRCzr9/zDJsu/vzpfhk3g7bc1OoaDsRB5DARxCKDPgp/Do6jCx8BJHQeGQCX6Av00ou0bnlo8W44b+LYt0MIoFYlVOEd2ZutT1OGyZkSiOCqC1I4CMIC7L2Hg38PX7VXsfn13i8mLnpYCBpCwAwKslMEAmBuDyTuWNRTyYIQhUS94hYQgIfQViw9VBJROdPWrsPf/tuNSavzZPuN8vYSb7+BBF7xG7o9aodF9wW/314R34pvJRJgiBcYcKqPRizLMewXXPpJAsfEUtI4CMICyJdtOUe9gV65xdXBq/p1/PN3HQQ3V6fjVU5RwznfbM0J6LvJQjCXaQxfFpsTm03xgU27y9G3/cX4LMFO2PdlIRl6Y5CAOaKO6Ju8dJPGzBoyibjDr8rAMl7RCwhgY8gLPBYaPVVSE3xdTEx/qes0pcAZsUun6Anuo1qLNt5OLIvJggiYrggyjlN2lKba7uN+46hqsbZYLU93+e9oBKDRMjpP3oF1u09im6vz8YvWftj3RwiziGBj4glJPARhAWyRA1OSNVi94TL9H1/AYDgAvLAsQrDebJvnbJuH/4xbk1E7SEIQh2x+8uGgnhIwLT3SDlu+WgxhvwqsSxY0LpJfQBA84bp0WhWnWHLAV9mT83aZ8WkNXn4ee2+aDeJiDH6dyElUIePJD4idpDARxBWuDRAyxaGHn/szJeLd5vuE3lm/Dr8tv6AK+0hYk9FtQdLdxTioETgJ+IPWR/WEjCpun7P3HTQzSYBAIorqgEAa/cYPQWsSPWvQju1bep6m+oSThbz/5qYhWcnrItyi4hYM+iXUOWLNk6QwEfEEhL4CMIKl1T4stp73MLpSybwEcnFM+PXov/oFbh0xJxYN4UwwbYXGg34ljw/0VjLK1ICCSEcjhmBtWccWCljTYlfaA4HLY7Tzv1/YqbzLM9E/LF2z1FU2zxsM8GO5D0ilpDARxAWpLi0GJIJfFblGWheSH4WbrN3ASNii6rlTnUhd7zaE0Fr5GiWOqfu55rCya0xLpF5fMzqsM9NZWpW3kFTNob9HUR8sXHfMeVjOeeBfh9NgW/vkfKEyAxMxA4S+AjCArdq5sVDrA9BEJEgU9r4kFnrZWuvaFjuh/62GUD4Fj6ZMqouUFHtweApG1FSUe1oAa/Hn5cLk9buQ42F5YfqryYPegve4dLK0AOE3Zv2F5ue5xbbDpWg19vzMGrRrqhcn0gOSOAjCAvcWgsxFxdVpMUjiNrBrqtp3Tprb/gCQ6Qs2u6zFDu18GmLzzoq72Hsij34dlkuPp67AykRmDlFQa7SIlOq+BU0hic6oe/LsKlbQj6LT7dKUAJE66nvPVIOgLJ7E9Y4EvgYYx0ZYxWMse/9n69ijHkZY6XCv78Ix7dkjE1mjJUxxnIZY/111+vv317GGPuZMdbSnZ9FEO4QTXcnq4WW1dd6vBx5ReXYd/S4620inDM3+xBKK2scn1dXF9rJhLbYf2tGtmGfVYxuNHBqPdRkjrpqedIsojVe7to4b/UERKXfp/Op9mEiox+79QK8+Fk8NFoWPq3vpwov8o78EselWojkxqmF7xMAq3Tb9nPOmwj/vtUdXwWgLYD7AXzGGOsCAP7/vwDwgH9/OYBPw/gNBBE1IrXMbbBwFbK6sv5rj1cFY3+8HLjirXm4/M25IduJ2mfvkXL89ZtMnDd4JiqiEJ9FxC9VNV4s2xXUqL8/K7Se3d4jtauQcTpUBXK21E15L+R3u+XWamW5E7/hh5V7XPk+onb4bb11jcV6aaFLabO3IFqWXU2Q1N7jgpJK9H1/IcWNEiEoC3yMsXsBHAWglFKOMdYYQD8AAznnpZzzxQB+gU/AA3wC4K+c84Wc81IAAwHcxRhL6BzRJRXVKK9yru0n4pNIFwK/bzkEILJEeJU1HkzdECzHUOMNau0GTFofwZWJSBGFPDFWg0gOxPWZfiioqAkV8D+cuwNHy6sAAIeKa7/UhtO1ZNCls25KfF8sDMY7HS6rCvz9/fJc03Pu/nwpMgZMDdkm3r55WwtMz2UhLp0OGkrEnJELQ2Pj9D0mPVUn8In1O022u4nmNapZ+LRSLeNXUWZYAPhk3g70H7U81s2IOUoCH2OsGYDXATwv2X0iY+wQY2w3Y+wDv6AHAGcD8HDOtwnHZgHo4v+7i/8zAIBzvhM+a+DZDn9DXHH+kFm49A1Ks54sxMNS6Jkf1iE9NdiS/qNWBP7Wiv4SsUGM/XGqvY2Hd4sIH5ky6FCxL3mDXdr2aMI5x6iFu7B8l008T8Cls25SUOJ7Vvo6qN8uzTE9Z1VOkeU1n/5hrek+WZzg2j1FZO1LAOxcMfVjgZk7d7TkfC1+V/aOnf3adLwxbYthe13inZlbsZTiG5UtfEMBfMk516sLsgF0A3AygGsA9ADwvn9fEwB6f7ZjAJoq7g/AGHucMZbJGMssKDDXoMWaMctyAADFFWThSxYiUX47zZoX8r3C3zM2HcQz44PFetftDRZYrvGQqjiWiBM91U5MPqzi8GRDgxYzE0ur2TPj12H4tC24d6S1RltbxKZSXYYQ9Iv792dvM1j1wkF2l+/8dClenrQh4msT0UU/z8abVVxba2glQsTWVdV4DRZKom5iK/AxxroB6AvgA/0+zvlBzvlmzrmXc74bwIsA/ujfXQqgme6UZgBKFPeL3zOSc96Tc96zTZs2dk2OGYOmbIp1EwiXcTqur9lTFHDzs9MKujFp1JCQEVNShWfo5FGUV9WgjOIv4x6rLizbVeWJ/TP9Jcs63kjDW8ctfGbon/mHc7ZH4TtCv2TE9LptgYl3nFrszcYNq/EkkvIgz07wKYQ13U1dLbVCWKNi4bsKQAaAPYyxgwD+DaAfY2yN5FiO4PyxDUAaY6yjsL8rAE0q2uT/DABgjHUAUN9/XkJi18cOl1YiY8BUGtyTlLyictz16VIM/DnyQGlVYXCPTbHVnMIyPD8xixKKRAnxMTnJwDZ57b4otIaIJvoeKet3VTVceiwRPxRXVOOBL1eY7o9WJkUrvlhAFph4xqli1VTgs/AYePL7Nbjlo8WorAl/rtbWDSTvETJUBL6RAM6Ez3WzG4DPAUwFcL2/LEN75uM0AG8CmAIAnPMyAJMAvM4Ya8wYuxzA7QC+8193LIBbGWO9/HF/rwOYxDk3WPgSBTutiuZD/MWCXZTYJQkpKvMFSm/2x9WJQ/tmh7F2Tsbrhf46XDKGT9uCn9bkYcXuI46+n3COk3ViXU2Fn2hYPdL5kgQd2oIuEezuPJC0JcYNqWWG/ro5ULtQhtnaPjNHPoZOWadmURXZf6zCskg7EV/olQAMwK6C0sBnvSAXTjZOLVTDqpajHZzctF3neJUHE1btSYrambYCH+e83O+6eZBzfhA+V8wKznkBgO4AlgEoA7AUwEYATwunPwWgIYB8AD8AeJJzvsl/3U0AnoBP8MuHL3bvKbd+WCyw62LixErxPsmH5s6lpWiurfHBqtbO7M2+LKGx0FrXNZwsnDfud+a6c+DYcbw3a2tSTDqJhNX9/qckQceew+W45r35OHjMuiTDsePVEbcNAPZTLU7HLN9tnbzBbG7+4+fLpNvFDJ9W6L023p+t7szU9/0FGPILhYzECpmC7pr3FgT+Vh2WrY7T1g2R1M4Lllohgc8thk3djJd+2oDFO8yVRImC0zp84JwP4Zz/2f/3+5zzUznnjTjnp3HO/yla6DjnRzjnd3DOG3PO23POx+muNc6/vTHn/HbOeUKbIewsfOJ+rzc2qbuJ6KM61FoWXncwXqeZaPPKxGLgJCcoc9U78zD0t83SfaWVNRjw0/pAofVwU62PW2HMzFft8ZoKGE//sBYfzd1hWdeRiD1jV+zBroIyTLBIhz5vaz66/mcWlu6MfAFhlRXSjmRcFBZXVFsmy6qo9tjWR8wvcTgvK3Z8vdJN/45YtXtHfim+0WUPXbHrcKAMCFG72HWdcKZbbR53moiNvMWiS2GpL5tvaRIkY3Qs8BHm2A0C4u6ur8/CJW/MwT7S0CYsz45fi2lCfTw9Vv76gLlbX7XHi9+35Cu3Y8G2AqmmX/x2svCpk3O43JCqXWP0ol0Yv2ovRi9yN+amssaDjq9Ox5szsgPbDhVXIGPAVMzadDDg5kOPsXYRb/fuwjLb4zUtfUW1uZZ+pd+9eu2eo6bHqKLV2wqHZLMWl1bW4IIhs0L6kJ4qBTfKag93NC+r3kV9HLXeMmg25sio8Xhxz8jleODLlcrnENFD/w6E1OEz+VuPtn60WzfoeXWyMWdAsvVtt3h2vHMFmWaoSYY7SgKfi4Rq+42vh0wgPHiMrHyJys/r9uOpscbcRdqTD3fM1TRKqnyzNMc2BTuN/845InHV0u6j7H5ycHi93DKjW7/PlmLy2jzDds3F7wfB8rfJ7/b5w8o9yD1c7v8OwgnjVuzB2xYCgB3icx4xXf06Vi77mkHe7UVZXe/jmgZ+yrrIEyLZueRasfdIuWHbjvwSSyUAAOwq9MWEcc4x7LfN2JFfanpsXpGvfVSHNTbY9TXzOnz2ndRpPxYTgFnNT4RvzeYEzjmmbzwIIDmU5iTwuYhosflswU7pEXpIE5NMRO4iVVHtsS3uK2OPZJEhvlvJMFjVNm9aZNM1u5uPjslEx1enm563OrcIz03IMmwf+tsW0+vO21rgWsxXXeOVyRvw6XzZWBxdarzmi3ttnnAjjJuS/wTRBGmr+6p6t5w8G/3Q+q+J6wzHbD6gnosur+g4Ri/ejYe/8VnvZO7fV707X72BhOtYWfRkn1XQ+rJ4qtPs2tq5x+t4Vu5XJm9wpdi8WFPbjezrsYYEvigxc9MhwzaZhY9ytyQ+GQOmYpgQ86U62Mveh0FTNkYUl+P7fo5RC3ehRBis6D2zpqCk0qCZl90zLe4q93AZMgZMNfTzudlyV9z84gpcY7FI+1VSO011MX+krAqT1hithoQbGF+Cao8X3y3LsTzLysIXcN3yH7J4eyHmb1V34Xaf5BAag27PVvde7bdaxdPp0Vv0ZaeGc4c1K94rk4OF2Z0WgH/028xA4i7CPewUqGZ7VVw6tXcvM+cIOg+cgUXbjdmA7bjug4WOz0kmxq3YEyg2b5Zd1ylF5YmvdCWBz0X0Lp01Hm9IYL5s0CcLX3IwevFugwAXTla1cKx7es54eRqGT9sSopGi9yyUOVsOYe2e4L2+aPjv6PX2vJBjZLdMez5a8hSZoCZjwKQN2KUQA2aH7Dk++f1q/GtiFvKKjFZewn2+X56LgVOs+7alwOf/X3Pv+vOXK/DQ16vcap5jdheauw4mElr/tZLVVMdBj4PxMvugvfXOSSHsFL+pknPnLpteLw+xCv2+5RAeG5Pp6BqEPcbXQ1+WIfLv0OYaJ9khrd7vnQXJ0c+dYpVAy45kq25BAl+U8HKOD+dsR/9RK7B812HM2HhQmpWTLC/Jy3ibgUY2lkSSklmPGAtYV1+z0soaqVD2yLeZuPPTpQCAZTvladqV4i2Ev49aaACt4vqcIGuRNq5UO8zuRtgjWz+VV9m7S1k+ChZc0ItkHywO1OKqTXYWRK6IiCesrC+q862FR64tWrytiOrCsarGiw15wXdAdi2rRf31/12IzgNnOHYFJIw8NiYTMzf54rf08rq94sDZWFxZ48EBfz4H7dIpOoufClZHumXpSjScKFv0JFsmYxL4ooTXG5xIP5q7HU98v1qqFabYquRhsb+YbyRP1Cr2xylVwqqzrr5nr0zagH/+sDaQAEWGGG8rWv2sHmRgGhDuq6wum9vIHmOOf1FYVeMl9y2Xkb0CZmVQgOB7YbVIC1r4Qrnhv4twxydLnDSvzhRNL6moRsaAqXhGIcue5QI5And7VWRJt1Suxzkw+JdNeOJ7MRGYscFmQ/nq3CPY7k/0ElKSBz6PBsIZszcfwt++Ww1AEqOnO1Y1hs9se7YQ46nN1VrxdLeMAlscxJEmC3d8siQiZWuyDa8k8EUJL+eBt+WARSbO5yYYA7wBYPmuw2H5bhOxw0khXTPcTMIgLnzqorxXXlUTEPRu/nCxqTAkrt81qx9gvTbUlDlHTOpghe0+4/A5iaUC3p6RjcfGZGL5LuvC0nWREEE+QtJTzadNbWFvnaVTs/DZP+w/jJiDjAFTsSO/7i3WRA4V+4SoKev2Y/uh8O+F05T3kTLw542Yl52vZCngHFimq80oe0VE5V2N8J4VlQU9DLbrMnw+8i25dbqJXdcVS27MzTbOOxNX7cVhQTEgWqG0S2vvjFvK2o5tm7hynVjy++ZDeODLFcrHr9t7FFtMXK4vfWMO/jBiDkYv2pUUCVlUIIHPRfRDutaJi4+bF2zML6mUaiTvVaixk7X3qKk7GhH/yNYAbmrsRWthXbTwnTtoZoi7mlksi5nLx6LtBdLSDCJmRZz7vLcgZNFfqeiqW1JZE3THkjRr4qq9uPD1WYFyLgUlwUWDlqmVijEbEQV5J8i6TXqa+bSpxd04SdpixX7/c/5qSY79wUmM2EW15AmFpZW44b8LpWUQRMSFdW0Pg98tz8XD36xy1VKgf7Vkygy7Mj2EM/RThH4+lb1XmtLvk3lBDxIOjoKSSrz403r0GPa79PqaQKMpIlfscuCKaRm/qn6ZeOXRMZlYtL0QN/x3IaauN6+BrMLB4grsP1aBYVO34LvluXWiRBoJfFFE68N2ddUe+GpFSHIXVW7/ZAnuG0UDezLh5sLAY2Phq/Z4I9KWJwuy2FoAKCytQvehs8OeWL5eshuAL45QK7atwkdztyNjwFRsk2gmJ2TuRVF5NWZsNLYpCebzqCIKx6rILELpCgFZVq7Z28LoczUuxYAmKqJSRltsT1m3H9kHS/CVv5/JyNp7FD2G/Y7/rc7DK5M3IOdwbOIVw40FelJS51UvbGhWfv1X6N06RTjn6D9qObl6homK8CSLxzfTA6UKY4qWnVV75zc7SNxjZcFOpvkh+2AJ/j5uDXIV+rNq4qNbP16Mb5fmhHjIJNM9A0jgiyqqY/ySHYfRf5S6mZpIDmSThptBwqK7j8zC98a0Lbj2g4UJkd3x4uG/Y/SiXcrHF1eopVAurqjGpv3WE8JIB98romkMndbQ00o9TFpjX0BadAu0KtRMABst4jidkGbh0qlhFXfzm1+B4MS9sEZywYpqj1KGSAAYuyIXv1hklI33Oo/iqKiNZdq2r3XWz+KKmoCAnH3Q17f//WMWxq3Yg2fGy0Mo3Kba4w3EdKui+j6oWJcAY7iI6K1Q5fFi6c7DePJ7o0BJ2KN3x54uUcDJ+NPnywKWu/TU4Fst8zJJtgyRbqC/JxXVzhVhZuEWBSWVGPzLpqS2jpPA5yJluuxttdVfN9ssWIlaJg59J2RNWrPHlw0uPwzLR22TX1KJYVPVC6k+bpGKPEvIhFhaYa4F1+Ccw+vljktbaEeHO3FvJeurq3jCyGIqdelMtX+gKtk2zV6nN6dnG7bJ3P6dCPivTjbW9xSvuK9I7pocL4Ssh7lkm44l/lAHfUz0Hhv3T437R0emgO08cAb+7CDWCPC9DyoKv8/n75Ru1zJKaugtQ33fXxDyXUT46G+fqGTQ+Gqx0fJc5fEaYvQAQEGHFDlJ8ND1/SOcmNw+7y2wP0i7fhLcMxES+Fxi475Q7XH2wRLH1prcw2VhvWCqmd2qarx4c3o2ShStH0R4qD7BaGTYE5+t+CrJ2lTPv3CtdrEURLywaZ+5EuR2ob+opGyurPaiwyvT8M7MrY7aELREuP+grcYW7blX1XgxeMrGkBimuozMSmaHbDh+1iTRluNrm2z/fIFxQV+t2HZ9aMB1HywwzVop/rY0BSE2loS6dPr+t2qxl3OUV9WgIIJ3f/7W/LDPtYrhNEP1lN9MXMwnZuaFfNa/u3bxyHUVr5dj75Fy3dxp/TBku4/rSmH8uDrPeJDJudLj1A4LYfshcyVQMogubo1S0zdYW2S1MKxkuGciJPC5hF7gA8xjg8y48p35hiKRE1btsa3DUq2Yyn/y2jx8vmAn3psVeTZJIjImrckLGfi1eItIhMCsvUdx/pBZ0n0yl85oCCJxg8VPE++xyv0+XOYb/L9dmuOoCZqrmVMLX6RPRftN0zcewLfLcjHcgWU0mQlnES7DLaWvk0RKKrW4PF59tELGAAAgAElEQVRuCA3YdqgUVQrxf5HUqnKbA8eOW3qtaPctRehY+gU65xznDprpWEkj8tDXq6SxsuGgcne9nCsdt6tQLQ4x2awT0eKtmdno9fa8kCQqdrdO1ndV77ZmldKedX6Je8lC9NlZkw39OBXuKy6LjRV5b1b440Y8QwKfS8jmy6VhZNDUuwK99NMGdHhlWrjNCkErzKyyACCsiXQy1RcDv/WjxQAiE/j0sWjczsSXxLi5fC0s9WnGw5UXnFr6VSZtLTOh7NraY9cWJYH/vdxREd9kI5wal/GS3VZFWD1oomA0Pze4XcVNtba4bMRc3PThopBt0ozGwt/63/jN0lxX2vKESzFuj1q4mGtMXmsfsyvD7OlavTJaJmBaCwCT/bHSYpIVu94m288d3koO3zrg4uFzAtl9Q/YLX1JZ4zHsd0qcDGURoX9fZb+potq9e5UM90zEkcDHGOvIGKtgjH0vbOvPGMtljJUxxn5mjLUU9rVkjE3278tljPXXXc/0XELOmj1FBlcT/Uu5cFuBNEOUyJwth9Bz2O+udI66iNVAsD7PeXIITWsbidVN788uftIvXFfnFmFljoN0z3GC21prJ7JYuIv/aATfj/bHh8juh9lv6vv+AnQeOMP9xiQINWHE8N3usBC6Ixw0x8s5Jq7aG5LhU/XdNXtvQ/RBcb6wkSk2xG2Hde6KiZCIKtpYxTd9qygQj160C1sVEwMlKrJ+pB9X9YfI+tS+o4pxsMKpa/wlNdbnGWN+xTb8a0KW2rWtvjbeO7kNmZL1iuwd18cqh0OC3ypTnFr4PgGwSvvAGOsC4AsADwBoC6AcwKe646v8++4H8Jn/HJVzEwq3sisusSjPkLX3KO76dCke+nqVqaC2dk8RHvxqJUZMN3fj4hwY+ttmFJZWYr/qIEWEYDceqNxXt72o7NwdqgXtWL/PwqtLFmusMg2KmPXHpTsKQ+6LEwE73EnAzcyrit8o3bqrsKxOa/TDeXxuuYHKcHLlag/Hiz+tx3UfLLQ87tpz2xq2qbjwJ+L6RuxWl7wxJ3RfLbfFLcJ5Dma/1Wq8MrMG6xk2dQtu+WiR/YEJjIo7s+FWSu7tkF82KX3fEaFOaj1/tha7Oq2zN4eWz6jxeBNegHOKLLumdgvKKoNJc1ytTZ1kt1hZ4GOM3QvgKABxZL0fwK+c84Wc81IAAwHcxRhryhhrDKAfgIGc81LO+WIAv8An4FmeG/nPqn3cmmDMCjkDCKkhZKa1LfIPJrKU0OK22l+EJiZmY6rdYPvR3B2W+xljhmv/fdyaiIRA/ami9mtudj46vjpdGmsaz+w9Uh6isTugWBzV7D4O1k3KG/bZZ1PUCMfCxznHVJsA8XAZMS282Lz/rc7DoCkbXW4N4RQnC7ZSSU01mbJCdslvTGJPxUPjffH4oCTjpZWyJlHnt3Cegz5ZiIaVruKHlXsCf8/YeBAZA6YaSnNobakOwzKeSMhuud0vls4FkldOpph/5Juge6/2ntq52uvd0c96dToGmozhpkmaLL8hsekyeCaem5iFY+XVqHRBqRlO9s9EQEngY4w1A/A6gOd1u7oACNiaOec74bPone3/5+Gci+rFLP85ducmHLU9wdjNC9vzSw3ZOGfo0jYDwVphhByz+2wneIgTqipT1x+ISHGgn/jFts/yawinrAsvTiRWDJqyMcSqZ/Y8vF6uVJxa303/+o19bI2GJ4zF2JR1+zHw5+gIV18sNK8POOy3zXjOxA3o3z9mYcwyd2KcEonqOLNuOnmdVMdpJ0qJRArx3VkQVHaqFE9PTHEPKK10HmLxmun4YnyqXi83FKJ+4vvVAICu/wkm/OKco/i4fcmaRIJzjvdnbTW4XkoTsAibZElVZN1M5rr/mCR2U/t+Jpwjk6lDQzKCf2teB98vl68xzOapONfp2GLnjfNr1n50fX2WbTiTCoEYvrgfGZ2hauEbCuBLzvle3fYmAPQmg2MAmtrsszs3BMbY44yxTMZYZkFBgWKTaxc3J5j7TAo/qgiVYqc4LtQFPHBMHOSC2cD6R1hvqK7S6+15EZ3PEIzDCtkegeLgP79uDvksU/SNWmT8znge0szq7tR4vPj72DX4bb1PGPzL1ytx1qvTg+eZXG+bRdpq27aEcY5bKfydwUPeLafvVEW1B+/P3uZKooB44uVJG2LdhBCc9DtxkVrkj1eTPdZ4STLjBjvyS7FkR2FI3CIADJpi7zoXjnImHih0sYSKlmxKZPi0Lbjx/+xdNL9cvBtdX5dnfE5UtueX4sO5O/CkX8DVkCZgEbauzimSeM8YkQkkiySeViKaO6nHQUIpTXFlNqzHm2IrUrYdKkHHV6epx0gSpqTZHcAY6wagL4ALJbtLATTTbWsGoASA12Kf3bkhcM5HAhgJAD179ozLkdxNA9+yXUYf5NW5R0JM9fqb8EvWftzW9RTp9aas24dnxusWnomqAq1lavtlcze7pHHxcFrLhoZt8bw20mtNtbZu3F+MqRsOYOqGA+jevoVhYk1Uly6nLNxurwBTcRPbe6QcnAPtWzXC375bjQXbCtC8YToeueIMN5pJSAi33x0qqUCLxvWk+5yEHIqL2liOARv3HUNqCsM5J4cuB8RC4U7ZVaBWuqCukZlrzAYpcqi4Am2bNcCb07NrqUW1hzYjlOnco2XvvlgWRBb3LBtTZes2Oz6e5wv9cJJAWIv3S0+R22tGS5S6QHwrdq3Q4pZnbDR6qEUL7V7F89ooHFQsfFcByACwhzF2EMC/AfRjjK0BsAlAV+1AxlgHAPUBbPP/S2OMdRSu1dV/DmzOTTiivb7s99myEGvB1oOhbhlWmYnW2AzyRN2hT2djUof4Rt6xxK1FQhB8NLPOxmNFA/kkGHrPpm2wnyh7vT0Pvd/xWa0XbPMJkcerksulS4WP5myXZoPTKHfxnoTrLqRlG1XJLqhHtJaFHhqbl3v25kO45aPFSlYnPXVEp6NEQYmaZdDOkqQlv6mRDHb7jh53rS5hLNDqNup/mthntPCBOz8NJjWTxTC60VvEa+gt0te8N980NlNzWTSrv7z1kDyrarzH6dph9ruKdaFLbhB06UwuVAS+kQDOBNDN/+9zAFMBXA9gLIBbGWO9/ElaXgcwiXNewjkvAzAJwOuMscaMscsB3A7gO/91Tc918ffVGrVdxLrfZ8sM2/YeKQ9Z61m9rDRXqqEfJMsqa6Lq6hZtl6xEc/kyW9SJSSzEn/TgVyuj3KL4QuVpVnm8YQnCiZ6sQZ+EQoX3Zm/DHz83jq0af/tutem+aNK+ZaPA3wURuP2ZZfmcvTlfuj3aPD8xFi7PycdtHy9WOi6c0iQat3+82LW6hLFEP6eLn75eYrSO1Xi8yDnsfpkPsR36Nu0qKMP6vfIEa5rLJufyMc6NGLZI8Hq5LoQoemTmHEFFVXKFHkQTW4GPc17OOT+o/YPPFbOCc17AOd8E4An4hLd8+OLvnhJOfwpAQ/++HwA86T8HCucSDrn63fkho1f/UcuxYtdh2xpGhDn66bHL4Jm4/ePo1eaK9iI70Z66IXbCPzHeL8SeikLsyt1HpOfVJVSTDNgxS5cKPNH4xzj3F6eLd1jH5DjB7plME7K7itbA1yabJwFy8pzFQ9+aERsXPpqH3EE1e3EkCj9ZTGAiob1pegufeE9KKowWfFkJCzf0puJcLyv/ckgyjgOhQrtMsIp1DN9Hc3fgshFzfQYIh1R7vDhcWqlsjRzy62Z8On+n4+9RZUUYbrrxjNM6fOCcD+Gc/1n4PI5z3p5z3phzfjvn/Iiw7wjn/A7/vvac83G6a5meSzhH74axs6AM94xcLtUe0TQbPtkuFKI1W+fEMjD52PFqZAyYiomZ+txMsUN/n2TzQDRrpSUiKkktzBDHCn02Pz3lVTVhTeq1hdP3wi41OuBuTAfnPCRWSI8osIrf27pJPdO2OFnQz90SXwK90xidnEKK03NKXR4rtV+uvwdil2nXoqHBG0ImBLrt7Cd7LM0apEuPFZU/FdVG4c7Mwldbzj2L/HHlhxRrPQLADf9diI/mbEfHV6ejx7Df8fWSHOVzzcrOuMGTYxPfoi3iWOAj5MSNolLSjlU5oTF8K3aHytWymn2Enzo0P2qL9xf/tz7GLQmi4iq95UCJ7rO1oJJMuJ2c4oeV6sL+g1+ujDhbbTQ5qVkDR8fXdlc/XFaFmz40j11LFTIWiYu1rLxjhm0adgKfOE/9vG6/+YFRJL+4Ajf93yJDqQknVsaisirLsiSEnJ0K44U+qQnge2aJjtY19H1E7BMeLzcUUK+NMAjZd2ix1HrEQ+/4xOhtFEuXTo+XBxIDib/ILqQg+2AJ3psdTN8xb2s+Fm0viEp8Xl2GBL4kY4ck7fxmyQJYHOT+HgXXp2QhWnVYft8Sm5gZJ1RUe6QFn8OhsLQypEyIKjJFyn6dFfSVyaHp9qdvOBA/Cpg4QZYAYLffQiI+l80H5HEjMuwy/iUatZ3UoNyiP2zeX2zr3i0bm+x+Qr3U6Ez5B49VKLuSjV+1F5sPFGPsityw++mFQ2eHdyJhy+u68j4AMFOo4Zu4yT987T6ge1dThJfQyznGrwpVev0iUYy4fQtUhMrt/qQldkfKsor6zvOdec8Xy5AxYKpy2/YdPY6MAVOV3BtlNTLnbDmEzgNnYPoG9YQ/ReVVeODLlbhgSGxKgyRb/T0NEviSjOHTtsS6CUQcwxjDbF1sllkSmts+XozzBs905Xt7Dvsdd3221P5ABf70hXlSDSA+s2nGmouG/27YdvW787H/6HGcM2hGYFuWSaKAuoD42pRUVDtaFIXD3GxzpU9W3tGQz/pFdp/35ksVKHavfpqsOnSElFXW4NIRcyxjCzV+ydqP9/2afNkad3WSKRESkcNlxlg9cUxNWHlPYLugGBd7RI2HG8pbye6H27dAxdVWi6m2E7jtLHx6Dy87Vu72CXrjVsoLvdvxyLe+4vNO3CMrJa6qtcnq3CJcNmJOTNsQDUjgc4lEDj5PXI1d9Em2W8M5x3jdwP3Q16sAAGv2hC62IilSXlRWhVELdxliwpa7EARtl6CAgyfdc4sUswXFB7NDq+Doi1xvdSFeNRHZHeP4ML0lTv/0dhaU4X+r8wzn2Y3lbs1TOwtKMXqRz6VScwGckx1UJHHO8dPqPIMyyap8EOdc+puI2mXPEeO7L1rEEi3Ts4bY7LRUeT/weLmh9qvdtdxAReDTnoFdzJrZtTbuKzZ1E7VCC6tw+ps5BwZPMSqBqmq8OHfQDExZt8/8XGdf5Tq5h8uVEyElEiTw1VHEzlssDUomAEQlHXOskdVYAoC8oqCrpEoCCyte/Gk9hk/bgjV7Qi0V0xy4dcgYt3KPbVRfYUmVVCtLGPlRt8Cu1GmHr/+vL43/kbKqiN+JeEccE/X3obaZtDb0ucgWWzKXT7uYVzfEvdLKGvR5bwGGTd2Cao83sDgThcnZmw/h+R+zAtY8wPcOWbUnud+uxEGm6Bs2NfE8h0orazBoysZA7Uzx/TITXjycKylFpm5wN/5VReCr8XCs2HUYY5blhnWtX7L24y9hlC3S3Bv1CmE7GAO+lbT17+PWoLzKg2fGm5dkSVzzSXxDAl8dRT8ouGF5iVd2FZRiqWI6df2i9vMF0Uv5GytqTAq2ntaiYeBvqwXvt0tzDAkX9Gj1gfTuJU61hPq598CxCtu4n1UWhbMJ5+SXVKD70Nn475ztgW2iJfC5CcaJe9uhEuQVxZ+yxMoCJsZtRFKvzA2W7Agdj2UxJWb9ONr8UXDNZgj2abFbakrELxbswnfLfYu+Yb+Fxobpf1Pu4fKkVyokA4nyhEYv2oUxy3Lx1WJfbT2x65tZKRnUhI1pG5xllLVDZbip9nrxgILAtstl74S1fqVtXtFxy5j+vKJyDBcUA2b3UQwp2biv7oYQxAIS+Ooo+kHhaLl6NqQfM/diTpyl9LbimvcWoP/oFSgosa7vsj7vKDq8Mi3w25LV1VW/mNQ4qXlQ4Nt6SO7Kl1dUjsG/bMLj32UGtnm8HBMz96JGcPvZ5B/IzYKfvV6Op8auxupcuXBWVlkDzjlW7jZqFW0zd5J60FXyi32Fvn8XJurfhf4/ea3RNee6DxbiirfiL4OnapdOjUKsWyTILXy1I/B9Nn8ncoVkDGJZGrFZZoqYgT/73LrMPAtEzMYdIn5IlGlRUx7I3jtR4DMkT4pB11dZa3g8PCYZOEWLoqhM3VlQimU7g2uJ+0evsIxLlmHm8bM9P/xwEsIcEvhcYvhUY2arZOWF/60PBOImEhcN/92QgUvkNn9Bda0eVKJMbG4hCmxmpQ00N7Li40EFwYRVe/Hi/9bjj5/7kqnkl1SgzCQDYXmVB5PX5mFvUTmmbTiIfp8tMyxc80sq0GXwTLwzcysKSyuNF4mvtXidYfOBYizd6bOUq5TLiDWy7msVfyTuireQbFmrZS6dbsRX7SooxYRVvjjfI2VVeGtGNq58Z760RijnoUodr5dLF69m9Rr1i+06NuQmJCoZDDnn0vIOtYq/E2vynthu7RU9cOx4SAZjjtiMbevz7C1doxfvRrpJ7GFt8bA/3n/pjkL0eW8B7hu1HBv8bS9SCKPQjw2fzt9pW7KBcA8S+FziULFkYZpAiGmXN+8vxqApG5PSwrXYxLVTczsCgmmNk+/XWydtEOO5zAKqtQWldp3dhWX4NcsXz7Bur8/1o6wyOIAv3BZ6v39ak4fnJmThtZ+DwdwdX52OKev2obiiGj+v3YcDR33uopPWmAd1W5KMDy6GiMNA/1ErwDmPWCAqr6qJehFo2fil+o3xNvQVlBjnlxqJhc+22QrP7daPFuOln3ylTn7MDCrIBv1sTMBw8FiF4NLJ0OGVaXjxf+sNX/NPSbKWT+btjHmsJOEclb7x1ZIcdBk801BCpzZJ91vpPX7XZ7Hd2t+5khj9eFP2iESrrIrId8ty8OrkDThSViXN4F1cUY3fBMvcrR8vBmBcW8iGd9m788fP3cneTdhDAh8BIOiWxTnHTR8uwphluSgstdbYnD9kJn6WuHPFmsFTNuLyN+cG4shEUkxG84HCYkazOCWjwAuYTxqiO4ZssB6zLAd93lsAILhuvPrd+Vimi/8U/fjNYiAP6Qr5PjN+HV6etAHPTlgXqBtppkmO4/k4KVmpi4l8Y9oWw8TttK+cO2gmnh5vnrExWqhawNbryiLEI3skVjM3xizROj9ierAY+hyJu1bvd+YFeulBf5/+cXWeYdHs8SoqCZJ0zE0mio9Xm1psNbSaa2IisNomPc03z2mWcFkMn8y6FM/zi5nnTCR4vEFrbEW1BwOnbMLYFXvQfehsPPTVKsPxT/+w1tBND0s8cZo2SDNsk/Xujfvk3kSE+5DAR4SwfFdwcbfXJulCSUUNnpUkbIgVnHN8tzwX3y7Lxb6jx0OSC2ioLPhW7i6C18uxPkkDimUDsR5Z8oT//h5M2rGrsMzUFeP3MOM7p673LRLsXIFsF47xPGMnIF8v2a37nGMQxu2KhMvQnne0kLXIqvuL+xIhK+HOAmNyhkjEpTV7ivBDGLW2VIRM1ULGJO7FPxe/MQe93p6HI2VV+Me4NcgYMDXg1qehPcdYJuUY4y9fIJvLPP53dv7W0DIFa/ccjWsLn9tc98ECnPnKNHQZPBM1Hq9hfaRX5gJyq+iHQkIvDZmL5wiqEx1TSOAjQhCzMN31aWKZ2rccKAmx1MkCf80WJ+IgX1haiUtGzEm436+C2e/XZzEVB35tYa4/NzNHLU2zfjFgh/bdZutIuxgLMysuER4VuiK4HsmDqZC4/sQjeUXlWOFfxOw/ejwkRlRVKIln1u5Rs0zKYmPv+nQpXp60wfF3umnFUYllIuKDnMNl+M0/N9z68eKQ7ItajPfrv8Uut8F+fyZprVeL/XtNbpFpvGk8075lI1evJ5bg2Hf0uAMDe+iBslCR93Q1XgFfHCIRO0jgIwLMy87HY2MSLxmLhor1ziyTuV5IkMXMJAPVXi6tUffwN6GuG6JS9O/j1kiv9ecvVyh9p+bjr8q8bJ/WtUCWsAXxHWORjOjT/4up+DVmblRPUx6t1PvHjlfb9tu+7y/EPSOXAwD+8OZc9Bz2e1TaEq+UVNRg5e4jgeQLVqguhgdJiisbr0WG92Tjn+NCXbIH/LQegM9dXzXL4phlOXhnZrbtcZEw0Z+oTSy1MmzqFnR4ZRo6tGliOF4lm2ysiGb24CvfmS9V5umRjf8y3EggRbgLCXxEAP2iX2R1bhHGrrAu+Blr6qeFvs6ywdFsEIqzLOxRo7RC7i6pT56gX+zN3HSw1uwfmhuJ2XxhSKOtw+w3EuGhd7FljOGnNaGFwcVYza9stLhHyu2zuamyOvcIHvlmFTxejsvfnIuLhgcFONX1xgqb9y3ZmLPlkFKNRJk7l4z7Lzk90iYRCYg+a6umSJTFlpoxaMomfDLP/Vq34vxV4h+//vK1sYbdDolgGusanLHkgiGz7A9ixrGySDKmq3obELUHCXyELVsPlqDfZ0vx6mRzTW5pZY00a1xtUk8n8Hm83JC4xcs5Ri7ciYwBU1FeFVzIWmWvTCZUf6Ze06dl4LRDFjdZ21TF+D1MNvTxeR4vxy5d/BgPHOuVunFxHqwhpWV1dYOnf1iHOdn5+G39fkNR4F2FalaGe0YuR35JRRI4dKpxqFjtt/YfpWbBH/zLJsM2/ThTV4RpwrkLfzTQG+m2HyqR1hqW1bLUezTEE/FiNdO7v09Zt588bxIAJYGPMfY9Y+wAY6yYMbaNMfaof3sGY4wzxkqFfwOF8+ozxr7yn3eQMfYv3XX7MMayGWPljLF5jDFSFcYh4xQse+cNnomnxoa6/q3OPRJWjZXKGg8yBkzF05JU3nqOllfh6yW7wTmXxm5t1xXy9XLgmyU5AIDDpVWYvfkQvl+eW2csfKrI5jyVuSYzVy2uT4wdcJtYKx7qIpoA2PHV6dL9n87fibNfm45jx6sNMYFOyS+uCKR7b5Dum8KeGR+aPGp17hFH2d8uHj4n4eJ5wqWi2hs1t1oNfSze5gPFmLZB3e2XSFw+nGtM4AH4BMEvTaz/bvc9/fWu/WCh9DhZsql4tvDFQ9t2FZSRAidBUbXwjQCQwTlvBuA2AMMYYz2E/Sdwzpv4/w0Vtg8B0BHA6QCuBvAiY+wGAGCMtQYwCcBAAC0BZAKYEMmPIaKD6iJ+lhC0vfdIOfp9tiyk3pqMVTlHDK6imlXuFwVLQLfXZ+M/v27G+rxj0kFIZp3QgrlzD5fjsTGZeO3njQlRSNoNpqxTs67oNYn7Yphe2ynF5NIZd/zkr/FYUFKJt2aoxexU1njw9A9rsasgVDlw8Rtz8Ic35wIwt+baWRlkC8yRC3cptSvRqfF6w16wyepyyfjar1QTIct73SAtJXRZebi0EhkDpuLWjxdjqEkSl8kK5Z2W7ijElgNqShzV11tmzYt2fdBIiJc+tE3iCiuzoBLxhZLAxznfxDnXouG5/9+ZCqc+CGAo57yIc74FwCgAD/n33QVgE+f8R855BXzCYVfGWGcH7SeiTL/PlmLTfnVNecaAqRi5cGdAaBPP5Zzj57X7Qtwo7v58mcFVVD9hmLFciDHxcC7Nsqcf0EVB5iNBE0kWvlBKdEKTivBN1G3W7pErhq54ay52FfosgE762cTMPPyStR/X+Gs/yth7RK6IsEtuIJaf0Ziwaq/kyOSj99ltwnZfve2jJa62hUg+9BlgN+rWD1qphmOCgJAjSfWvp//oFbjx/xYptUFVoSGT7eJFqJLhZjK5cLyvNLIUQzyI+EI5ho8x9iljrBxANoADAKYJu3MZY3mMsa/9ljswxloAOAVAlnBcFoAu/r+7iPs452UAdgr7iThgtaJ1T+SNaXIN/i9Z+/HshHX4wqQYt0aq4JqZX1KBqhovOOd4edJ6rM4NLtTGrQjWjGrRqJ50kNe7QCzaHiw/sGJ38FqUyj8UfUA+kLyF6Al3uNOkjImYtv/N6eoZ+aqFRELvzdpq2F9VY74w09fX0nPfqOWGbfrERcnKiU3rhx0LtFXnIk8QIgf93jMiC3R98aGvV+JYeTW6vh5MEOIxiZvbd/Q4HhuTGRJvL4Nzjn9NWIdXJ28AN1H+ypC5Nsvi+pIRley6RHKhLPBxzp8C0BRAL/hcMSsBFAK4CD6XzR7+/WP9p2j5bkXfmmP+Y7T9er8bcX8AxtjjjLFMxlhmQYH1RE5El72SDFyy4qr/87twiS4YhaVVIf+LHDXJ3Hfx8Dl4ZvxaVNZ48cPKvbhvZDCRQHpq8PVNZUw6xKumWCZ5L5RG9VJj3QQiCdAvAEW3bzvEbH8fzd2B41UeHCoOXu/s16bj4oyW0nPnZOc7bKm1AJlsxEvyByK5eFeimBH7LOCb/48eD53vK03iegf8tB6zNx+SJoyrrPEEMgjP31qASWv3YeyKPSiuqHFg4TMeGMcena4yMTPP/iAihERXejvK0sk593DOFwNoB+BJznkp5zyTc17DOT8E4B8ArmOMNQOgOfk2Ey7RDICmIizV7dPvF793JOe8J+e8Z5s2bZw0magFZK5cYhHWA8d8Gn5NCNSneQcQkk1Tr52bLtT4qvJ48daMbNR4vIbU8LLOqJrAI4V8OkOQlT6g2DjCKfeMXGa5f7lF6v9vluaEfF64vQCXvDEnZFvb5g3Cbpse1fi0ZCDSxDkEIUOm/JW5SC7eURjyefTi3Sgqq0K/z5YiY8DUwFyueeTIYvxu+XAxugyeiVU5R0IEyCvfmSctEyBDJvAl+qKeiB6J/mqEW5YhDfIYPu12MM55EXyun12F/V0BaDmcN4n7GGON/dc05ngm4gZZbMzMTUatfVpq8LjLRszFhFV7AtY+LZ5HRAyUlnUqcdtn83ca68aZOHFUS9R1Hdo0NmwjcY8g3CfXJjbn3pFG10oz8l2MX5FRVzT7FI9LRIvsg0aX39kSq/53y++lI20AACAASURBVIyZv6/9YGEghEQljk4r7n7358tQLzXokXK0vBrPT8wyOy0E2dck+qKeiB6J/mrYCnyMsRMZY/cyxpowxlIZY9cDuA/AXMbYJYyxToyxFMZYKwAfApjPOdfUPGMAvMYYa+FPxvIYgG/8+yYDOI8x1o8x1gDAIADrOefqQR5ErZMmEfj02jrAmOnqpZ82BP7OkQh8Xg78sHIPluwolHaqnbpMfXrB883p2dKBesl2Y9vaNKkv/X6CIGofMQvn9A0H8P1yeRmYD+cY072TosY5VB6BiDWymHkx2UvX/8wyLR3y/fJcjFwYmgdAf7n9khh0GVILn9KZRF0k0a2/KhY+DuBJAHkAigC8C+BZzvkUAB0AzIDPDXMjfHF99wnnDoYvEUsugAUA3uGczwAAznkBgH4AhvuvewmAeyP/SUQ0yVIsqnpc5xLIGND3nLYAgMNlRneL1BSGlydtwP2jV0gXdnqhUt/vfG6fxs44IdOYeU/qt08SH0HEhIH+5AHr847iybFrTEu5uJmhjiCI2GHncllR7cXH83ZI973280ZDYrimDdJCPsvq68mQlWCg+FbCjER/M9LsDvALZlea7PsBwA8W51YC+Kv/n2z/7wCoDEMC8diYTKXjPLpBkwGol2auj39LyN6nj90BjFY5mQOn6jgtS+RCgzxBxIYaD8fK3UdwrxDvJ0sOJYN6LUEkHgck2Tz1vD97G1o3qSdN8qancf3QpaxqLT3ZvC+LQyQIIPHdfW0FPoIIB73FjNmkwZyxyZmbkWw8v/aDhUrnyiaDMkmSEoIgok+Nl+NPX4Qmd1mVY6yTJ0PmYjNtwwFX2kUQRGKgHwZkBdVlLJKEfKhaBwki0Qg3aQtBWKIXqvSfl+087KjezfhVe0I+fzxX7u6hgr42H0EQsWOPxJqnWhdTprx5auyaiNtEEETi8Mi3q0I+k9BGRAPV+o7xCln4iKigd+kEQrVw941ajr9efoby9VblhJZ+mL05/MQDqu4eBEFEH1lsnpjAwYq6VDuPIAg5R8urQz7THE9Eg/SUxLaRJXbr44REz9wTDS7t0Mr2mK+W7A77+pHc8q2HjKmjCYKIH35dr+aWqZK+nSCIxEQlfk9GqaTWL0FEQrsWDRO+XjMJfC5A8p6R+VsLonr9akUffYIgEo+svUeVjtPqdhEEQRAEYQ4JfC5AGR5rH4rDIwiinJItETHitJYNY90EgiBqCcWw8riGBD4XINGj9lFJ60wQBEEQ0YAhCVaABEHUGUjgcwEy8KmxWJICmSAIgiASjXA1/p//uYe7DSEIIuokg4KHBD4XIJdONUookJogCIJIAlRLh+hp14JcQQki0SCXToIgCIIgiDpGMiwACYKoO5DA5wJk4SMIgiCIukO4Fj6CIBKPZOjtJPC5AMl7BEEQBFF3SNUJfDlv3hyjlhAEQdhDAp8LkIWPIAiCIOoOiV6EmSAIdVgSWPRJ4HMBEvcIggiHOc9fGesmEAQRBom//CMIQpVk6O8k8LkAGfgIggiHM9s0iXUTiCiT8+bNWDPw2lg3g3CZ809t7vicu7qfGoWWEARB2KMk8DHGvmeMHWCMFTPGtjHGHhX29WGMZTPGyhlj8xhjpwv76jPGvvKfd5Ax9i/ddU3PTSQ4SXwEQRCECS0b14t1EwiX+c/tXRyf8/6fulEICEEQMUHVwjcCQAbnvBmA2wAMY4z1YIy1BjAJwEAALQFkApggnDcEQEcApwO4GsCLjLEbAEDh3ISBxm+CIAiCqBsMvOVc1EsNz0HqtBaNQj63aVrfjSYRBBFNksCnU2nE4pxv4pxXah/9/84EcBeATZzzHznnFfAJeF0ZY539xz4IYCjnvIhzvgXAKAAP+ffZnZswkMaOIAiCIJKfu3u0wyNXnBF2Hb4Wjevh3JObBT6rXCY9NQlWmwRBxBRlFRVj7FPGWDmAbAAHAEwD0AVAlnYM57wMwE4AXRhjLQCcIu73/635QZieG9YviSEk7hFE3aZT26axbgJBEGHw7+vOdnS8Nt+7lbXvtJaN7A8C8PKN6rrwJEgoSBBxxcgHesa6CRGjLPBxzp8C0BRAL/hcMSsBNAFwTHfoMf9xTYTP+n2wOTcExtjjjLFMxlhmQUGBapNrDScGvjNaN45eQwiCiAm0wKq7dGhDY3qi8v0jl+Af13R0dI7bDj2jHnR/Ifne3V1DPreiGFKCiIizTkz8BGuOnNA55x7O+WIA7QA8CaAUQDPdYc0AlPj3Qbdf2webc/XfO5Jz3pNz3rNNmzZOmlwrUNIWgqjbRKLtv6xDKxdbQtQ2KRbPfuQDPWqxJUSkNG+Y7uj4e3qeFtb3iCuGaCT06UDZfwmC0BFuWYY0+GL4NgEIqJIYY4217ZzzIvhcP0VVU1f/ObA6N8w2xQwS9wiibhNJDeYfHr8U24bdaHnMeafqdWNEvGD16LuddkKttYNwzsknNAj5/PXDF9XK90ZbSax/J8kDgSAIW4GPMXYiY+xexlgTxlgqY+x6APcBmAtgMoDzGGP9GGMNAAwCsJ5znu0/fQyA1xhjLfzJWB4D8I1/n925CYOTpC007tZt2rVoGOsmEFHAysqjQr0066GYnAgIwn30dTBVejEXVLw8QnVv33PaAgBuvuBky+MYmPI3zXi2V0RtIggiOVGx8HH43DfzABQBeBfAs5zzKZzzAgD9AAz377sEwL3CuYPhS8SSC2ABgHc45zMAQOHchMHJYozWbYmJWzEQpGlNTsKx8OnjbKzw0sCRmFB/j1tOaha07t114al410F/1HP/Je3DOu9f1/oSxohZO2Uwpr7O6HwSeQMQBGEkze4Av2B2pcX+3wFI00f5Szn81f/P0bmJhJO1GM3/iYlbGdmIJCWM98PJKb3Pbo0tB4odfwdBEHLE/vf+Pd0AAOv2Hg3rWhe0a+5Gk0IY99gl6D9qBQC/wBeRupjmr1jTukl9FJZW2h9IEFEi3Bg+QsDrRP1O425C4oa8l57KwOgFqNOIVgXVd2rJgGsw4IbO+PT+7lFqFREtqL/HL07csL9/5JLgBxes7Zq1TmuCLKbvwtNaBP5OYczZOkNHfxsL5IlU/D3qkM44cVn4wtWxboIrkMAXBd754wXKxw6747wotoSIJ67vclKsm0DEmFTB91NVGDj1hIZgjOGm863jfIjYQAu55OeKjq1dvZ5mrdPeHZksJ75XTHLM3OdNHa8Mxz3X17r0RFokWafqEGe3DT/7Kd3hxCUtNTmeHgl8LqBP2mKVEll8bdYNuhY30yKuTkGLwyRFMcAmROBz8V2wS/pCRA8rwZ36e/yi+mz+795upvvCTabU3l9svVF6mtJ1rup8Ijw6iU9moXzjzvMN2xqkp9qGJHgoK5QSn/S397LQnq0eGgvcpTbr4iXLs6NVgguEO1YysKR5kQj3mfr0Fbj4jJaxbgZhw9Wd2ih7eYmKdP0ibPJTf7A9f8ELV6k3jHCNTm2bxroJhMuEm1nXqq83a2CbFgGAL2bw8z/3QPtWjfzXNF41NYVh0C3n4pYLTsb7f+pqyOSpmh1cpc7fA5eebthWn5RIBk4zEeZEzIyl5N7tHrd2PaVWvy9Znh31aBdwVJZB56eRLC8SoYbTp/39I5fgn9ecFZW2EO7QpEG6stInRVgNdG8fWqNNtph46A8ZIZ+bNnBWGJqIPulpNIbHC2e2aRz4++IMubLs1BN8pXFkC3OlsgxCZ9eMbprw+PXDFyu1s1mDdNxwXtDFX+/p897dXZGemoK/XnEGPu7fHfXTUnG2TumgMuRc0K45GqSn2h53aYdWhm2kjA4PM2sq3U/3+Oi+C2v1+5Ll2ZHA5wLhOkMky0tEqOMk2ycDQ720FDSsZz9hE7FFNYNeqvD827Uw1xa/cef52DbsRgy5rUvo95hJluSRFTNG3HmBqXBBnnK1izi+ppqYWlL8q55mDY3KkxObOUteovXHSOtwdtQJc/16tJMe16fziQCANk3rR714O2FE5TGbhenQco+INSTwuYCTcfelG4JVKBjIdz5RcGOw5typNTh4nhnh1n8i3EX1sQ669Vyl405sWl8al2eWqC/SAtBEeHRvfwLOb9ccE5+4LNZNIQD8R6cgkdG+ZSO8dvM5GPlAT8O+k5s3RFsboU/sadr8nZIi2xtK77Pb2LbNjk//3B3z/n0VFrxwldKYIx7z7+vOdvRdd3WXC52ENc9dK7/PVNopvulxegvTfcny5EjgcwGjpi34+TNdKvVrz20b+Jsx5kgAIGKHG8/ptm6noKlijIcZz/UNTiYdWje2OJJwyt0mWnUVVF+Pi0wsQXrM1gZ2gl2XU6jocjTocqr8vqalWk+htSWIa26KdR3xPlitrx/t1QEnNW8g3dfGQYkCLZGKioXvEhfiseunpeKM1o3RqF4aMizGf1lz9MqirqcFXcplb+ngW8/FL/+4PMyWJiey5/zlX3rqjqmt1oTSsRaTmCQb553arE6MoSTwucCPq/NCPouLv84nmy/AGCJ3+RHrehHRo//F4VvS7u7RDjlv3ozru5yEUQ8atcpOeLpPMJ7vtVvOIbuOIhv/c73tMW/1Uy+nosfMfUzkNptAc1GpYDoumFn4dLFEhHs0b5iORiZu1U7u9pi/qsV3hUNdeeynmAhpseKf13TEmW0a46qzT5Tuz2gVdNt2+xmlp6agl4NyEXqlpV1zUhlDo3qRKSiTjfTUFFx+ljHeUSSSGL7TW9knhSHch3OYjvFA8kRMkMDnAvqCqKr1URmzfslUGPNI9BYRRBAzNw0Vzm/XPPD3yc0j0yIxxrBu0LVYO/BaXNO5LcUIKaKy1opkQfZJ/+6488JTAQCtTLLiDbmti/V3qLhomWz3BmKJ7K9BOEffz1o3MVqBZJkORdxw6TNDL+g3SE/Oqd2ui9TGcCi+C51Oaoo5z1+F5o3kyZQYY3j0ijMA1I4yRrMoBy2PwX36dYleAHzqqjNDPpMLohy9JcjNOZjueOx45eZzTPclS1dIzlkhhnQ97QTDQHppB7krBwND4/ppES0EInURJNSIxeSn1fPRuwyf0KgeWiik2iZ8/K13B6UB28kzvvbctiHxk+1bNcLLN3W2vA6DTVZeha83cy3WttIirbYwPge72K9ocpMuUcTs59SKcicaThbXj/fuIN2elhLZssdJF2uYnorG9X1zdKTKXSec3bYp2jStH5IzQO/y11yXtOZF4VjANxydckJ8WVQThf6S2PpoD816hcKW12+I7hfGORuGXIcHL7NWwmlw7suca0ayZNMngc9lxj92acikxACMf1we0K/1z5Ym2kEiObAaKk4wefZd2zUPLBSsIROfFdlDb8DLN53j2oDdqnE97HrjJox8oIehRmLrxvVxQ5eT8MUD5sV5rSb9NhKrkZ56kpixPwqxhyTvRQezXibe78d7n4lBt+iS8tRS93ys1xkhn1XqhSUiTmIi9QKNxokOYvREnvd7eeiFaytG/aUnnrzqTDx/7dm4L4KwADPObCOP22pcPw2rXu2LP5wVdPm85YKTMfXpK/Ct37X44oyWliEhPg+kNFwZRct0IhLuXBJtoUE/9id7dm+7LLVNG6Qbx+MwSZZ5lQQ+l2lYL1X6IjZvmG7IIBaLeJu7/G5nRC1i8ZzNxqxHe3WwPcZuH4FADapIu5pW3+vcU5ohJYVJLWkpKQyfP9ADPU5vGZKcSYMxa+GfMRZIu25Gqyb18fXDF2HK3y/Hdf7vOLFp/cB74NaIMuKu86nIux/5wsJ4p+ulaXXTgjWiZKn/o4HVXPK/JMog6mS8Mzt0sE0mT7Pv+Gefjtj5xk24vstJ8gMk5556QkM0SE/FP/t0RLpNgp9weOUmczc0PYwxdDmlOXp3bI1vHr4IT119Ftq18Lknau3OGnRdyPG+/91rbzIQzfuREoFP/h8jSDqWiKiMBXZJtQLXstmfLF2ABL4oINbX0gaHrMHX4S+6Ispaogena/Y/nGkdNGxGg/QUnEmZnKLK1KevcHS8mZbqViHBh9X7catNIhDCGU9edSYmPfUHw/ZmDdPx4xOX4dP7za13IrLkPAxyQdEpV3c6EV1POyEky56GG/L/xRktcd/F7XF6q+TIAntCo3Q0c9313fxOa1365vNPNhS9rp+WgieuPFNyljl22fdeuamz5SK0qYWrEgCc1jL5s9OJNLHxnNDHunUT+pldcibt3JObN8Cyl68Jq31PXqX+fuhLt6gsghljuKrTiUhNYQEBIy3V978sFlE1J0FdRfX2qFinUyOYHzqfVLcyNKve91NPaIg2Tevjv/d0C2z77Z9X4ByLhIp6kiVUggS+KNC+VaNA0LyV5tVOmaPFhHQ+qSly3rw5sP2rhy4Kq13J4occz3RobVycWd31tgpZVjUXpKG3GzXTl5/VOuTdIOSojtcv3dAZ3dsb6/GkMIaLMlqGLJ61eLpUxWs3rp9q2wOdrK00ZQEH8Ig/McSQW+3rkJmhFQ+/sH1wgfunnsmhNf6zTUKVcHEak7l12I0YcGNn4w6r77B5af586emW7bATGBPJS+DpPh0N20RXyTRhUg33d4lKnW3DbnSk+NDGhNNaNAo7QddLNzh7P0Scrks/uKcbHu/dAd3aGZVHGn8R4qCyh96ADUOuC8SX10VU7rH+3XtIp+w3wyzEo66jWaJFVEtlLX7paqx8pQ/uELzbzju1OaY/0yvwWZtLzWpVJsvK2VbgY4zVZ4x9yRjLZYyVMMbWMsZu9O/LYIxxxlip8G+g7tyvGGPFjLGDjLF/6a7dhzGWzRgrZ4zNY4xFZ1aOMrIB4ISGvqQaVhpBTWtg9t5qgp1euyBqjWUTvZnGljFjRlHCXWTvglXq7AcvOx0vXN8Jt1xgHhfyp56n4bP7u+P+SxKye7iCvp6lxhlRqkX4wvWdAACd2jYFIB/wLzzNJxjeqVCg+OYLTkZaaortYkGbeFQWFVpXTmUMA285F7tH3GSw+tlZM0Su7NQGk5/6Q+C3A76FqxXP9jUuwOON2p6s3R5h7RR1KTa+wikpTGoNTkT+1PM0w7YRd50f+Ds0dpHjZv+4esVZ6uULxDFFb0GzI+BaHcZL17pJeIm4xMRtl3Zw5v1z6gkN8cpN54S4Es58tjc+uKdr4HOfc4Lu6Q3SU9G0gc/boa4R9BII3qvmDdNNvHR0JTAU34dPdPNc43qpSeWSHS6f9A/eF21+kt12mVKcMXPPmpf9yrd7LvKNK/+4Rj6f6T01EhWV0SwNwF4AVwJoDmAggImMsQzhmBM45038/4YK24cA6AjgdABXA3iRMXYDADDGWgOY5L9eSwCZACZE8mPiiRr/aixNIvB1002+pqnWvb7/rSyBsiydZguExvXTcGf3oJYjXdU0QUSElYY4JYXh71efhX4W/vcpKQw3nn9yWP79ZokLEg1x0SFiFbj9L6GUhlPrtr5fyeaLjNaNkfPmzY6SGti5hgSzbdpfS1+KQX/tHcNvNK0/OFricvq33h1wYfsWynEPAPBs3/DLlSQS/76uU8hn67hav9Du4Pr3XdzeVDFkVvRdxO59GRCB1SieUBkCu/st1Jz7ForZQ2/ANw8784pZ9OLVmPO880ynPNAnnY/VU5/uhYl/c764XzIg6DpqlsTFCZ1Oaoo7L7RWYql4piQbsySZb2c91zvE5fWqTr654NYLjKEW2noOAJpKFHE3X3AyTmwael9fuL4TembIs7wbrm8yKCVDiRaxO/39al8tYtnvfeCyDEfX/duVZ2LH8Bvx8OVnmB5zWsuGSZMAx/ZN4JyXcc6HcM5zOOdezvlvAHYD6KFw/QcBDOWcF3HOtwAYBeAh/767AGzinP/IOa+ATzjsyhhLipkpsBiTzFDfPXIxZj3XO/DZrKh3wGXMYpZrXD8N55/aPGSb/vjzTm2G124+BxP/dllIfOGSl8KLMSBCGfvoJYG/nU702ph1lcuZ0DRtcZK4npsqJ270Z8yTFbUW60o5ye4HGBf0brtDn2XjZqfyfX+5LANXd2pjiA3W0AS373S1Oru3PwF9JUllnAh6iUYkVjfGGFrqyqAwFvq/2XmqjLjrfHz3yCXSfaLFVUseJKIy5lwWZtx3vKHyWz3+h63NvQ3SU5GWmoLvHrk4xI3LitNaNgpLePIGvtvxqWjbrIEh868KmvCgeSMQtUfbZg1wjZBoa9gd5wFASHZUjXDyJ6iOITedf5JUAHr37q6Y/kxvDL7VnWyVsYKBYe7zV2L845cGtr13d1eLM+QMu+M8zHg2dAywm/dObpY8Mc6OhyXGWFsAZwPYJGzOZYzlMca+9lvuwBhrAeAUAFnCcVkANJtrF3Ef57wMwE5hf0Jzq9+VpHE9oyanaYN0nC0Mzped2QqrXu2LhS9cHeLapy0yLrGZBPqcE5rZT+8eyMDwaK8OBve3E+ugli4cXryhk+m+4Xeeh8vPao1Zz/XG8pf7OHYB0mCMoX6Y58o4T6cEkFE/LSVhMnsxxvDw5RmG7S9c1wnrBl0rrWUpTpZOg+GduFY65YfHLsUEYeIS0eKt9AKGjBaN6+Hrhy9GK5tyDr06Bu9Nzps3Y9JTlyu31ZNIAV5RQmZF/uIBn77zrxLNsDbOXqDQBwGEuM/NFhSBGmLdONnTSLHJ/mqFFvuZKChZvr1abG3owb06tnGUqCEczjzR9+zvsrGQuQljDN88fBHGPiZXGLiBLIZq+J3nRe374hGzd69eWgpOad7Af4xZDVaGj/tfiNf8xb27nx6MEz/Pb8GXnWll0Rbf5Y/u6y5VUPyxR7uohT3UJg3rpaJDmyYhLsuXOHRfBnzxzo6T2ySJ0hxwKPAxxtIBjAXwLec8G0AhgIvgc9nsAaCpfz8AaG/fMeESx/zHaPvFffr94vc+zhjLZIxlFvw/e+cd7kSx/vHv5PRCPfTee5UO0jsoosBFxYYFr73iFSt2bPeq1x92xYa9XuxYsCEICgiK0g6KdASkl3Pm98dmktnJbEs2OUnO+3me8yTZnZ2d5OzOztu3bfMy5DLjpuPbYulNw1ybgqtXyEGDonw8eHJnNKiaj4ld66N+1Xx8elX/UBD3C+f0wK0aH+VLFb/jq4ZZCyiEf2RnBEJxdS1qVkCt4KTfs4l7La28gPOSYtuJ206IfBir8VxzLvGWUbSsUdfdJ3erj0CAoXK+XjiS5+nMjEAoKYkbhDa2jXioRjHpv3txWLCSD+/VtMhSSJs6vBVeOq9n0sRcddYksEk1Jvdp7EtikrcvCv8/uzSsiuIZo7WW0g71KuOjy/vh3L7uhKk+TcPWgOYaK41pDan5HlYxKk2qFeCU7pExb6mM1YK6WY1CTAgqr8QCN1ElMWRqV8pD8YzRti768WBAyxqo5qKOZ7TMvbI/Viju4ZN6NLSt45fqRBtTaUXF3Cyc27cJ3rjAnO15YjAuVXdtu7XwZQQY6lfNR/Uoa0wmO04eMSoi4SFhxrXAxxgLAHgewGEAFwMA53wv53wR5/wo53xLcPswxlhFAHuDh8ridEUAe4Lv9yr71P0hOOePc867cs67Vq+eGkVAMwJMm+LYzXFfXjMQd4/vAMDwyRcm52ObV8MZGh/lQICFHnYfXR6pIU4Xt75UQfbpf+eiSGvKd9MGh97L/xor17xo0BVtVzV9zWtWSCflFZoo7m7qdV/k4QHet3l1fHJFP0zpb9RDbF3Lu7tUBynznVtX3+zMQMzud0tuGoqF1w82bbtvQscIt1en7I0A0L9FdTx/TnfcO76Dq/ipZESX2TEa1NhrO1rWquDepdOhmbxb1yWz6OKzqwfgrpM6OPadDEbc2R6sUzpL/9wr++PeoIvXXSe1x6zJ3dLCspEs5GZlaJ8pd53UHq1qVQgJLenE/zwoRMUz3+pWblMnvNTt0rAqCnIycfvYdvj4in6hzM+6+dVuCmmleSb56SWULKjPdRldVs3vpg3GJ1d6j7+1IkUfe1pcXR3MeHI9BaAmgHGc8yMWTUM5BzjnOwFsAiA72nZE2BV0hbyPMVYAoCnMrqIpQUmpc5t4c8/4Dlh9x0i01EwC6gUrTyLCt5sejlFgMRPIrlw6S40cRB2PtdbNx7eJKnFEqqE+DJ3ic7wubJvXrIBWtSrilSk9cf3o2GIgEql0qZyfHRH8P75LvQi3V7fJAPo2r44JXetry1WUB06SsrBeMqiZtsZiLDjFa8qCoypENqtRiECAma6vwa3MLv6CNy5InWx/uphdoeV3KmtRkJOJAS31vwHhLwNb1cCHl/fDzWMi58e5V/Y3eTkkM0asm/n5oZbVsLtLM5R6hjKzz+uBcVKyPMFpPRuiRc0KqBG0RgkPIVl4tpobmlQvwI3HpXZcnlvswjHkUguCWpVyUdGh9qgdFyp1MNPJYOJWHfAIgNYAjuecHxAbGWM9GGMtGWMBxlgRgIcAfME5F66azwG4gTFWJZiM5TwAs4L73gLQjjE2jjGWC+AmAMuCrqIpRUlp4iW+r64ZiEU3DAl9ZoyZgk+7Sj7iw9rWMh37yRX98dApnYPtjEWf12talx20vGH1m/3LY50tme+vH4IlNw2N+vj7J3TE5D6NkR+MHT2tZ0NtnFEqYpeREwByMs3u0+riWA1qP1F6WIhgex09mhRFHZsZGktMR8eHCwc0RdeGVXD3uPZ4/1LnZBZPndVNGygv5hIvJDrBhNekPTLywuqqYS0xVOPG6ZXiGaPx9Fld0a5uRcd4TfnaUQvIt6hZGGwTbmWV0VYX18uRnAuaRddHzoEill3+rr2iiOMh/Cdfk6ugWY3CqLKVxpMCixCb8V3qad1Tr5fCLOzCc548syvO79cE1SW32rlX9sMzk7uhd9Nqttb+no2LcN+Ejrgs6Ilw9/gOIaFPWP1GR+RlsI/zVpM7JYMV3y3q2O2uoXgsvWtXVgX95LqGY8FNHb6GAM4H0AnAZqne3iQATQB8CMMNczmAQwBOkQ6/GUYilvUA5gG4LTfaCwAAIABJREFUl3P+IQBwzrcBGAfgDgA7AfQAcLJP3yuhtK3jLjjfT+pXzbf12Z91dnfMvbI/lt48LEJj0axGIcZ0NNIGh+4lj9d07Urp67sfK1kxZDusXiHHMibNDScFNYl52Rn47faRptIEOpLseRxX1GfeqGCGzyGta8StMLcg2RY+gDGHvH5Bb0zs1sDkcmRFpbwsbemA49rXxghFqeSEXebheDCynXWdSyfiNdZBrWpiziV9bft/+6I+pnv0hXPNro9iISm3sbrUknnhoo5NFw7RJ5j5UP5+p/eK731L+Eui3cJHtTfPS3aCl27Xef2aYM2do7DgusEh10sdrWtXxLRRrU39N6tRAQNdWJoDAYbxXeqZhOZBwUR8IizguPaKwGfxPYRgN2tyZNbqROI15u4/Ezvi5GAtPDnJGGC/RnFbfD0WkvDRHTVuyjKs55wzznmuVGuvkHP+Iuf8Jc55Y855Aee8Nuf8DM75ZunYQ5zzsznnFTnnNTnn/1b6nss5b8U5z+OcD+CcF8fhO8adCV2TL9NhYU4mmtUoRKW8LNtJrkpQS9TGY/aydA0O1mG1ULITEuZPG4TPLOo4xSIQOiH/r7MzA45xRFULkv//WCW4+Ds1xsLzsoWwW6MqoZgsuxo8fuElRX8yU6NiLvIVTXcgwPDo6W6q9IS5frR/CYrc4CX+LpnoVL9yaP6pkJOJ/OxMU7IlnbtTKsZaurk9hIJGnj9TyXJRHlHjzPISXMC6bZ1KpnqK0dwaGQGW8LqDw9vWwm+3jwwp4tRsyX2Ccd4Nquabth8bVIqoLo01pCQmryVhIfcTO9cLKbqrV8hB8YzRoXnOqSwZ4Z70i/AsA1J5MVe3ch5emdITM8bZB/erjGhXG0+e0TXkhlAeePfiPnj7oj6olJeFrg2rhFIs66hdKQ9NLOo4FeRk4vighbWOT5bSx0/vYumW176e8dDQJem4Ymjy///eC34vXXyqF0Qs1pxLjsXz5/QIPVj6aGom+YWIp0qn+2REO2/WPB1eNcBlxQMTOyX8nMUzRmPpTcNCCx7hjioeM+3qVgqVchDbcqWFtGVq+CR9TGVHoQBTLTdEcpKZEcBIab4oi1qffhSjl7loYFPnRj4ghxHIMamPnd4FNwTdzN+/rC8WXBdO0HXb2Hb4curACAv56Pa18ehpXbBs+jB085CtWnDtyFb49fYREdtj8fQqKsjGl1PDoUmdGxhKOVFXum7QtfLUHvo61YAhHKp19fzGLmlMqkECH4EeTYpQmJNpurAfPNl+ocMADGlTE/1axG+xnGx0qFcZnepXxtKbh+H1C3rHJOg/OLETXp7S0zLexivD2taydMsb26kuPr2qP/pK/6thwTgkNe4tldBp98+wcfEa1b42imeMRru6lUwL5Hjy1FndUDxjNBoU5Ts3ThHuGNveU/t2dSti4fWDQwKH30lP4kl/TX3HRFApPysUcyeu84Ck6e4YdPUa2ykchyrcua3ch61mq7K2kn0+dYB27mpcrQAzTmqPywY3x9Nnma8ZYeVLRWtmujKlXxPt9nsndMQL5/TA0DY1Q/UrgcQkiou4FRhwVu9GMVm56lXJx0OndE5oDcvCnEz8s78haA5sWSN0/RfmZJqsj9mZAe2zhjGGEe1qRZ3MpGpBNnIyM0zWUgCYN3Ug/qvEcKsC0rnHNsYQZZ1zcrf6WHzjUDQoCocmDW9bC99NGxxKLlalIBvFM0aHSl9Z4bmungeePbs7bogxYVsyQQIfEULOLnlCp8jsRzpqSZms+rWori1DkEqc31//0PKbQICZiojGE8ZYhJbzARuBvkoU5UTiiWVMkma7iE0l4kdedgaObVYtpIkVnKTJRAcAcy7pa8oa2q9FtZArTqzJcJzIlCQCL3UYBVUcEqokEjmpRJPqhSieMRoDpYyc3CE1vLbOFxITB6MiW+jqVs5DxdysiIXi51cPwMndG+CKoS0wqJV5wXjdqNY4vWdD3xRmROxY1ZEtzMnEsc2r4YkzuqJnk6KQW6fwflCzB6tuin4zfUxbrZWr1MNtMKZjnYRnyfzXiJZYfcfIuM+ZOkQilabVC01KluzMAI7vWAcLrhuM24KJz3o2KTLNKTcc1wZPntkVx0mJZ6yUA7WSLDdE/xbVE6YcTgQk8BExUbdyXmjh98DETklTMDpapo2MfGjpElWkItkZkttXghI4qPFeavF3wFuB26//NVD6FPkd3JYaIGLjhXN7RNSp+vc/OuGn6cMAIEIYlGFgKMzJxHfTBmPlrZFuQk58e+0gV+16NSnCb7ePDH1+7pzuuO2EthHthOZcRdVKlxVVCrJxz7gOEXUUVUKlWCyTtui5Z7w3d/5YqVclDzMnaWI+PSy4a1bMxW1j25XJ4pdwZuF1gy33/XLbCCy4bjBuOK4NZk46BndIGZIndq2P9y51X//OLaIQ91Dpnr57nOGpIJ7v8rV0xRD7ZGdlgZqJ3U+6SFnd5TCTdXeNwudXD0CXhuHnqrDIfTk1/CyuWTEXbWobIRfdG1XFcR0iFa+yJdAq3CUZKA1K/k7Zk1MRmi194sIBTVO+lp0ubbcV8rN59nk98PKUnmlzg6gpkL38LsnM8Lbhh10i4nlGd6htKh3y5dSB+OTKfhHt5k+zXhyo1KuSj38EkyQla0xSeaZCbhaKZ4zWFi0WmnsRhF+rUq7JTdEtdZS02VZkZjBT/7lZGTiluzke5NQeDSLqut0U1Nwn0/X1j271UcMhcYT4fXXp5a1oX69SRFY8lWqF2bhkUDNX/VkJzzJPnmm4Z75xQW+TAE75V1KfG0a3xgeX9XW8VoUb4qj2tVG/an4ojnNUh9q22TCdUGPKugRrhy64bgi+umagKVdB/eD9IsqsFOZkhuLIGqdR3JYTw9rUxAvn9MB5fQ0X1XZ1K+Higc2w8LrBYIxFrGuFwFeQY1bmdmlYFQuvG4yxnevick3MejxzXcw+r0corjlWjg0qAFIp9MAtJPD5xDUjWuHzqweU9TBi4qzejfDQKZ1d1eSS3f4q5GaZ3BOdSgG4xWvmUMHsc3vgqqEtMP14s8vF2w7uph9fYQgj11u4pqQ6mRkBk4ubDjW5yC1jIi0iVqj12P7v1GNC73OzjNiC2pXy0Lup2ZXV6TGgxiS1r5faVuTywmdXmQsvvzylJx4/vYsvZQ7uOikyjlBNSqSLZcvMCGDe1AGhz7efYFgXhLYfQCihUqrVr7xkcHM8e3Z39LZIQqRbb7lx3T+uQx1cNayladvVwyLn+LV3jsJVyvaO9SuH0q0LRMxNl4ZVcHqvRo7nJ1KHc/s2Qesonts9mpg9M6KNj1OVzj2kdUn9qvkmK17vptUw55JjcbqUbVskCnGq+ZrqyHWUT+5eH3nZGSHvrMwMhquHt7QU2p+Z3A33ju+AIk1ZMHGMlSLvxXN7YO6V+uzlsdC7aTWc2NmfbPlNg+7ystUzXSCBjwiRmRHAmI51QgH0V9kIbqPbu6trteSmoZihWZy5wW02QNXlsjA3E5cMbm5aWL5zUZ+IUhLFM0abCm4LraNsQZirsUilMsK3Xl4M3ycV01YXhWf2bqTNklVBcc28d3wHbfxcZsCYYmTLivyAPbFzXa3mb+6V/UKaRHW3sGA0jHOsBxEbTaoXhupIAUbm2mEe6/VZcayLzKpWcmXDorDGWixMJnYLX58ie2uvpqlV1DsrI2CbZIYxhpfO6+mqrxM62cfCdqpfBcUzRiv9m5U3FXIz8dYFvTHc5f+8Yz3Dk+KFc3o4tCTSHV1GaTfx9TpXQjva1a1kev6IZ0sstXBTge+vD3veFATr//VqUoQq+Vm4oL+9Nb9mxVxM6Frftg0A3D+hY4Rirk+zaimToTkdIYGPsEROg68GVtuZ54VyrEHVfFTOz0bnBvaakmXTh+GHG4cCMNwr3rv0WEwd3jKiYLyO5jUK8byyQGhRMzJ9f8f6lUO+2UA4aNgpJX+zGrGVAkg2xMI2KyP8/xvfpR6mDjc0+Lrfo1SJZn/7oj6YJllBczIDoQdAz6Cm9qzejQAYcRE/3zocN0qZruTu7p/QUWvxaVajAqb0M7S8amaxIa1r4Nmzu+PcvvoFwPn9miCHYnvSghfP7eE67fZNx7dBxdzM0LWc6OLuqUCvpkURgppMVgbD4huG4IGJnfDGBUYmQ6FQkxfhqpUeMJ4J8nPhljFtEQgw1K0SVqDdeaK18m/GuA549+I+IZcqovzQL+hW3CiYYVJnX3NT2mZyn0ZYc+eoqMdxxdAWuH9CR/RL82swJzOAk7vVx4Qu9dC9sfHMLirMwY83DUP7ev6EsIzrUi/ChZ4oW2hVRFgiJt0hrWs6JgyQqVbB0I7ZxXMUSa4XhdmZqFqQjW+uHYSZk45B2zqVcNHAZq4ClNUJpUHV/FBWpXpVzBYgWXAQ2vvG1QpC1qpkitmJF3eMbYdfbh0RIbBfNLAZ1t45Ct0aVQ3930VWw7P6NDK17VS/ssnqV01y7RC17irlhX/r/OxMk4uHnIVNbNdZEaf0a4riGaMjsmQxxtC/RXXLBf20Ua3xq5Ssg0gdTuxcF4+eFk7oUa0wR5t2Wy24WzxjNPo2r45l04djQhfjGpzU0z6dt47yMAfYseC6ISgqzAFjDF0aVsXyW4aHMmG+E3TPzcvKCN23MycZbtsjgla8jADDI5OOwSnd64dcY2UFXP+W1hbI3KwMk0WYKD+c27cxFt0wJGR9r6a4C142uDnys/VFtude2Q8TutTDd9MGIzcrAxkBhu6NqkaEdLihICcT47rUS+naym5gjGHGuA64d0LHtP+uRBgqU0/4zindGqBKfnZoEaDOJxcOaIrK+Vm48/2Vpv11NckYTu5WHy9//0fo88xJx2BR8U48/c06APZ1mAYoi4tK+VloX7cSfvpzt8n1qEFRPlZs/BtZgbCAeVLnunhryZ9OXzXlCAQY8rL1aYbFIq5dnUr4evV2PDO5GwDnOjey6+1Jneti36GjtrVz2terhC+nDkT9quH/99RhLZGdEcCsb4vdfhUiTTivb2M88ZVxP/8nWOj8XyNa4e4PV4bcsM/o1RDPzV+Pc4OxPVULslGvSh427DwQ0V+Nirm2Viwrvr12UNoknnLithPaaj0v1O8vZ9XVhTWJbKwTuobjZ0a2r42Rist/nUq52Lj7YNrHRhHRwRgzCXlDWtcw7bey7n16VX80rV6IeyeYE3a8GkOdPYJIV8jCR1jSKRjEe2Zvb5ryQIBhVPvaIQFC9ce/ZkQrU1kAOw2T6gLUqlYF3CRp7kQdQF0aZcYYLh/S3JSoZGIwgYBcl+6Zyd3wxBldTYLQvyd2wrq7vC8a04GZpx2DNy7oZbKiXGqTpU+uv5SZEcDkPo0d06U3KMo3/d+rFGRjuocEMUT6oJMB/tm/CX6+dXhIABFzkRxf2yhoDXgyimxqd57YPiLJUJ3KeWlVc8mO03s18px9WMT9dpJK79Svmo/iGaMd6+GJRB66siwEoSI/G4a0rhGRBKR93Uq4+fg2EfVlCYKwhmZfwpKaFppyrwWMDfegKli8fmdom5qVywp1opfjQYBwqYFR7WvhP3N/M8WmAcDlQ1rgckkYnNSjAU46pq7JPaRGhVwMbZNcBT/Lkoq5Waa6OwBw5bCWeOiz1REZ9wCKlSJiQ8h7HaXYEcaY6R49sXNd5GVlmBKAtKlTEV+v3h5Kr+4FnQtxeebm49toBW+ZvOwMvHFBL22MtBMPntIZv27+O+2TYRD+I9dsPLVHA8z7dZu27AtBEPaQwEe45pMr+mH5xt2us67Z0aFeZZzesyGe/269Y9s3LuiNwpxMUxIZgdAENq5WgDEd6+ACh0Qv6kKScI8q/H9x9YByH/NExM6YjnXw1NfrQu6cOhhjEW6CU4e3xOj2tbXzAuGNyS5LUKiKILcU5mRGfSxRvpH1iXZJfwiCsIdWvoRrmtesgOZRaHdlZp8Xzqh529h2uPUEZzc+N/VQMjMCES5aRHxpVK38FKcl4kfH+pWjirnLygiEakcRBJFe3DC6NW5/7xdXydsIgnCGBD4iIZQEc/HrMi5Gww2jW4fSCRMEQRAEkT6c27eJZekdgiC846g6YYzlMMaeYoytZ4ztYYz9yBgbKe0fzBhbyRjbzxj7nDHWUDn2acbY34yxzYyxK5W+LY8l0gtRosGvIOtz+zahFN4EQRAEQRAE4YAbW3kmgD8A9AdQCcCNAF5ljDVijFUD8GZwW1UAiwC8Ih07HUBzAA0BDARwDWNsBAC4OJZII0a0q4XiGaNN9dkIgiAIgiAIgogvji6dnPN9MAQ3wRzG2DoAXQAUAVjBOX8NABhj0wFsZ4y14pyvBHAGgMmc850AdjLGngBwFoAPAZzkcCxBEARBEARBEAQRA56jYRljNQG0ALACQFsAS8W+oHC4BkBbxlgVAHXk/cH3IkuH5bFex0QQBEEQBEEQBEFE4kngY4xlAXgRwLNBK1whgN1Ks90AKgT3Qdkv9sHhWPW8Uxhjixhji7Zt2+ZlyARBpBg1K+aU9RAIgiAIgiDSBtdZOhljAQDPAzgM4OLg5r0AKipNKwLYE9wnPh9U9jkda4Jz/jiAxwGga9euDuVhCYJIVT68vC9qVMgt62EQBEEQBEGkDa4sfMzInf8UgJoAxnHOjwR3rQDQUWpXAKApjNi8nQA2yfuD71c4HRvVNyEIIuVpVasiqhZkl/UwCIIgCIIg0ga3Lp2PAGgN4HjO+QFp+1sA2jHGxjHGcgHcBGCZlHTlOQA3MMaqMMZaATgPwCyXxxIEQRAEQRAEQRAx4KYOX0MA5wPoBGAzY2xv8G8S53wbgHEA7gCwE0APACdLh98MIxHLegDzANzLOf8QAFwcSxAEQRAEQRAEQcQA4zy1QuK6du3KFy1aVNbDIAiCIAiCIAiCKBMYY4s5513dtPVcloEgCIIgCIIgCIJIDUjgIwiCIAiCIAiCSFNI4CMIgiAIgiAIgkhTUi6GjzG2B8CvMXZTCZFF32OlGoDtPvcJ+D/WeHz3ePSbKuNMpT7j1W957jMV7nu6llKjz3j1myp9xqPfeI0zHvd9Knz3VOkzXv2mSp/x6DdVxhmvPpP1Wd+Sc17BVUvOeUr9AVjkQx+PJ+O4EjHWeHz38jzOVOozlcaaQn0m/X1P11Jq9JlKY02V7x/Hcfp+36fCd0+VPlNprKny/VNlnHHsMymf9V7GVV5dOv9X1gPwgN9jjdd3L6/jTKU+49Vvee4zXvg5VrqWUqPPePWbKn3Go9/yes/Ho79U6jNe/aZKn/HoN1XGGa8+40XCxpqKLp2LuMsUpIkkWcdFEET8oPueIMofdN8TRPkiWe95L+NKRQvf42U9AAuSdVwEQcQPuu8JovxB9z1BlC+S9Z53Pa6Us/ARBEEQBEEQBEEQ7khFCx9BEARBEARBEAThAhL4CIIgCIIgCIIg0hQS+AiCIAiCIAiCINIUEvgIgiAIgiAIgiDSFBL4CIIgCIIgCIIg0hQS+AiCIAiCIAiCINIUEvgIgiAIgiAIgiDSFBL4CIIgCIIgCIIg0hQS+AiCIAiCIAiCINIUEvgIgiAIgiAIgiDSFBL4CIIgCIIgCIIg0hQS+AiCIAiCIAiCINIUEvgIgiAIgiAIgiDSFBL4CIIgCIIgCIIg0hQS+AiCIAiCIAiCINIUEvgIgiAIgiAIgiDSFBL4CIIgCIIgCIIg0hQS+AiCIAiCIAiCINIUEvgIgiAIgiAIgiDSFBL4CIIgCIIgCIIg0hQS+AiCIAiCIAiCINIUEvgIgiAIgiAIgiDSFBL4CIIgCIIgCIIg0hQS+AiCIAiCIAiCINIUEvgIgiAIgiAIgiDSFBL4CIIgCIIgCIIg0pSYBT7G2MWMsUWMsUOMsVnKvsGMsZWMsf2Msc8ZYw2lfTmMsacZY38zxjYzxq6MdSwEQRAEQRAEQRBEGD8sfBsB3A7gaXkjY6wagDcB3AigKoBFAF6RmkwH0BxAQwADAVzDGBvhw3gIgiAIgiAIgiAIAIxz7k9HjN0OoB7n/Kzg5ykAzuKc9w5+LgCwHUBnzvlKxtifACZzzj8O7r8NQHPO+cm+DIggCIIgCIIgCKKckxnHvtsCWCo+cM73McbWAGjLGNsCoI68P/h+rFOn1apV440aNfJ5qARBEARBEARBEKnB4sWLt3POq7tpG0+BrxDANmXbbgAVgvvEZ3VfBEFr4RQAaNCgARYtWuTvSAmCIAiCIAiCIFIExth6t23jmaVzL4CKyraKAPYE90HZL/ZFwDl/nHPelXPetXp1V4IsQRAEQRAEQRBEuSeeAt8KAB3Fh2AMX1MAKzjnOwFskvcH36+I43gIgiAIgiAIgiDKFX6UZchkjOUCyACQwRjLZYxlAngLQDvG2Ljg/psALOOcrwwe+hyAGxhjVRhjrQCcB2BWrOMhCIIgCIIgCIIgDPyI4bsBwM3S59MA3MI5n84YGwfgYQAvAFgAQM7AeTOARwCsB3AAwN2c8w99GA9BEARBEARBEGnGkSNHsGHDBhw8eLCsh5IwcnNzUa9ePWRlZUXdh29lGRJF165dOSVtIQiCIAiCIIjyxbp161ChQgUUFRWBMVbWw4k7nHPs2LEDe/bsQePGjU37GGOLOedd3fQTzxg+giAIgiD2bgW2rnRuRxAEQdhy8ODBciPsAQBjDEVFRTFbNEngIwiCIIh48vKpwMweZT0KgiCItKC8CHsCP74vCXwEQRAEEU82fF/WIyAIgiB8YteuXZg5c2ZZD8MTJPARhBN/LARKS8t6FARBEARBEEQZQwIfQaQbaz4DnhoKLHikrEdCEARBEARBlDHXXnst1qxZg06dOmHq1Km499570a1bN3To0AE332wULiguLkarVq1w7rnnol27dpg0aRLmzp2LPn36oHnz5li4cCEAYPr06Tj99NMxaNAgNG/eHE888URcxkwCH0HYsesP43XrL2U7DoIgvLNnM7BjTdmc+8gBYMPisjm3W9Z/C6RYpm6CIIiyZsaMGWjatCmWLFmCoUOHYtWqVVi4cCGWLFmCxYsX48svvwQArF69GpdddhmWLVuGlStXYvbs2fj6669x33334c477wz1t2zZMrz33nuYP38+br31VmzcuNH3MftRh48g0hcRKEuLIoJIPe5vabxO3x2f/n9fAMy5HDjvMyArz7zvvauAJS8CV6yIz7ljZdUnwIvjgeF3Ar0uKuvREARBeOeDa4HNP/nbZ632wMgZrpt//PHH+Pjjj9G5c2cAwN69e7Fq1So0aNAAjRs3Rvv27QEAbdu2xeDBg8EYQ/v27VFcXBzq44QTTkBeXh7y8vIwcOBALFy4EGPHjvX1a5HARxC2lK9MUARBeOCDa4CtPxt/dbuY92380Xg9sCvx43LD3q3Gq9+LJYIgiHIE5xzTpk3D+eefb9peXFyMnJyc0OdAIBD6HAgEcPTo0dA+NQtnPLKQksBHEK6IwcK3/E3g2/8CA6YBc28Gzv8KyHBx6/29CTi4G6jRKvpzEwQB/DIHaH2c9+NKjgCP9QeG3gI0H6ppIOYF3cM5uO3RPt7PmwhYMKKDvBcIgkhVPFji/KRChQrYs2cPAGD48OG48cYbMWnSJBQWFuLPP/9EVlaWp/7eeecdTJs2Dfv27cMXX3yBGTP8/14Uw0cQdggty5IXgdIS78f/vQl4fTKw8QfgnYsMS8D+7e6O/Xcrqt1FEH7w5pTojtu7Fdi6Anj3Evt2qVgTKjRmEvgIgiC8UFRUhD59+qBdu3b45JNPcOqpp6JXr15o3749xo8fHxIG3dK9e3eMHj0aPXv2xI033og6der4Pmay8BGELdJC7sgBIKcw/JlzI4tnk4FAwEJ38vPbUvsoBEaCIGKn9KhzGx0ZQS1tyRH/xpI0UHwyQRBEtMyePdv0+bLLLotos3z58tD7WbNmhd43atTItK9FixZ4/PHH/R+kBFn4CMIOO8398jeAF04CFj9t10H4bchC6NEasOw1b+0JgjBTGqXAJtwedVb5Q3uATUuN97NP1hyb5Fa/UEIqqjFKEASR7pDARxBuURdGIt37ns3WxzCNwOd1Ifjmud7aEwRhJlorlnzP/6mUWNgc1s5ir24O0Nzne7ZENw47jh4C7m8N/PqBxwPJpZMgCKKsmT59Oq6++uq4n4cEPoKwg0m3iOqSefSA8ZqZA0s2Lgm/Pyx8upNc808Q6YAp+6QPAt++HeZ9zOHxqbvN4xGTu3sDsGcj8OG13o6jkjME4Z53LwEeaA8c3lfWIyGIqCCBjyBskVZt6sJIWOw+u9368KWzNRtpgUUQcee7R2Pvw87d0U7gO/i3vtzBgZ2xj0ll+2/RHUdJWwjCPT88B+z6HXjnYu/HLnwCeLi7/2OS+fVDoCTKWOUUhJczRZUf35cEPoKwQ+eSKfj2oej6pJgZgog/0SZqkTHdq8oDVxX4vn8q/H7rz7Gf2y0vaeIHXUEWPoJwhVxLc8uK8PsnhwDz7nE+/v2rge2/+j8uwaq5wEsTgS81Y1k/P1xzM03Izc3Fjh07yo3QxznHjh07kJubG1M/lKWTIGyRBL5tK4HC6vpmS18GProOuHoVEMiw71IsIresAB7pA1y2FKjS0Nzm9+/Mn3esAYqauhvypmVAdoH79gSRjviRFdfWwqf4bL53JdDtHOP9/r9iP3e8oaQtBOGOuxvqt2/43vjrf43xedtvhqKpZptwG9UV3G++vA/47Dbj/c5i4/XdS4zznjIbeGYEAAZM32XVQ8pRr149bNiwAdu2bSvroSSM3Nxc1KtXL6Y+SOAjCDvkRd2zxwHTd+vbvXcVcHivsdDb8D3QapS15lwssN48HwAHVr4H9LrQ3ObrB8yfV7wF9HMZ1PtYX+PVaqwEUR7ZuxUorOHtmGhdOg/v9XaeaFn2agwHk0snQTiydl7ktr/WAQ91itz+f92MV/nZu2Fh+H1pqXUJp2hVmZ8LAAAgAElEQVQRwh4Qnq9+eE5plF73eFZWFho3blzWw0g5yKWTSF++eRD4T7vEnvO+ZsDLpwAbFgNv/VPfJmThC8b4fDQtss1vSsa9fduBhzoDvy/wb6wEUZ5QreZuiFbgqxSbJtY1pkQtHpNBifGXE7coopyzdaU+rtaJr+4zf2YM+OVd98fbJX6zY8MiYHol4O+N7o9R7+Wlr7g/lkh7SOAj0pdPbgJ2/+Fvn//tAhzer9mhLLYO7ASWvazvw40LVYHiOvrHd8Bfa4EFj7gapiuOHAR+9vDgIohUJpq6eHbCkF1/Thk8/eDQHmC/xl1s1+/ANw8ZVgg7kr1OIEH4ycwewKPHej9u3Zeajcq9Y5ssxSYPgB0iJnjN5+6PUdcWOmWyF3asMZTXRFoQ96cSY6wRY+x9xthOxthmxtjDjLHM4L5OjLHFjLH9wVeNjZwgXLJ5eXQaPDvUhduO1UbNLeErLwiVXAiy6UfrPu0Evn3bjQDsxv2VY8TC08Mi7bPbgW//a2gJ924Flr8JHD0c3j/3ZuDV04Hib9z3SRApSxQCzjabRAu2ljGX5/r53ejTvMvJI2QeaA98cqPhcnannaWRkrYQ5YQ1n4Xf+xFTpypLvv6PTVtpDeElkVRmtvF69GD04yo5En4fzX3+32OAJwd5P45IShJh4ZsJYCuA2gA6AegP4ELGWDaAdwC8AKAKgGcBvBPcThDeebRPdBo8O3RacM6BBzvaH2dXquHjGw1ffh0vnQy8OA44oloRg5P1ijeN19fOAp4/0X4MX94LfHyD8f6JwcDrk4HPpXHt+t14PZg+wdwEEUa5d6Oxur18ivW+WJOdbF5uKFzmXBHd8W7Of3iPofBZ/WnkPkraQpQXvvp3+P3sf5j3LX/TuEd0ipc9WyK3bVsZKTzt/gN44zz9ueU1hJ1L595twKpPwp8XzzJeSw5rm7tCFvh0c4Bb5s+M/lgiaUiEwNcYwKuc84Oc880APgTQFsAAGEljHuCcH+KcPwTjCU3qhGTj1w+NCTHWzHOH9wM/ve7PmLzgxY3CDbEukFbOsajPh7Ab1q/vW59z8Swjicuaz4z/y48vAHs2259zd1C48xIPQBDphBcXxn3bjXtLxyN9gDlXOswDLrTpRw8Zr8teAZ4eoW/zzGjg1TMsOvDwfb6YYXM8WfiINGRmr7AyRbas7VhlbvfFXcbr7g2RfTwzUt+32nbFW8BPUgIlud7mCyeF31utRfZsMeL/XxxveOHI1ns7gW/HGut9AHD0gH5MXonVNZRIChIh8D0I4GTGWD5jrC6AkQgLfcu4uZDGsuB2Ipn49r/G6z0xZkX65EbgjXOA4q9jH5MXYnFZ0h3rh0b8nYv02wMWiXNlV9X/XRbZ15NDjfeLn7U/r+77vHxqeN/SV8ILUYJIKzwISNtXWe/bshxY9BTwyumR+8Q9FGGh15ApObP8Pt94Xf2peTG5/mvg53f0x8ccgxecC8ilk0hWfnrdULwciiLr7dafgUVPG+9lS5cVuvvgLwuBSrUG5lQ0f37tLP1xv7xrKJNU/lwUfr/2C+CR3vbjEqz6OHKbrn+CQGIEvnkwhLi/AWwAsAjA2wAKAah543cDqKB2wBibwhhbxBhbVJ7qbiQNfgX3CytUomtUxSKg6Y6NxwJJLDAzsqI7Xljw5k6Pfgy/fQS8NQV4fED0fRBEsuImQ96az4343JxC57Z/aywCq+ca99DcW5yPZ0q9zmWvGdYAebEnWP6G5njl8e11ng7NYyTwEUnK53cYr04eLI5w7VuD4H3z2pkeulPWBflVzJ+tEib97zLg3qaRSlX5Xl74uP25ACMD+LZf9W7qQoEbAd3n5Z24CnyMsQCAjwC8CaAAQDUY8Xp3A9gLQFGLoCIAJfsFwDl/nHPelXPetXp1i8LXRPzwS+ATBcn9KIjsBSeBb/efhhZRDuwOHRsnC5/Kw12BX+bElt2Pc+CAkzAtP/iU73bob+N168/WMYYEkaq40fI/P9aIz9VZ2t0oeoTb1KYlzm3VefXNc43Xg5r6ma+frUnS4mVetpnHolFgfXyjQ0IYgvCBv9YarzGvQeTjletd9L1tpfvu1JCMiGRxHPj1A+sQChGrV3LEqJknrynU9ZG63thZDCx9Cfi/7pqOGbDL58zkRNoQbwtfVQD1ATwcjNPbAeAZAKMArADQgTHTndwhuJ1IJvxKMS402n7H1AH2/ulOApoojLroGXfHxivJwSuTgF3roz9+9dzYzm/KJuZicUwQ5Y0P/mW/383ccPRw5CJPh5oo4ZHewLx7w5+tEkq5Rrh0RjGffftQZGZigogXsTxz13+rJE5R7xFp377tRq6BH180smVHC4eRgM0qiVxeZeP1x+eBdy8xW/X+VMsgKOPNkPMaqnMABzIswkLIdbvcE1eBj3O+HcA6ABcwxjIZY5UBnAlgKYAvAJQAuJQxlsMYuzh4mMbMQpQtflv4YhSYSkuMxCVyiYG7G1m3dzpfyN/epTUvWbPaHXKxAFv+BrBkNrB+vvEglJEfivEQygkiVbBaHC14NLrjZG6vbizyllrU6RR8dlvkts9tsv8CwC2Vrff99LrZnT40j9FCkEhyYhFWlr6k9CU9v/dsBrb9Ev78+mQj18A7FxrZsqMmOF5dnUwAyMo3XkUs4NovwvtUC79aBzAQDPtQ4wZD+y0EPrrPyz2JiOE7CcAIANsArAZwFMAVnPPDAMYCOAPALgBnAxgb3E4kE365dIYsfB5q0ej44VnDF171dbdi0dNGkXEntA8VnRCYpMKQtkCshrcvAJ4ZARxSXcdkgS/G/xFBxIs9WwwX7F/meDvOyzymVeq4WTB5WFTt2WS9r/gbe8+K7x4Bnhxs3ibc33Ts3mAkzJIzfor5TjfvzbnCOo07FWImEsG6r8LvM3Ni7Ey28En39qeKUmX3n8A+H/JE7HZwqxTu5W68p9TnesgV20LxbCXwJcLCt3uDMTfL5SWIpCHuAh/nfAnnfADnvArnvBrnfALnfGtw34+c8y6c8zzO+TGcc5tq1UTZ4ZPA5xeicKrsxlm/h3X7T24EvrjTXd+/zDEmrNtrGp91k2qyxrct1rikekHWMpLAR5Q1h/cbbtbqQmVLMF7m+yej7/vQHsNLQPT928fA9tVSA52ix8WCyYv1386KvvQl68Xgxh+BD691fx4gLFyaFqLB77P288j2i542p5OXEbVACSKePHtc+H2sXjXMIoavTqfIdmoypXgQ+j7RrK0kRY1OiRWwSvzmUeBz4zGkItxRRQ1BIqlIhIWPSFUWPW1dkDQW3r7Ap448TGDfPAg8O8a+zco5Yavh0aBFUCvwpakw9INU0oFcOomy5rPbgDmXR9ak9KN+3BvnGV4CohzC7AnAw13C+x/rH3mMGwu6VbkVHXbzyM7icBIllViy6MpCK8X0EKnCsleNtcg+CxfJDYsjQxSskJ/plepH7rd0ifST4L0XTX6EkGVeszb5+R1gq0UajBVvO/d9T5NwuZmPb/Q+tmQzDhAmSOAjrBH19/Zu8ac/31xDHTZ+dL1RYkBl3TznvlXhVueakKwunX6SrkItkTqIWBY1DkbMI54FFjk5Q9Bty8pyoLvHFzzi8XwO2Fktir+y3hfbSY1F4V/rzL+f7LWw0UWGUYJIJCJ2Vi2aLnhykHWRdMAsWMn3nW5NIBdQjxe8FNjyM7DGwm3ajqeGiU4i99k9t1dp1kQq+3cYtQKB6FxbXw0Ki6RMSkoSocogUhVx0+60qClT1lhNKvMfNv50HDkAZOVZ96lq3MTkJ1MerF8k8BFljbgXIwQjlxY+VcEkfxbvLd2fEkBZzCOcG3F8mXnA8Q9K20sQ0v++EEuyCoKIBzbxpjp2qAXTLWL4vlMy4SYqIVtpCfBIr+iOFTV3E508zsqFVN84rkMhooMsfET54jOHLHduBJ3yIAzt3aovU0EQiUIsLtT7zbWFT1mcvHKa9TnKgj8WJP6c4jc7esD8u8rv92931wcA/O9y/8ZGpAc/vW6fQCgaQtecS0FCW6NOdFFqPXckyjL1nEN4iYoYl/xMjkbg85J/QFV+//ah+2PJwpeUkMBHJA41PXLUxBDD4+SmsPEH5z7Kg0vn65ON+Kmtvzi3JYi4ELzP37tKvz0WkmFBcuAv5za+I33vEikhdrRKrFgTRRHpxxvnADOjtF5Zolj4So4C3z4MHD2kb266nlmkYscyi2QSzAs6So8CW1caz2QBLwXev9pbP17WLupvdmS/lxN5aEskChL4CBt8vGn3+pDqWBCLVt4P61x5cOkUWf2sHqg6lr8BbPs1PuMhyh9W93nUMXzaznzoI4Uwxe1ZWPisEEm85OzITpSWGgtzvxN/EcnNURdlkKIieP2+dDLw8fXA1/9xPuTIAWD9N+Zth/cCH2gy3SZrBu6Sw5G/aTTz34ZFHhqrc2OsZW2IsoYEPiIxlB7xv083k72Kl0nylqr67eko8D051Pw5mqLMr59t70pDEHbsLAZ+fEHaIC0wvnkwfN/J9TwP7zfv80x500RL31fW2Fv9fmukkg0i1frOYven+/ltY2Gu1jsjiGgQz6XVQQudWqRch86Szkv1SZhEfFyycWcd4INrlI1RzF3PjHDfNhbF+qqPgaNUUjvZIIGPSBBJokn3onmycn9Ixxi+DQvNn52KuxKE38w63ihr8Mwo47McQ/LJTcDyYP23jGCildIjwBd3GfuWvmQogI4EteBr57kro+CHlTAjO/Y+EoX8fT+5Kfzeak5bKRW3F9+zxIPVX1glDu5yfwxBRCCs+srziHNgwWPujjVtSpL1iBf8ivl1W79UnRu9/mZxs/IS0UICH2GNn3Euam0bzmPoXzfxuOzr57eBQ3vDn9/2UDdLsGS292NSDfFgVTX/Mf3fiHLNL3PChXmXvWa4B+6SNOp/bzBehfuVusA4HCwELOaS0qOGaxYAfPcoMHc68NX9xufnxgB7NkaO4av7DRcvwdPDItt4pWbb2PtIFE5KLDvXS5HRtESjubecFywW6gThBWZ1HXHn5GI6QeX1s30ZVkry3lXArj/86ctuPfBQZ3/OQfgGCXxEYlAn6ncuBm6pHF1f8gS+fRUwf6Y37Zdc12vJC9btrPjtA+/HpCpPBV09S4OZzW6pHF3a9lIpM9r21cAPz/s3RiI1eGUS8MQg472odbXl5/B+J6FAXD/y/S+EP2FBcooV+/RW4Mt7YVIQxazAcKH5nhjFPBMPrFw3hcCnusnLv42wuKp97N1mzAtay4E4PgUtKkQSEbx+VKu9p1IBRIhfXaxh1N9VnSdXf2rc91/epz/eKdsvkXBI4COs0U2kUadbViYLr4JWaak+oPrRY4GPpnkcShrG4MWbW6sA70813nstFlty1Dh+7nTj82N9gXcv9nV4RKqhZNot0bgURrhvady5hMBXEowRZgzYvcH+1If2hC2Nun694iY+ubBmbOfwC0uBL7j90B7zdnmuFM+DrT+b24iYvqUvG6/y/1InpBPpS7y8P8Q9GqGQKI0sH6CSjjH3sfLBVOc2Ef9L5fMLJxmvCx71ZUhE/CGBj7BGN3mryT1c9xXjouo/bYB7m0Zuj8ZPvKwyceUXAfWiSGpSpZHvQ4mK758Iv/eSqU/E/IhYC0/pnYm0RM20ufwN8/6f3zWyQsqItvKrrjj7S6fYn7tYydhnmaI9SK6DJ4JdJtvCmkD3KUD9JElmdMgiycXRg2GhWcZNJk/x27OA4e5+WxGwc73SyIXAV1pCi/NUJ14CnxwDunm5fEI4Xluk4PUH4b6pKud08waRlJDAR3gjWjO9l6KdOvZskrJtxagtLqsHQL1uZXPeWPljYeS294L1fzgHvnkI2P2n9fFWRXOTNQU2kQAkC9+mZZHC3auna45RriNZ2RMSOliklUpFFVxemmjf3inhSF4V/faCGsDVvwGj7rU/Phn49r/AbdWAv5X7WGTaKzkK/D7f4mDJirfiLeP9tpXmfW64vyVwXwv37Ynk4vsnge2/xd7P0cPA53da73+0T/i9G5dOP5UIzaJUeKcaJUf1Cp5PbzUUOkdkRTvF9KcKJPARiWHOFf71Fat7kHgAJLo2VLSaMFUwunKlvl28eErzkBOL6p3rgE9uNOoiWSEEbFX7q3uglJZQOufygHwPP9YX+OM752NEshVxGe1YFa4XGVLiMOesmdFk2e1zmX57lcZAm7H6ffW6ej9PoqnZznhd+pLxumONeb+YI48egCvUOD8vLp37tlHcTyrz3lVGiEWsLH4GmHe3y8bc7NJZ/A2w4m1zEz8FvlNedm6Tmevf+RKB7pl7X3Nzhl7AUKoJxRx56aQkJPAR5Q+xOPxuZmLPW3okcuFTo42742Qyc/wbU7SEHrLB77N5WXif6uJmVeJBZ2l9YRxwe3VfhkgkM1EUT597c/AY6ToSrqClkoVPzQis8tca+/1aLASWet2AHv8Ezno/cl+yZLO9SGOhF+RUMH8WNQ4Fv75nvNq5rcr/y1AGVTFnUdKWcoG41q3iWY8cdO/R4WShV88rP1NnjQJeO1Np46PAl+Ewt4z+N/DPb+zbJBsvTjCeufL/R1u7UHah9zPpFZEoSOAjrElEoH1Uk4VPFj4vmr9zHOJ83DD0VkSM3er751YCTnvTiAE6RnmAJUPdLzHxC21mtrRwVN3zxINEXQzofv+1n0duU/nzB2Dxs+7GSSQnlmnWHSg5Cq0LUSAg7feZiS9Yz4WMGedu1Ee/3w/+EWNGW7s5NrvQ/NlqatXFTwuEUAiLBaFdv0R64HQf31ETmHO5u75EnU2350100pYOkjdL5QbmfbU7Wbt4J5qR97hrJ5KwvTjeoSGX3OhJyEtFSOAjbEiEwFcGcVziAfDFXe7aD78r9qQLg28C6nTWLBwtJk7OgWaDjRigSnXN+5JB4BPjFv8/OZ5KdfeQNaxyDbRoNa9PDAT+d2l0xxKpzZOD9QKMEPR0NeJipfXx1otKWzdtnxZFFevEdnxuRet92fnmzzorjOrmqfLbR+H3TIrNBML/qx+TpCwFER/snuPiGvjBpZLOSYDz2v7PRd76s+L0YHyqLJBOmaeMhQEBxUpeFvS+BOhoE2ahwyn79qZl4We7aQ4m4S9VIIGPMLPgcePGBhJk4fMo8B3a40MMn8dYOj9cQnpbxABZntNmEvWiAY0Xwu2G66x3yv9H1rDeUUu/nShfRGvh27QE2gWGKMpeYud6GAOym+igG4Ah0yPbHHMmMObh8Ge/XJ1ine8q1gEq1tXvy1ZcOreuiGzz/In2/YdctnVuXrQYLBe4EfgAl2WdPF7viYrFr9zQeJXngvyq5jbJIvA1GehOcD60132f3/1f+P2u38PvD9v0Ee/EbJwDB/+O7znSCBL4iDDr5xv1WR7rG9yQhALfXfWAvzfGds6SI94mIj8EE6vJ12pRaPW79L3aeKjU6mB8rhCj9j9air8yrHVuhGGrNvJ3XDsPWPqKtzFQ7EAKo1iCvGBnLYpXinB5kdfqeCMDJ2BO0DDmIeAYObuoXwJfFI/pBr3Mn+t01rfLLnDu65DDgiqUmVFJkb9hsdm9+4fnjeyLdN+mH7YCn7TPyVoMGElb3LJnU2RdSL/IqWT+LBQvdjHCLBAZBysQc0YiCGRYj0PmLgtFkBO6LMpiTSUrl7506VYaLfP/D5hR3z5LOBGCBD4izDMjzJ+T0cIHAFs0WmgvlB7xdl6vFsGa7SO3hX5L1folxRyNlNK3W42v9yXGa6dJxmulet7G5idHDwJrv3BuZyUw79seXvw9NwZ4a0p4n5tFIVkIUxe1Dp8XfrSJabO6JpwSuTgha+0zssIuzJk27tVuv1tmnv3+aAS+s5UyOJZ9uBij2+/x90ZzjM+Tg4BNS8P7373YyL4oJ3gi0gO3Ap+OHWuAL2aEr7Mdq92fV7Y0+c1FSuZgcQ+JuaT7FERiY+FLZLI1FnCeN9wI31borKpCsdugZ3jb8jejP4cbfg0my3JlOSYSIvAxxk5mjP3CGNvHGFvDGOsb3D6YMbaSMbafMfY5Y6xhIsZDSJSWAAes6kwlQOBb95X9/tKSyELK6+bp27ql5KhHgc9GsOg+JazlrxgUvnQWLSvhuSiYDGH0/UAP6QFyqkX6Z9FPj/OB8z4Hmg60HpuK3xpGFgD+p3FVVb+rlYVvZg9gwaP6fW4sNV4FcSKJiMHCZ4fVNXH8g7H1G5DcqAOZYfeu2p0i24a2ufxuaoxuBB7n4fxqkdusFqG8FMgvcujQ5ffYsylcb3XPZut28UisQ5QtsQh8r55hxNN/80AU542jtTjiPhIWvuC9pCZsAYxnok65VKWRkYgtnsgWSZbhLPA9d0L055JrkxYEs2qL/7OswJLXAkcOAB9O89cFU4S37FznX59pTNwFPsbYUAB3A5gMoAKAfgDWMsaqAXgTwI0AqgJYBMCjTxcRMx9dD9zdUK+xSYSFb/YE+/0bvg9rcfzis1uNGl5usRP4ipqHJ9axM4Hmw4Eara3bq79po2ONwO8uZ5u3NxkgHyS9DYT7qXuMs9vGsVdKH8rIlcrOffaXOcCS2ZpjXAhz8UjQQSQGcR37HX9hVWMvGiuZjLyIC2QCzYcE79uzItsOujE4FmXeqN3ReC2s6W1sXsc+QkpGlReMMbKaJ+RSClYc3O3t/ADw0XXW+8oiURcRX2IR+A7sNF7nTvf3vLGi3nfiPhECn5hrGveXjmH6+1WnGNLRMIZsv/LSwo2Fb7+m9EI0HD1szOPi98iS3NzlMSyeZZTCikawt+KP743Xdy/xr880JhEWvlsA3Mo5/45zXso5/5Nz/ieAkwCs4Jy/xjk/CGA6gI6MsVYJGBMhEHWstAuvJMilHY8JffNPwFPD3be3K9QsT/C1OwKTXnXIoqn8pp1PB+p0CqeV94rdpH7am8CAadb7Y7X4Wf1vVK2rXZzfwV3Aak12MDcWvmgWokTZsE8pqC0UH1/929/zWHkrxCrwVW8Zfi8WfnU66ZViolaXOm+c9R5wxrvAhRauYla4GXudY8Lvxf13+U/AJYsd+uDuYn38xI8kWJwDn94KbPfg/kfEj2gFvpIjwN8xxF/FVeBT7m0h6AWU+3vg9dIxAQtFObfYLtGwj97tMyMKV1A3At8Rn5LdHNptxNEJgV2OC5bHIH6vw/sNQ4PqBVD8NbDwCW/nFplIu53r7bhySlwFPsZYBoCuAKozxlYzxjYwxh5mjOUBaAsg5ODPOd8HYE1wO5EoQjekxvrjl4VvZ7E//fjJYQ/FXe2KDhc1c55Yj71Cvz2QFZnlywn1AWf3P6pQyzw2+dgz3gEuWgB0mezt/KaxuLQY2llItyw3u4cI3Ah8Cx5zd36ibFk/P7KOm7gu1RIesVL8VeQ90bhf7K5fzQaH3ztlyRVJC9RSLjkVgCb9Nfe8wzzrJuvf+Kcjt1VuED6XXdKoRGcV1C3SvSpv9m0HvrofePZ4f8ZExIbu/hJeQ/L/W20nl/OJ6rwJtPAJxUjEM1huZ3Evy0XLgbAbpKmNRU1BOTGULdK5AxmJ8dCSWfh48Nzy/Ch7JwV/v7VfAPMfjrTKzRoNvH+1t3MWBN1udb8nEUG8LXw1AWQBGA+gL4BOADoDuAFAIQB1lt8Nw+3TBGNsCmNsEWNs0bZt2+I74vKGVaFcY6c/53j9nBgOjmEMbt0onChqqt9+zlwjhk6tPaUi/7amSTiKRajtw0YiIweo2dZ6MVexnrEYrBJD2KzVw9ZtDJ9g9dzIbW5cOu0sr0TyICfuEIjrdv/2yH2xos5lp7zs78LQSUCq1tyw4smafzucFEayy6XVnGbKtqkrTG8Twxer9dMrOgWQl/TwQHiOISt/cqC7v0R8t2mf6v3h8NkJv2K3dBYi9TkmPve6GGg5Guh6jr6dFfJ9NuWLyP1W96LbJZB8LGOJF/gE8v9bHoLqCusUklFaCuzd6nCyGJJ/lUPiPdOLKsv/5Zxv4pxvB/BvAKMA7AWgVoStCCDC9MI5f5xz3pVz3rV6dZLkfSUk8HlINOKZBN+M1VoET+tTFkcrK1j9bsarrdAMG9dHzfapa4GrbeILcyubP4vyDCrVmgfHJv0PdYJnLIs9Pyx8Vrix8JVlhlLCPdp5JEGLkQp1DGHIV4HPRR3MGq3dW84GXBt+P+6pyP2mhZzmfj3rfef7eNmrFjtcuJr5zSGNd4WsvHFTMkfMKWW1qCXM6O6v0Lyvq8+IyH2A4epXFvS6yLmNuMcKqgGnzJas55prUKfskV2n86qE34skJ1YCX7MhzmMDgIa99edKNMVfh9/L97K61lz7hf0a4tPpwH3NgX07rNs4KdsJE3EV+DjnOwFsgP6/sQJAR/GBMVYAoGlwO5EoxA2jXZT79DB1kxK95Kg+s1s0D3Qx2cnZ4Kq11Ld1g2N8nUMRaavvoGtfUAQUKrF1YuHY4eTIvpoPAS5cENmPzg31gBSkLSbaWB4MfsTwWWFlvVslWQPtkuMQyU08F+py36PvC76JckHQWVNvKtYSDypyEpf24+3Pp1sQFlRTXLc133WgRRKVskig8vIpkdtkbf/XLuI6/VLmEf6gS6wmSgfJ1+OhPcC7l4ZzBqjrDrm4d0KJYT4y3ZPiu6r9KS6dstKoauNgk1L98/iEmc5j6H8t0P9fFmNKMPW6AdlBRz25KLtYx8jJYnb/Yd3PL/8zXkVSHy1k4fNCIq6KZwBcwhirwRirAuByAHMAvAWgHWNsHGMsF8BNAJZxzlcmYExECBuBz7dFmYt+Pr4euL+lw83tEhFjIy8ixrqYNN3SfBhw6ZLw59PfBI45U0pvrnzfPpfHdj6RBKbEIpawhibPkVVbgRCoaraJflyyUDbqPv12wFuRe4FVra5NP8bWLxE/tv0GfHhd5MN3v05DmyDLjLh3ol0Q9Lo4/L5FsE6pUwyfZxzGJlsKdfe1Gh+ko++V+u0cSIrkXHIs1+q5xtxm/8UAACAASURBVHfassI6iyvV4EwO9v9luOPqyvOIbI2yUuG7mcAPzxoxXEAZLNSla729lCHcjYAkW+VMXWqULVqnBqmdPIfU6gB0PAU48bHINdeAaeaslzJVpVCTgdPMfareBboYXy0+zAUZWeEcCXKdRKG4kmP27f7/dvklQm2C49V5DRARJELguw3A9wB+A/ALgB8B3ME53wZgHIA7AOwE0APAyQkYDyETsvDpLCo+LQTcCI6iftP+v4wsTqGsflGModu5xmTeWwoK1tXMiZZJr4W1cgBQpzMw5iHr7yknaYhG8yZcVOt2dX9ME019PjmFvMjQ1XSQ9/EIvpKEvKKm1gviaLTxr52l3256uNKiL6mY/Q9DQ79rvXn7vLsj2yZKAx26Fn1YWE6YBVy+3H/rZGENo7yLFbLW/7AuyY0q8Fl81yHTNYeW+m+xjIajknKOc2DxM8AjvY3sfzro3k8O7mkMPNBOv08s5mWBTzzXQwldyvD/2FpK+ON0Tzfqa93GlYVPajdhlrmvobcCJz5qhGHIfeVVBXr803pM/a9R+peFPOX8bpOa+KHMqt8d6HGB8b7lSGlIGuul3e8ecgG1ywAb/L0XPOJtjOWUuD91OedHOOcXcs4rc85rcc4vDZZhAOd8Lue8Fec8j3M+gHNeHO/xEAp2Ap9v6xoXHcmpjp8eHs7q910UlrmcQmDck2aNnJsJ75/fRG6r2sT7+e2IJiNe7Q7AJT+4izMQyBY3AS8FLv3RcFOr1TFyv1cWSVpDlgEcF6yvc+Qg8PV/gBfGGW66XhMyuIXqeSUXIauLi/s9XkalrALz55CFL8prRT4uKw+obCGARMulS4w5ZsoXwFW/6dvIAll1jTXfjYUPCCtkZOp3s7YgJBJTtkYO/K5xU5fZGPSwOBynuYVwj5VXjrh35Htod9Dis+hp4J2LgL1b4js2lTZjwu9lC1lMCiidcCdt63gqMOLu8Dnkgu7HXgFUkFy65Tp8/1oH5Ckx+zKcA9mF0jml75BTqDR2OeH6Udu21Whg2G3G+zxJ2a1b+7ix8P34gnUbUvx4ogwdfYmkQNxU2qyIPq3KZI2/XC9KsPtPc8pj2Z3v57ft+67XLXKbOom0HuNOK6+bXM//Mvz+xMeAMf917seOaLXpRU29WRYyLWoBVm0CnPBwuFaYn4gaQvNmGDV5Vs813HRfHOfjSaTfIF3duv7yKfNcwhHuTG6u0zhIfJl5kYqrWF06450MQHgK5BSaF34ycgxxU43lPr+qeTFl9V11i9quZ1unfU9kHJDsqspL7S0Nu/4AvrjLej+RHIjFuFwbs0Id4zUzx1jIe0nSUj2KmO1KimfPSVKdt1oWlkkddvOH/EwXwq24d469AjjxEaBS3fC8KNpM3x1pde9xvvsxgQMTXwhbAeV5V/VoSnRyo4wsoHJD87pSZ+ET14iuCLxo/+1D1ufZud56HxEBCXzlHTExLXlJs8/jJLHiLWDB4+Ztm38yF1atUCvyuP+00WsDj8aqbfK6WNN83xypSkjHk4FjzohpRDj+wdiOj4W4xkskqJ6XKetoGgp8y98EHupkTk6TKojrS354r/1C3zYewkSlepECn1iMRW3hK4NkAKqlz5S0RZmjOp4SWW/TCqsETVYCXyWfrZl2HFUFPguFFQD871JgWzDUX13QE8mDuOdm9ghvE0pVYZnyYlEarfFacSKvkvmzWthc1My0YmLQumQ3f4iQCyDsSdT1HMOyJ8fvu3FRdFpzTXoj/J6XGgqgkYrLfJVG5s/VW6FM4nQzsszZtnXJ7/YGy6x9q1GkO81pezYDy16OfnzlEBL4yjtCQ7Lhe82+Yvf9cG7EXX0w1bz9r7WR7XSEfPql/bNGuzivTTpo0Zdbv3TfFqHSd6jV3ryrYp1wBqtEE+/Fq5+poAs1igHjJOG3Vha+z24H1s7zbyyJZO3nxutmTe26ZGH/X3qNrLjuH+8P7N5gvH/uBH0fXpVJzYY6t8nMMZQA8nXuVNjd0eJeBgJfhZpAgZSp1yo2Z+itwAnBrIauBD6L31zNChxqn8DlgUng4/YC3y4ps188PBUIfygtDbveCkLK3+C16EXgi8v16JDlMeSSaGfhCwA3/QVcsSKs0M6taFj2ZK8huxJYbqkuZRtXx6wrDzV1DXDeZ+Z2tX0I53BDINPIDC5q6enWB08PM16z8sPbSkuNDOtbfrLvf18carimOSTwlWf+3hQ2uaua8R1r9EVtmwzQ97XwCf32r//jbix7Nhqv8mS4YaHzcdqJOrit1Wgje+aIGe7GoD5Q+k3Vt3OiQa/w+7M/jtx/1S/R9euGC+YD1/5usdPioVWouJJl5Ojb2cF9tvC11MQbAYqFz0JT+uW9wHNj9PuSHVGTqSzTajtxT2Pjb+OP5u2yJf99JaGAzF9r4VnjfNrrzm22LDdet0qVfao01rcVOAl8ZRUnKq7zIbeY7yv5+u91SXifm6QtVvfnoBssxlBWAp+NS+eBXcAOqU6pm5qIRHwosSidI+ClkQofsaYQl7EXl7xYr8fjpLWIeEarbpYRuFQaBzKc68KKPpx0SCe/FBmDP+Ju4NTXlHtYFfg0wmtBNaMOqTxvxJKozQuBLMPD477mRm0+u/WBHILCS4CNP7g4AZVi8EoSryqIuCOnx1WtJXs26Y+xsqpYCWfqotCJxbO8tZeLjQranmi8ZuYY2TOtNNgqjJmDoI+1SGXuhOz2mZ0fuT+ngrHIOicObns12wC5lfT7rLSYU74wf86tGMWJuX8WvoIa7tx5ddfiyvf8GYOfzLsH+Pkdd21FEgo1+Ugy8kzQAv/tw0aCHhmrOooA8L/LvVn46vc0Xr3Gv+ZUBCrWtm+jXrOnvWH+XOBy7vCd4O/T4R/K9w5ubznK7CIlL4atfnurBbNVYqp4umhvWGRo6F+eZAgBsqWHlxoJcnSoSVp8L5FBuObTW+z38xLgkEVZDfEo2u/BShNVhuuWRozbpT8a8aoAMHUtcLrIDeAwD9XvCXQ7DxjrQxbI4x80Mog3GWDfrtUooPt55m09/wm0GOZQb9OuCLlcjuIfroYbM7L13clbyzR/ubSAUu09z5DAV56RFzt2CzQZy3aaiVMbh+Rwk8qZH93QYaL5c3ZhpJ++W1jAcMsI9aUR1lz1w4xSDXb0m2pkyPOL4x8ETrHwZ68pgtMtfvscxcU0GquGnxa+7AIbVx+HGL6XT3Xuf9cf1vW94sHndwCvuoz9XPKi8aqLd0gG/pAUO0f2AyvfN2porlbudTtXrf1/eXPHOcNBWB58s/GqKmh08cIAcOVK4MLvjPfqNdtsiHR8bWeBMVpOfBwY/4z1fllbLy+GxPZQzU9lO2CtLPGqkPHTRVvl2TGG98fKOcDnd5qfK7w0ck4K7VPmsHRN3JQKrPvSfj8vdY6R88K+bd6PYQEjxk1WahQUhTPThm4brs9snZFpxA46We/cULEOMPr+2NyQ5XuyhlJDV2vtF8fJ67MoBSW3pR1C41G+p23sIpVbSgRJuqogEoJJq6Jm6bTQfLkVDAF9dsZ4P6BjdfvIica6pWHyh8C/iv3pyw1dzjLXvJHpeaH9serCLhqBr1J9/1zAdq4Dlr+u1+C5cel04oF2wBOabIfJRDwX27Hw4njpAwe2WbgnH9UUCBds+Qn45V335wwtzix+k9ISI+OduiCRrx85ljY7P5zYoP14WOJ1geOFjhOBdifZNJCuc3HNtzoO9lr8ILri7ID3+9OtFbb/v+z3T5il2SiNf8Gj5nu59GjYwmcXywcA2391M0KiLCgt8Tc75JEDkdvGPmp/TON+9vtF+ALnQDs/s0nHCfkeVhXGFesaWUFVLwWVaCxj1/4OXOYxrnybknzKtgSDbHwoAf7e6OIEUn+VG3oaWnmFBL505bPbgSWz7duYtMI2CzRBkwE27kIuJ3Zt+YcYUDX0kz+Ivq+M7LBlpa3dYswFWbnmOoBlifiNrFzi1O1OQm+2WuMHQPUW/qd+njtdszHGsgzbVxuvO1ZHM6LEkYiMp1Gh/I+tHuKmump+ndricSWEBTvLfoOe5n6y8gzXrpH32pywDF2GmCLYXbPOsAiGLH82x1r99l4FPrcKlS6T7ffrBGdear52VIFP7HMac7IqRsoFDvcHL/U3DrTVcZHb2jjEanc+zX7/qa8YQlKVRuaxFjX3PLyEINYnusy6jBllHqrpxh6jhS+3kuF544VDSg6ITUv07UqOAB9KSiNeArx2pnP/8vwhr2EO7DQndiJCkMCXrnx5L/D2Be7bq4tn3eI9K18v8JWWAsteCX9+dkxkdk6BU6C3V1S3Bje1df7xvH67cCP6VzFw0uP6NqlI25OMOIaht+r3y5Nl6+OdY/jUib/LWTENz5JvHrDfr16LpQ4L1CMHgYe7eBvD+m+B1yY79+03iVzIblrmTuu76w9z3C9gLTjHwy3HSQiOWAQ51KMrKDK7V/0rmEDi2CuiGp6/KAkY8qsaiQ2EpdLOcqGrdQp4VyK4UQIC9ov6y5bq90fcu9L1UnIkMtNyCOVz0ipGCEOIt1ICRiF0ZOmEHCeFgIMSsqipISQxZr6WPNXDSyDi+3oVpE2Jn8po2W+VwO+9q8yfXT9rZYWRNH/8X0/DiycW/lqnT1qY4pDAl0r8tdZcxDRWTA9TbrMvCAsYdfVU1PS56+YBD1nEsHlxCdVRq4MyJuY+TkDUlWo2OLyt50VAr4uBq1eFHw55VdIrGUBmthHHkF9Vv1+OF8utDMdAdvHAqNnOsDyM/re38XjJEibHeu3dZsSLCdRrab6mlo9MNNfei/8AVrwJHN7j/dhYSNT19/M7wGN9geUObkCAt4doaQnw64fRj0uHlRBcUM141S0I9R2ZPw67HTjv83AKdWHdL8ucAMLdU1W+1OlsWCY7Tow8RiDPbzJeF3purbQsAEyyyKKaVaD/v5UeNZf9MVn4SqTPDv8EXdIuIn58OM0olu7Gu8KuzW6PFhirsiwsYChwx3uM/df2JV2nZZWd1xGmvLqkzjFGMruOp0QqycuaNUrZCPU5bZWEznSMdK3t3Rz7mB7qBDyRoGymCYQEvlTioc7+XoTqQ9aJlXOM1+JvzNsfc/CTl4nFpbNaC6Bm2/BnJ191lYsXAddtNCxUV68yrF79rwGG3+E+k2e6U3rUhdZU7GeGEOlVy64mnJCZ9qf5831SUdt/tzbvK1GuJZ0yQiaah7gQhhO1AAhZZxJUKHfLz8br9t/s21liE+v7ko1QEtWpLM7VaZLxalVAPKIf5frufQlQV7KK+e2aHA1DbjEsjrrFToHN/WOHV4HvsEMNQ7nfLIsEV4EM6/nhVymjrqyhLz0a/ux033nN3JoI/vwhfTMIfjcTmP+wYa1x+o6xzpmyEsEqIRkLGG6dfsTfyfdHsluOvSaUCwSMWNoTH/U+v53zibf2XlGvk+WK8sgqY7V8/cUjN8SO1UbZkDSq90cCX6rx1xr/+pJvNPWms1sA7rKq8+YCdZHuBdVFpHH/4BuXE1hWbtgdsbCGYfWSC6MSRmC80wMh5FYS5TnsFp45hebFo2khqFw7aiZIJzfIaBYgYqyJzgYY70xlJUcMd1XxmzgJA8Vfe+s/Hr+XbhGWmRu27KkCn9WC1LXgU4aL9kDA+9w08h7DUmmFV4GvcV937Riz7ttun8zHUi3A0iPh61L9H867x/xZd09/dD3w/ZPO54wHqz4xEkJ5zTadamxZDmxeZt+Gx5i0pV7X8HurzJbynF+5QfTnAszzS+fTY+srXuRWBAbdCJz1fuLOWb97fPtXnxUfXWf+bPkstHDp9JMHOwD3t4pP32UACXzlGTuBb87l1sfFolWNVetnWjwkgSY+3Th6UC84NZGzWsb5d7eqwaUiu358/R9gmYUWWHDEpcXC6VzxJLTQjbNF8YdngWdGAmuFgODwP3VbVkIQjwew7rqUBQKncixuXINkvCYpKGt6nG+2VKp4FfhaDHfXzlbgs7HwWVF6FPjkpuAHReD7UYm/1ikW5j8cGRcUTzYvD1tDd28wXr3Wn42F0lJgw+LEnQ8AVrzl3IaXWsfyu8Hpem09xhyOcOb/oj+Xer5oSzslgn5XG0nS0gWnZ4XVs1CeGuL5fPY70WAZQgJfMlBaCjw3NtKXOd6YBD4HbfYZUhr1WOrIxBKbxLl5XSq0h8nggpUuNB+m/x9Nei38nkW88Rf1WtxmkXpdtvBpM3oqvHNR1ENKmIVP3JPxPt+moHZ+w/fGq9PiyquLWrwF5IqiLpY0LqfvcM5cYOhtRkyrHTXbAf2vta+Tl4p4Fbx+meOyoY3AF8jwnoBI9gJxdBss45pdB/8GHu0DvDXF+Cx+40S6dH77EPDkoMhQCysO73d2f/cD4S4eNcyIKbdSMKsKHCu3Yteno+WwI3lVgUyXClm3OD0rLJ+FcXbpTEPoCk8GDu8xNO2vxOBGUHLUXBTZDfLD0smiINdDYhnA4X3AxiXA+vneztnOpu6VDlOAsfoQJUHPd7qerX/wyUJgyKUzyt/faTGkTt6f36FvZ1fcW4fbBZGOhFn4gr9NvBey6iLK8V9p8T+zKhAfi+u2G0RGXds4ZGXM1VsAfS517psxYOA0oJKPRaOTAa8L2mNsnkcN+wBT5hmZf/Mq+2vh0yWL2b4a2Ls1cntZL/SE18DvC4xX8TskMunHlhXGq7AuOvHmecCjxxrC6ic3A5/fZd/+yEHgicHAhkXexrVnk7f2KowZsfbXWdRkU3/jWBNdiedZJ4dSDuWZq34Frl0f/txihLvj2v/Dep/TPezmWVjW80CKQAKfX8ye6M7NQYfQgMZSu2re3cBTQyNdO3bYxPzZuXSqyAveHauBt84HHu8PvO5Qg0nGTltnhVxXj3Nzyl6y7PmPm5gbOWlLVFgIDyImU10gWqWHX/eVsRh59NjYzuvq0CgfKOu/9XgeDxa+H54DXjvL85AARP6PP7s9un6sroFYF3uCsY9IHzTWPFl5ULWJ+dh0TZwRLV4tbXYurQ16AnU6AX0uC/ZtcR2wQIz1/4L/w4e7RCZtAhKniLEiVC9QeJuUgcAnEFZGJ8ScVHLYKH0zb4Z9+60rgD8XeXeTjfX5zAKGNd7KvVL93wckga/VccCZbi3UEtdvBsY85P24dOVaJZuq+v+Y+IK7fqo0st7ndA8f3K1/jprWrz4KfIkuwZRASODzi98+jH7xJR5qXh4Sxd+YL0yh5dujaMPmP2xz2igFvk9vCVv2PC3soliAmRIxcGDVR+GPoQcKCX6JJUZXWp2mfuKLwOlBhUnXs837rBQhm5YYsaZu3ZOcBICSI2FNvUq0GsRnRkZu+/RWI7GEDi8xfO9eEoOSyeP/zjIBSpzvPdmzgOsEPul3SjeLnN94TtoywEjjrjteTeLjp0unyoGdxqu2BuxR4OVJwOJZ1sf/+ALwSoKsNmIu2rkuMW6Tn94G/PSqt2O8CqPi/+dZiI11bnA4Xo0xlS18J7/oPumQTFZe8mfo9JvBN1vfv041ee2sqkOmh9/bxcG5Udq8c3HkNpN3h6aPaBV+Ze0mHkdI4EsGvCZqWPUJMGuUkSIZCNa8ek/f1m6hKt8QTufmJUB1jYbVC5zDs9An19biHPinTbbAizy6tBJ6XBezjfKBvu7LyG2ZueEH7aAbzPuyC6372vqL+/M6XeOf3gI8Pez/2zvreLmK8/9/5lrcDUKMKEQgQBIkwd1THAIU12AtUIWmFNpfW6AUvhQKpdSRYi3uRSsEJ0Bwt2AhISQh987vjzmzO2d25tju3rXP+/XKa3fPmTM7e3OeM/PIPA/w/tOF50rpSXjoPL8hprP28NWKkcTl7QGM4dOLlxhf+K2LERur9t+4NH9s+hHAt14AIIADbwi394Z0NqX7Xhc/H+U/17FKlQu6+SR/m38cD7xwM7DsU7dsl5K3AkPo2/9VkQe3f6e83/fQuRku0gbmhLLzcpCWP+0zacEN8W2icN1Ta++Wf2+XYmiqo9q5nUlTizvyqkvKJFc2w6bn3+cyqjtIcl+5lDDzOv3+C8MBkdXLrg1MdQgVvmogrSXixUC504vd0A1qLeQiFb40Hr524FjDre4Ls4tCynS/9YeL7A6A3kML2+mfXI01mWqJ4Rup1zivjVZ+4tr5UsR3HwCsMc06aC7m0ygjEfeTfa/F3eNanpZ8qDwGVx2QP5dVAdvqjHTtk9YfK5b3nihNP50ZNuny8EVfULah1B12rS1X2vcdfq6ev/M+V+VTTCLLMpSwTqdNGrn80+7pasYmISevwb1mKiQA8N9LUXVosbCfMZ+/DVw0Tb2a3B+Ee69YUtz3DhiXrr1rHtj7j/72xRoWSJgji0wi2ByEfja1AGO29CvkifboOeZDO6RTyrC3O+uc3dnJEzuRTpMQIcQ4IcRyIcRfjGMHCCHeFEJ8KYS4SQjRv7PGU1KKXfSkXdw9HmSPywlKxAI5qm9TIOKErqM9/EBdmeXhn9LDZ2fTi7uUWbayc+an+f2SsRkbPTWybHx1xMZsCXTrFz6W9eEcZbEvCAWNGW8udKldeQxMr3lWD5++bsiUZO1zf9syevhWrcxn50yClMDyz93n7DT5pSZ0j6VV+Ehihs8AJu2R/2w+60dvCYycFb2gNv8/9vlT+FzaELllnyRvm0Yudd24UnrPK7FXr2g8W0ie/Avwyctqb7CL9gxGXpPmmOy4dii/a11Dpa4MSIT+1ofeofbmDRxbXLetVl3UojK0uxS+9sI25nMo65ydNhlcDdGZ0nMxgNwqQwgxCcBvARwEYAiAZQB+04njKR1pFb6nrwZ+a7i4k9yYru9weVqumWNfGNFnhj18a+8a3S6KrIrxiU/pDrJ/N4mmqTk/mUZl1AJUohQgfmHWHFHLaLYl6sUkLPKxYml8m5tPBh46X73XC9OQPAaylWWRKGU+tX1Sp2WSkM5PX4tOxpT0O2x8m9X/38js31U0pswLz3tSEnye9Tl/z++v9V5rLCWEtW+vnF7gLIu6UmaQ1XJaCwnEli5SmbWlR+FritmrV+wzOupv9L13gJ3Pt9p7lqd7/8Hvfdr9YuA4zz5skscMt7Tlc+TGxa3zNLp8g5bRYkJunXv0rPvUnjOzGk3LnWG6gnSKwieE2A/A5wDuNQ7PAXCzlPJBKeVSAGcA2EMI0aszxlRS0lr5bjxaJZzQPHxB/v3id4BfjFFpqEPfEaHwRY7Ncd3Vc9Q+ojTj1uGS47ZLfk3BWDqyTf6xtY1qYLKtJabuH31+nUAh7O1IktHV8OqZXrwDjDp+He1Az8Hh677+yv99yxdHj8fHii/Cn10TzuNXqr17r97nTgSSC9lKOXk88mvgx32BD4PEDUnv+yQevgvXAy6KKLBt8tjvgCt3sr7D07fv+IqMf/9SYP7dNjzGPKFeovZ3JlH4SR7fAru5Nb52oXlt+wrguP8AuwdGHdubX0oyKXxFeqpMkkY7VAOXzlSZtX05A7RC5lX4iv27RczTbT0LFUKfgjjpG8AaG7jPrXcgMHitbMNrJA6/G9gkKFFTrsLirVa9vmLqN7vGWKDwrQo/D5I8G166E/i/6WElr8egbGOsAcqu8AkhegM4C4Cd03cSgFw8lpTyVQArAYwv95hKTpTi9MmrwKKX3Oe0RX3Ri/ljz10PLPs4H7YZ9R36hr7t1KjBFR568RaVKTCJwnfIbcA2P87XWylqY7QE2oziqAf/I+F1+sEfkymwJsNrapDJQeiXXfgWAL7zBnD8Y2qxZ/5fr2aENLoexLqelebQO4xzEcpgFLaiaBsrzEXabacZHj5D8dGLnDQLy0tmAnefGT6W1EOox5QmNXRHB3DDUcAd3ys8d+u3gTeN+oNvPOzfi9PZKe4HJAgZMlOAb356/r3sUPfY0VYCILMu1LKPixtfPbLnFcAxvnqURRjOTIVv5TJV83C9INqkS0/gR56Q4GLJ4nkvpQXfnnOqOcPf0g+DN76s4DHzbFIP3xkeuYvy8DnP0ZBbNoTIZ0Bv/7o8Hmo7hFevHfe/Bpidcm+rS2btOXLFknz0keu8i5tPBj5+KZxt/ot3042thugMD99PAFwhpbR2AqMnANt0vBhAgYdPCHGUEGK+EGL+okV2Io8qwHxw6j1Ffz8EuOP7yhJ/8XTnZbnJwYwZ1p60jnbg1fuBX45Vlmqnwteu6pM8d33E2KQqmPqLMSpLmW/cPrr1A2adnA/3KzYOez2jmO/wDZNdl1PoPBNR7sFVv7HXnc6ht4e9coDK2rX6VGDo+sD2P1PhMzZChBd7GjOhjk71vOuv88fsWksjN84XwH13PvDPBAWzbd58RCUg+Mdc9c+2El5/eP79J68AzwcGCFMu9CInzcLyw+cKj+k+338m+tose/hkO/DMNfmsvRq7PuGSD4E/7Az8arK7n84uXutL5rHNPPXafQAwcXejvbEokR3qHhswJnztAdfk3x/1r+LHWG9M2QtYzfP/X8yiz7zWZaApV8ijaaT43+XJrinaU2V+f3v051R9dSiPQzHewiSLXF9IZ6nwJU/z3QMjNi7POEg0m5+ukorN+lZ5+rc9eloBHLxWPkooKebarqMDWHhHfj7XxqalHwC9hhjtEhgwdeTC7d/NH7vju+62dUBZFT4hxFQA2wD4leP0UgB2kY/eAArMz1LKy6SU06SU0wYNqkJ3q/ngfDZYJC+4EfiPY0Fsom9I03qhF0FvPgzc8yPgy0XKAuHz8P3LUTR1yQfhzw/+Ulm7dcpo17h92Bvui6lRI6WlMCZdBMRYHnutnu+flIaRmwDjt1NlMPYJEnN87y3g6AfUxL3xcUCPFNn0zPtmh+CeHTkzf2yd/Qqv2eFn+fdPRGRn83HXD4ELJqvEIk/+uXCh5zOUuBZtxXq/tKz9O+EzIc3C0SfHZh2wjg5gZRDi6AvhKZV3YqeEqeJ9z5JZpyiP0Omv+dskEfWh6yUbRyNywLXARqkcEQAAIABJREFUEfeGj2kL/G4RtVt9mB6+kZ24gDflMjLSxaCUhkFXWJnNwjsKj7l4/Ergb/uoPf6Zx5NEhn0KX3D84V8B9/80+xi8yr3j+Bbfz88vifshJaG5Fdjs1HBylf3+luzaMVvHZ9+1Ff/eqxvnPNee+or7+KrlwCWz1FaJp/4KXLUv8OhF6px2Inz0YjjD7282BB67wt3f438ALt4or4T6yprVGeX28G0BYBSAt4QQHwA4FcCeQognACwAsK5uKIQYDaALAE/8YxVjPjhdRaV96EWduadAC8IHzwLtRlIWn8LnEpzzJpiDywteQfhJAoXP3teRJqTTjoUu2DOQ8PaLe/DveYVaZA4usk4gKWS1KcDE3eLbxWE+/LsETnz9/9/a3R3fH1f0NS1JF3pxNX+yoPt8KWbxp40/aazvScaW5Le7+jHrGiVl1Kz4NkDhguGgm4DD7gzOxcg8w7eLY/z2wDCrNIqWwSzGDS3LXfuEw7fLzZcZIn5KqvDZHj7H3+6qfYGX74nva/E76vWLd7KPJ0m4apJ9hw/8PPsYALcy4NoCsNExQE+PEZ8KX+eT1Eh20A3Ajz6NbmOvFff9i0rM02+U/xo7s6fJh8+qrRI6NFk7MHRU3cPnh+Xvq8+AWz3ey5tPAha90HC1G8ut8F0GYAyAqcG/SwHcCmB7AH8FsKsQYlMhRA+ofX43SCmLLPZSAczFR5rJzuXhM62UHy1Qr0/9za/wxabQl/k2HavCD/ksCl+akE6fBdHsu0sfYOw2cYMILvdMUD0GADOO5ARRzbgME7kkKZ3kmU0ayuVatCVWFj2/RcvC2K2TfXfakE6be38S/ty+Iv7v7HoeZKlb1tYjWTs7xfqYLYERGyW7NurZ9c1b8iVGSHKailD4cvtfS6iIj0xoODBJEmKZReGTUhVvb7f+NvZ96FO4vkxgCH44yFJZzOMwTSI3e+xvpyjVEodrXbLTLx3trHlh258UtiGdR5ZyN/tf4z5urxV7DgamG9soTn+98JqWCIVPY3sO9V7wr79Kb5hdbBlXSikDVUhZFT4p5TIp5Qf6H1QY53Ip5SIp5QIAx0Apfh9B7d07rpzjKR/GE7rPsBSXBQ/cuAlo6Yceha8jgcLXHk5GYSbHyBLSuTJF5rt1rWyPrqxg33sLODBiDyKQF/BeqyX/blJduO7T3L3VSQpf0mQNLrlImtnPJ1OfvaE2lI/eMvp6PcZiQzofssIqn7shPumCa7GYZKFqssuvgNaECl8xSRminl1rbqpCkkk69HM2S1KTcuyjzmLAs8fuKnweN8bli4GfDAZeMbxyr9wDXHMg8Ccr2iHpHr40C+livNeJFD5P5uGX7yxs+3XGUgx22HjXPsAgI/Joqx+qV3uBT6NtZdD3Z1yYposJO7iPx93z3R1lt337P02euTb8uU+QLby5Lfr+/+gF4MPnw8fsDNRXxDkfaptOrWIppZwnpTzQ+Pw3KeUIKWUPKeXuUsoYH3GVYj6gu6QIQ9M3Z1wWQtGU3cO34MZ8zS7ZEf6uJFm3llohM+89GX+NZu2YUMCkk2DPQSpByJxgf+Tcx/1x/6Q6cU0krjIISUkyMdgkVdpci7Yk3sGVy5QXwMfdZxgTkmdh01FkSOc1BwHX2QWMAdxyMvC7mMnM/t1vPpp8DJqRM6PDckyKWdwxpLP05Dx8WRS+IOlSKRW+Nx5Kf40p4x0dwBfvFbZZFTPGj15Q/fzLCGvUhs43Hwl7MZPs4QOqTOHThuYE/8/3zEv+3S3dgHm+Ei6WrG92mmpbEMoftNuoRm3/NUvwd8/i4fN2meH5nuQaHfkGAP3H5Mv1TD88+v7/zUbAJTH7i7WTYruz88fsbKM1TKcqfHWLGSqVJhwmV5AyweLVq/AlsMh8EGQG7GgPP+STbMw2rXJAOutv3MMjzQNhvQPztdsGji3NvjLSebjuBX3vZgnpjCrq7iNpSKdL1pJce/eZwN+/6T//2RvRoZod7cUnbXnhn/5kNKs8hiVdrsIe25U7Jh+DRjQlnyCzKO1z56tXKnylR2dEHRMTduxCF1kupbd+rV3y76cfmewac36S7cC/HQlo4pTS32+fv15jzrOmHJVK4bvfSFC1vIgSFmnm53cfz7/3PYPNklGxRP3fJ7wvdNINu04rKS85D19GI9xmp/vPRe2Ti93OE0NTi+ElFsnu/3mOvaQanQdimmE0bV+ZvTRUlUGFrxSYD33fDXfXGcBvLOuCXtTFJRvpaHc/kFd+mS670G2nhSckO37ZZo/fFSbOWDPFnh57jw5pXFx7+LRikMmjkMHqltT74FK27j0r+pr3n1Yp1V0MCgoBS5n3DrgmVjNc2qcYvu7wehSr/Ez6hnq9YArw8t3q/aeO/RVJEE3JFbks4UPFeIVJNMNnKK+Lr2xDFNpTE1W0eNz26fpsNep42kWcfYTSt3uUr6jnwNPGfiTzelNeTeNPQUhnxhp/DxjZtl1hqElJY3AOlTnxKGSfvpa8vySGu5knRZ+fvAew3TnA9COSfy8pnmIVvq1+4D6+71+Auf/zX3fg9cDmEWUQzEzeLppajC1Lq9z3/+93VPVoo+gdbMV66S71ahst7bq6NQpX5KXAXHz4HviPXgh8ZMUP65tz4W3x/bsWOIteSD5GAFi5JDy+OKvjOnsXHkuTlMZe0PUZkfxaUl+4JpIkoX8tnoVeFu9QUg+fK/Rz2cfAgpv81/x2M2DxW+5zpoUzKqRz5Zf59z6Fxq61B+TTU2fF/Fs+fAHw4m3AhVOz9SVE8kVDlhIvVPiql33+XFjqwWTOtUqh9Ib9AehrzBHm/ZEkmQMQ3t/jU+yuj1AmzBBGM0GLOVeaRl17genzzL8dsei1KSaEzKfwvftE4THzN33mMfCkUgASKHzbnhX9/9/SBdhkbj6TM+kccpWvSryffu1dgf6jo9u4MnRr4gwOTc35+fXZa90Ol7ceBR77nb+PnkPyc/6bDwf9WmP63C4jXptQ4SsFIQ9fCgtb0kXLwtvcbbNMDKHxZbDm9E2htNkK5WEJ6xGRxiBJWOZsh4IDZPMOJfXwveJZtGatTWUmp4kK6VxhePh8C0fXc+DRC7ONS2PK6TuPAVfv72/bb83kfZkMGAtM2Dl8zCyqrkM14+gZFNaddXKy9qTzmLgb0G9kcX3sYRRPN++lpB79e36Uf28aUEyiQiZNBcdnHDUNQvq9zkjoU7j+91v/d9rE7X2Pwvf9Tzr2vK80ErhdtL77uvEpwrqjlAWWyK1utvi+ei1G0R4yGRi9RfrrotaxcWUTmlvzytn7TwOv3udv65ubuvZRawNzb65t6ChVjdoKQ4WvFJgTS5Y9fHFKlM/Dl8UaY45vZZkrYNhCo7Mpbf6dwrbHPgoMnFB4nNQvUZa9XBvPAz9T0paECt/rD7iPf7wwrJQlYco++d/w+oPAxxFlRk159E0wSRItpcX0pMR5XWP35XrOn/A4sL9V1Ldbv/z7geOi+9W0dVceAnOPBakfTLk2548s9bLSyqpNu0fhM5O+6PdmWFmxJFWalzly3Pm+3/X3u8MxD5u8/0z+eaPDvqOg17122eiYIIlOEXXpjn0EOPgf6a+LCvUes0X0tU0t4WfGVxF5H2WHO1Kga18lx3H76+sAKnyl4HZjw2rH18B/Lkl2nb6J+o4Eeq8R3fb8tbKNzSZLodqs9PVMXFt+vzCsY8ikdPsDSf0QldnWZ/3Lsj+0FBkEP3sj/TX6N7SvBJ74k3rvCpUKefiMxdOb/wYWLQyuK8MjO423NKnCN+d64ARHGJnJ6C3Vnp4GK35LAGxygnq1n/mm8cG811x7A9c7KP9e78Ex+drj4QOA1x6IDxcLPS+E+/iHQcbAJCUtbAPtU1epBBKmpw1IHnp+uaPEi+/727q7j0fx202Bx69U73dwFGIv8PbTjUcyMNha247eIv9+6oGIpKklvBaIi7Dr6kjY0rWPSsTkMqBosu7NrTKo8JUCc+J46U7gjohNqCa5wusrgbaepR+XC7uOEAAMmx7+fPg9wFH/Kv67uvaOjte36TUk/37jucV/P6l+5lwHHPOw/7zPk5fFw1cSJHDT8cCF6wGfv6UWbG9EjB/wWE1TJG25cgfg4hnqvWvCimLNzePbmIvs5THyGrfvTi/Sx20DDBhTeH52kJBiwFhVbmXbs4AzP44fI6kzPGngQx4+fS9tB7z/VGEXm37LuC5ou8a0/LGozHp/2k3JcBRfvJt/b8qjGdJ5/9nhcX9tKW8mtsFJJ2pZ8n50Ox8u45PXE5Fw+0Z/h8wCnizLdthbCbJ0EnLwP/J7fUdsCBz7b39bez6Ki4BxhazqEO+oDNv08JEcZihDnAfNtNybCl+XDApfqeKKTQt/Szdg+HRgaMxkmIaJs5P1N9PYl7P9OaX7flK9jNs2OoTJ9PBtPBc45hGVGCLLHj4AWPcAYPKe2a7VPPUXZeR5+mr1+fE/RLdP6sEyF6i+CebDBe7jPnzhqSaplOeYhWOcB1A/B0qdHIDUJrYcm/fi0GBf2YyjChdytmdY99N7aP6YttgXs1VgXh91r161X/6Yy4umx7IiYptEQYipR5aiFL5FC9WYXr4HmOJIqlZsHUBfSJ9oytcoyx+0PlOmSRkIzU8i4hzijSUuhU8fe8tSLI+4L7/toBSh2lUAFb5SsOTD/Pu4G85U0swCqOaNGFXTJNRXiWLmTStJVApdm279k7Xb54/JPIbFxI+T6uewu4DjU9xfQFjhW3NzlTJ+2LRsGR4BpXCstXN8O8CtqJmKyv2BUSJuMnApVC5jjZnB0yXbSxdF7wF0sbsj6Y2udaVJFdJZpMKn/9+436exEQk8fL1WU1b+cdsWthuxiVU2ITj/wj/zx/Te+jQGDdOrp7G9dq6QS31fRyl8Sz+wDngUpMf/AHzwrPvcpbPU61/3dCe98oWeJc6c61P4BLDVGcn6cEEDD8mKOdfbsmw/Fz5/M7ovVySduZccAHY6V70O2yBvdKLCR3KEijTGxPqaIVO+kM5RMbVHfLj2MSTBXHylycLpCtkqmox1YEj1M2JDYFBKa7tpBDDfZw3pbGkLL5TmXAds+xN3221/XPhdrsXe1zFhJK7Flkvh0V69li7uCSbLPgKXZ339g9P3o5FSbXL3Eafw5bIZ1keIDCkSUza2/6l/D986+4avkx35jK12P5oVX6jXHgPzxybODrf5MkE4sR0a6ipGnkThW/ZJ/HdptGJn0tERb1DWz40VS4DLt8o/m2y59Bl5fIm0RJNKunbaq9HfT0ipiZz3U64Xx25deMzOIeBSMKnwkRymxyDugfy8UctLL3raV4YLy2ZNYpD1pozarBrFbv8H7GAUjF1tCrBpTIHLOE56GjgkRTF5Ut+EFD7D25c1eUlzl3A/QyYDIzZyt113f+VhMD3Z9/64sF3H10CvoYXHc3gmpQ5L6dPy29LVI8sJJzezYPWQiao+mslGxyfrx4VsB8ZHZFVriSm1kUtgkzAxBalTHPfy9COtuc9os8YG4bZNLeFEJK7ngVa+zLnVvj+vMsIUfV4o28N326mFbXSEzhfvApP3Ava8orDNH3e1EsWkXKzeflp8m0+DenrnrQ28+zjwlz2Cr7L+Pm093Nf7DGn6elN51s8oJl0ixTL3ceDwu93nXPt6s9J3JDDrlPCxVqvW74Ib8+/7jVKvaffOVylU+ErBqJnA3n9Q7+MUvg+ey7/PefhWhb0OKzOmk+61WrbrPl6Y7bouvfIW0659VPKNrc8EDr1DKW5Z6DcSGOWwbpLG5HOjmLmp/G2RMDGSTUtbodfQt8jRizizvZ1gAVAhkqkKFAfYYZ36edDWI3mmPhe2QjbRStQUp5RF0dGOgoXqkClG39bkOWCc2m+lqTOLKSkW415qaUtelqFHgrBkHd5lLuhs46ZZcPylO91DjPPgA0ou3ntSvX/uOmDKXu508zrB0zvz/cXOfRR4Dx0K6i3BPnhd4uWL94ITlszai1yNb0HtCqE3IxJiYUgniWDgWGD4DPe5qGge/YyYmbAua3MbsM28cDJB+55/z1i7bn2meh1ZH2tSKnylYtI31I0ZF9JpLnQe+50K8WxfGV5UxqWLdrHXlcAGhyRr22d4+POUfdJ/H6AEJbcnxzg+cuO8ZYSQYjAt++aiI24f3uYehdD28DW1uPeONnfJHze/95NXjDHsol6HrhdheZTAxh6Pmh3WqD+3di/OAxYbVmmVuljDU3TZxTY/KjzW3fCA2iFhJ8xXmTg1ug7SgIR190h9osss9LG2IXgVPuOeHja90OPnuud1MiXTCGHKLxAOrTYNrXv/Mf8+yjix+rpBm3Zg+Rfhc9+4FNjtovAxvT74nRFaljS8ecxW4c/m2OdcHz6ny13oEDb77+NT+HyY13cJvB3d+ytFfDtPSHy4g3TfR4jG90wwSWoANo0TJz8HHHIbMPWAcJtTjX3y2hNeJwZKKnylpKk13sNnFn584Z/AHd8LFD5jETbGEWccx+Q9kic9sR/+uhxC0mQxZj+CSRhIGXFl4kvC0Knu4y1dVOKXXP8t7pAkM2uuL2RJexDav0bkgmbUTLfH25YZPamIJlWkfV4f9c9FVN1O0QSstk44dOXQ29V+xQOvLyyu3r1/sr/taa8CE3cvnHR1CZUkfXTtrcaw/1XxbUn9suExKipmHcvY6NvDZ3LYXYXHoupymsqNnQ3bF8Zpesmj9s5qOZTthe2693dktnSQdDFpZyo1nx92nT1daumx36lXXT5C0+qpy+f7e5j/FwcGymVzmyqpss5+7mtMdvpFfBtCXJhr5oJngihsE4W5Ru47XM3NQyaF25jzoxBq/mcdPlJAcwKFz7bcr1iiFozmjRiVpj4K2/U9alN3OzuTkVY201oxRJMx0TJkg5SB5gzx+/1GRexFEeF4/OZWt6HETKLk60tn0utYFR/SmSRTp5Y/b4i1IWNH3Ov/LtEEHPOQCl3RjNxEZTscu43/mjhybYzf2to9n/UzaYTB2G3CXkHSeDS3qKgYfU/p+nlRKdg1LuUuythgKnwFWfoMmTJluLUbcMC16r1dzNlM8qAVpI52t4wXHHPMk0nLK9lh3qbCN3IT9bpeUKjaDq228Xn4fIZb8/mg/066bdyz46gHCr0ohCTF9MoVhBYH8pR0S4Ud3ZLomlZ6+IiDJCGdOlW0pq1nYUineYMntVzo7zc55BZgwk7x103ZWwlCEmukCT18JI6Tn4tWTuIIefgSPK6mHqg8WT4lzS4l0uTZw2eWSVlip1O3SKLwucZeENIZM6mY1vfI/boZwqcSKXxBv9ogtfl3gLnzVfrq/a9WWRYJSYOwDIZps+9uMy+4LkrhM7xZaUIZc/tNrTndDK3U855sz6d3n31J/rz9XHCVUvAViP/tZsrD//Cv1OfbvxM+n5tzg+8wvf6+bJu5876FbwIPn1Z4B45Xr/pvb7bR54AyZfMmDYMp2/Z6OO260yV/sd/fovbevv5g+murDCp8pSTKw6f3zdkK39N/UxNKcxuw9q75Eg96oevLpuXCNVkmWcgNGAucsQgYND6+LZBX8oRgXS0STd/hqm5eVsyFSZKQ5WEbAAPHhWvNTdw9/96uQdfU5O73QyO50orFhedN7j/Hf/9H1Z8qCOmMs/R7vBE2WTKZJblGP5NmnaIUvC2/r1K1A8CEHQtDRQmJxfIWmXPYwtuTXx+lKE7dX50//rHC+9yUT1tW9XPhE6sUgWmY0ePu6Mgft58xJnrvosmD5+bfm+N7PwgDv2deMDZrfFpR1MXmm1uBVcH6w/wtrrDwYjx8g8YDB/wd2PUC9bmpRSmb5n5FXf9zjQ3cxa4JyUKxCp8d+pyEphaVkOmPu6a/tsrIWMyKOGmKcP0OHAcsfhtY5AnXamoF9v1L/rO2Krb1SF6/xxXCmSpUKyFNzUB7e+DhC6511fsipFhM616ShYPOqLf6OkYfxmOua2Cdnnky8J/AEu/aoxcq3TAF+NBTCFnjVez08ZhafOdOcBRmjmgPACNnAm8+UtguS8bQKA/Jd95Q2YX1/qeWLkrBI6RY9K0qHR6+cZ7w49D1CRS+/qOBM4M5tGCuS2CQ+cdx+WN9RlhyGFwv21XdOyBallzhm18uyr8Xze5F7Kv3qb39rxrREgtvC7dpbssbnOMWwv3W9IzP5+Gzninjtwuf+9bzVvum6P4IycIX74Y/m/f5pD2ABTdEX5/GgaJZHmPwrSHo4SslUWEUelHpS8X82r/cx22PYBR2qmqgPAqfMEI4WrupTEf7X52uD0KSYC40elu17k58EjjuP+FjPQfn32trYGgPT6A0bvtj4IyP1HuX3JqZJe1MgjYjZ/oXNtprZ9av0nz8cv59nLIHFH7HobcBU+cUtnvqr/F97Xx++HPUM6BbP2BNz35gQooip/GpF3N/nk8pCV3eFH4FVH1Yb3tLGYvSR1zlGJpbwovMnIfPUORs5XPkrHxiE6dB2PTce+Rw5VK/wUvXwm3uoraUSBmtaPUckj6kMy25/1ZG/pASsllQi3J8YHA0768k2aYL9vAmIOke2xqACl8piSpAGheO5rupBk/MPh4g2toIAN0HpvcI5CbZ4LpRM4FufdOPjZCk9B1ReKz/aGDw2uFjk/csbGfu4XEpdy65jbvGpMdA/8JGL/CaWwuz2V25Q3S/Nq7vSJqZ12b64epV//YsXkFCikUrSi4PXZyhBYAzpDMqIZA9H0YpJHbBdUApSqZyJw0PX+47rN9y6K35jLmusG1TOfN5Kl9/EHj+Jvc5XWezuRVYeCvw477AO4+52wLAoAn+50apFDR7byYhxXDUA8AuFyiFb97i/NanJLJjkkXhqyPKqvAJIboIIa4QQrwphFgihHhSCLGjcX5rIcSLQohlQoj7hRAZ01NWCVGLL32uzWOl892sqW9Qa+EWlyrdV+wyCm1JSZMmn5CsHHkfcMR9/vNbG7XhTMUlFyYWoxS55Nb0CsZNJB3t8C5szAVUnPElFsd3xP22KI5+CDg5CFW1ZXlYhucCIWnRXqshkwvPJSnonQvpNMs5RMiZbdjoWKWycD50XmE0zZqbF17f1JrAw+eQST0+p8Jn9Pe1J6InyX5G02v3sqeIPKD2G3oVvlIpaNbeTEKKYehUYNqhefltCe518/5Ksh5tyZCls44o9x6+FgBvA9gcwFsAdgJwrRBiCoClAG4AcASAmwH8BMA1ADYq85jKR9TCUE8CPm+BbzGYNubYThwTt8jM8kDe72/ARy9k2wBLSFrsIss2m34LePkuR+pvR5iYC3OBNmFnZSU3N4fbcr3GNODd+fnPHe0RSVtKqPC5FmPOVPAJlUBzn6MdSla0ckpIAgavBXzzZmD4huHjUXUmQwQLQHOxFxWebJ9rXwE8dz1w71lAj8Hhcz0GqKy+X32aP9bcakXjuDx8DtnJJTdzKHxJCq/be5dcJN2aITv8z4hSe/jo4CPl4Iv31Ku5h9005gwcD4zfAXj0wnT97vrr4sdWxZTVwyel/FJKOU9K+YaUskNKeQuA1wFsAGAPAAuklH+XUi4HMA/AukKItco5prIS6eELLAt2LR2NT1lMYuWM6iduEsjygO/aGxixYXw7QjqLw+4A1j84fGyPy1XClbjaO6ZCqI0lptzNOsXqw1rFyHa/ZTxtyAkAHOQJ3XJ9h8uAlGXPQYHCx3xepJNYc7OwvB39oArhSoIraUuUgacgS2eHUvgAYPnnhe1tOWhuszx8ug6f6WmIqBPo2sP30QL/eNPw1qMJG0q/fJfKwzdwHNB/DLD9OaXpjxCTRS8WHtPPgmmHAXMfAybOTt/v+t8sblxVTqfu4RNCDAEwHsACAJMAPK3PSSm/BPBqcLw2SbKHb5VjIzhQ+ADe/TdqIkxrabfHYFo9DrlNbXY1EzYw5ILUK5NmA8c+jMR16QaMzac6Nz18QyYB338//9m2yK9a7pejpKFmJq7U7QCw4EZH/y6FL4NM24tU/XnM1un7IqQYVl8X6OmRAZvXH1Kv7z+dPxYZ0uk4p8MfXSWVbPlqagkrd3o+j/Xw6Zp+McaYrX4Yfb4UyA5/pFGp1gOt3YATnwBGO8JiCSmWfqMKjwkrbDpLlEqd72XvNIVPCNEK4K8A/iilfBFATwB2vtPFAAo2uQkhjhJCzBdCzF+0aJF9unqIDOkMznkXhta1681RoS6+ycss/mqyx2Xhz+b1o2YCB1ydT9gQNR5CGomjHwIOvxvoP0p97j86fL65BZh+pHpve9BefxBY9nFhnz0GA1t8L//ZlQTChe858owjE26p9tHak6P+PDBhbU5Cys0pzwNz54ePDQmSmpkZqqOiWnSSp15D/W26GnXrbMVIiLz8L7w9nxzFVORc398U4eHTdB/o3jdYaoZM8j9jeq1W/u8npFjWCmrimTV2hbVvlFEqBXSKwieEaALwZwArAcwNDi8F0Ntq2hvAEvt6KeVlUsppUsppgwYltPxVgnf+F3FSRoeW+SwLvpt201Pz783Ja4KV+W+TEyPGBCp8pHHo6ihArFl9HZXdb8dfAEfcqwrG22gZ7UgoMyc87q8HaGL35wsN/+SVwmO+ci5p8Xn4CKkW+qyhQgVNBgU7QLoZmTmjLPuzTlGRLlGep8PuMvoyZHbibNV3xypVsuF/hnH1y4/y711GGH0sar5tbs2edTcp+1+tyjj4opGGbwgcmqTgPSEVpOcgNU/PvjR/zM4MS4WvgLLP6kIIAeAKAEMA7CmlDCqKYwGAdY12PQCMCY7XJq6QEAAYOAHY6DhVJ8fHIM/WRd9Nax7f6/f+fgeOdR/f8ZfqlYVRSaOw8dz4Nm09gGHT3OdyiQgS7pGzF54+Jeo2w3iz07npQlF8e4LTYhujJu+lnlsbHl2a/gkpB7n71qxlFyE/Tc0q0iWqjVkz05xnh0xWMvzOY8A5Q/ze7ygPX5TCF6WIlYrhG6r9kr5njGwGDdc2AAAd+ElEQVQHRm5S3jEQUgqGTQsnDszNz1T4fHSGGfcSAGsD2FVK+ZVx/EYAk4UQewohugI4E8AzQbhnbbL2buHPO58HTDscmPs/YMCYvPXONSFscKi7T29WT+N4Fmu8joGmh4/UPXoxWGR8fhIrfai97TVzfH/7KmD+FfnPg9ZKN1GVSn7t5FA9BqnnVv8Exa8JqRSjtwBWWwfY/qf5Y0kMJlFtzHOmLMqOsKLYxQ5Qiug7Fx0QYSwaPqP8Hj49jnLX4SOks8kpfDqkM4Xh1LUnsA4pdx2+kQCOBjAVwAdCiKXBvzlSykUA9gRwDoDPAGwIYD9/bzVAP6OMYJfewPQjgF2MBCnaGhlVp8dmxtFAX0d5wlAyiAz/jTmLIz18pM6ZdrjK1rnegcX1oxdLSVPGF3gRHArfXT+wmjSls/KbCzSf0SgJtoePCz9SC3TtDRzzkNqXppMdJZkPIxU+Q8kzk6zJjvB1PhlxZum0FqPuC8vvldDj+MqRkRRIViKCkGrE9vCZidfiOPJ+4PioLVn1QbnLMrwppRRSyq5Syp7Gv78G5++RUq4lpewmpdxCSvlGOcdTdvRCbeRM4MSnHOdbwq8mvkmq73DgWEe65TQevl1/XbgYjEsiQ0i90GcNla2z9+rF9aPlbPQWwGERhY3t9rnPDoXvTUu2RVO6RZ+ZBKKYxaK9AOZzgdQa3YNQzGL3n5py9Olr+feyI9y3L7TbJedJogNEU7Kxj94ivo3/S9TLcjtfXgDlntQqtlElTUmz7v2BQRPi2yXdv1+lcGd+KdFhEgPGhrOGaXR9IGeClohwM1f4RVTdoc1OB7Y+M/95g0OAXS+wrk8ZnkZIo2NuCh+xUXz7Ai+CQ8ZtK6RoSheKMvs34e+bOBvY96/Jr9fsfD4wclb+c5ZafoRUEl2cPElq9Zkn+895a9RZCp/PG+ZM2pJg/29ShW/mScm/1zcOn/eD6wFSs1hZOtN4+JJS4/MiFb5SkgvF8oRJ6gdyVMhHZL/msRb/+a1+AGz6bX9/5lh8YyWEhNEyqhd6cx+PaS+iPwOFVkjRlG4fz7Dp+fdDJgH7/BFYe5fk12uGTgUOvRWYsLP6zIUfqTVWfKFeP3Zks7Xp59gmoUmq8KUJ6TQNrB+94L8uibHHp9gl8fDrZ5BPKabck1rFLstQDoWvxkOeqfCVEp1gxef21Q9zZ8hHhFXS9uCZfQHZQrno4SMkHTp8UsubnQF3jWnAaa8Cc65TWS4LcMi4XSoi7R4+87mx3kHJr/PRZIXFEFIrtAUlfLsUlPJNR5Qy9NVn4c8unElbArla9BLwG090gBAJ9x965vux28Rfi5jkMXFlnAipVgqStjSpeXjn84rr96Cb1Guv1YHWMiiRnQgVvlKiF2re2H49EaRU+JzfZTz0s2T2SrSJnBCSY+PjgXHbA+sf7D7fd7hK6T5uW2CvKwrP55K+DMsfe/GWwjYtEfU6o0j7DHH2YXkxCakVyl1OoKkZWPZJ/rMv4ZkzgieY+z952d+/EEDPIcDau8aMw6PwuZ45vrH5FMs+VkKqHoOBk5+N75eQSuNa0+51hZqzi0Engyo2y3cVQIWvlOhMd3GWv7Qhnc6+WtzvE18fjKX96+h2hBBFr9WAOdeqDd6akNzGTQjB+W59I5pU+JGctvQEIdVCuY2YGxxqee9itm6EjulnQ8QzQod07vuX6HGYYzALT7d2i77OHMf6CaMBuvUD+o5I1paQSmIXXtcUm/lWy9uS94rrpwpgZcJSkgvp9FjHI0M6O1nh08opFT5CstPcBVgVlBeN87Dp82vvBnz4nKdNpRU+ev5JjbLDTwHIIrNYeugxSHnwTWUulYdPQCl7EXvmk8q+qfDZHrk49Hf0HprwAu7xJzWCXZZBU2xtS19G2xqECl8pyYV0+jZzR3j4krqLv/2SUvDMxWWWGzqn8K1Mfy0hRNHcmlf47Fp2BQQy29YdOPFJ4ML1HE0yKHynvoKSLcz0YpIhnaTW6D8aOOCa8vS9+8XqNZGHzzOXNzVH171NKvuimP37KcPSWKeX1Ao6EdMa64ePp8l67SLNnvoqhyGdpaQ5RuErRUhnryGq5EOxSVvo4SOkeExjS5zhxcwi5pNZ8znQ3VHaxUXPQUDPwcnaxkEPH6kH5lyX7boRnn2Ag9ZSr6bcxkXy2IgmRBtmEipjoRq8KRezqQ1KVPhIjbDaFOD4x4CZp4SPFxvSqRm6fnybKocKXynJFTMvU9KWPkYsvfmgz5J+Vu8jGr1Z+msJIYXEefhMZSpO4fv2QuUF7GyS1AsjpNoZt23GCz0KTi6rtXG+w2Ms9SlVIqOHb8hkayxFbOdIm9iJHj5SSwwaX5jVvliFr2eQtGXNTYvrpwpgSGcpyXn4YiaNLHv4vvUi0NbD3b6te/Ixarr2AU56GuiVNJafEFKAmbUvbmIxS6F4Fb7g2dBrNX8/+/wp+fjSMnwG8NRfgQFj49sSUm949+U5khn5omN8XjfRBLz/lP+7fWuAibPze36/cZkV3ZPSZp86G3iR4XCEVJpiQzL7jgBOeALoN6okw6kkVPhKSc7D5wmHeme+el3yfuG5OIWv9+rhz63dgI2OA6bsnW6MJnVwAxNSNcRlyUvi4YsKpWxqUbUAi60zFsX63wRGbQoMGFO+7yCkWklTTF3X5UzSFvB7BKOua24LBwQNnxFW2koVrpZmTITUEjqZ4lDHnvmk1Ml8SIWvlMTVsIp84Ke0vAkB7PCzdNcQQkrL4InAR8+r960xnvbc86HDbzmP2lPbdwTw6Wvq+nIhRN1MboREGjWHTQfeecw6GBOdY57XsiqawyHQXtmOSZDm8r7JjrDSJUTYg5h2D19aEhVzJ6TK+fZCoK1npUdRcWi+KSXFJDygJY2Q2sOU25YuCdtKv2Xep/Cd9AzQP1DEuL+OkHhOfQU45hH/+YP/Cex8fvhYXKkFc27XkTqH3203Tja+dQ9wf4dJR7vVnyi+JFMatvlxefsnpDPotRrQhQoftYxSUkzR4rSx9YSQymMmh4gz2gij5IFvoeaLAug3Mn+NL5SMEJKn56DoRV5bd0c9uhRJW97+r3rVCdAAYObJyfe9feOS8GdzDbDLBfnxmMdFk6XwlcnD9923gO+8kQ+HI4TUPFT4Skku7XoGCzwVPkJqj63OMD7EyHBu8STdoVjTj1BhZj5YI4+QEmPJbIGHLzgfZcw19+5u++N0c/k289zHJ+5uDMEK6QwlbbGeI6O3TP7dUXTtA3TrV5q+CCFVAc03pSRuDx8hpL4wF1zTDo1uO+No4LM3gE1OLFyozTwJ2Paswmu++xbyi06WTCCkpBQoZ5bCJ5qUvLk8aQMnAB8vzFYWSTPrFGDDY4CVX4aPhyIA7JDOiD18wzcEXrs/+3gIIXULPXylhF46QhoXs2yKiy49gd0uArr2LnxWrHeQ+5qufVR7wIggYG0sQsqCLVvCMraY3rZVy9Vrc6tS2r55c7bvbO0G9BgYPhYqrm4nbWlytwOAyXtkGwOgClcTQuoWKnylpD3YW5MkG9B+VwG7Xlje8RBCaoMkSZvMpC+EkNLhk60p+wTnA09ac1v+nFb4IIAdfw6suVnpxhNS+Kw9fM1d3O0AYNAE4Ij73H1udJz7+GF3qgiDguQzhJB6giGdpWT05ipsa9Nvxbddayf1evOJ5R0TIaT6SZSllx4+QsqCaFb782zZ2u1CYLuz8/tvTYVv6YfBtWWwm/s8fBBAizEGV6jpsA3cfXbtE/58wN+BJe8BIzZS/wghdQ0VvlLS3Ars9ItKj4IQUmskWTSO2QpYcIOy4hNCSkBgRGlqDjLkWgpfcyvQY0D+s6ls5booh8JnKXm5t3q8rWq8abJ02glnxm+XeXiEkNqj4iGdQoj+QogbhRBfCiHeFEIcEH9VjdLUWukREEKqkgReu/UOBE57jXttCCk1uYRIMXI4YSfHtWXeu2+HdAJ55S1N4fUs5aIIIXVDxRU+ABcDWAlgCIA5AC4RQkyq7JDKhJm+mRBCNL6C6yZChL0NhJDiGDBGva61i3qNU/imHwGc/nr4WDk8fIAKJT3qARRk6QSQMxClKbzOUHBCGpqKKnxCiB4A9gRwhpRyqZTyYQD/BOBJWVfjtHSJb0MIaTxYTJ2QzmfAGKXAzTgqOBCjFAkBdO9vHSvTMmqTE4ChUy0Pn97HG3jrignpJIQ0FJX28I0H0C6lfMk49jSA+vTwFVOvhxBSvwxaq9IjIKQx6d4/v2cukxesE0M6Na1BCRhfSOeeVwBzrg8fo8JHSENTaYWvJ4DF1rHFAHqZB4QQRwkh5gsh5i9atKjTBldyptbv9kRCSEZmHM0anoRUkpzilEHh60zZ1d7EI+8FtjvHSu5iMGUvYNw24WNU+AhpaCqt8C0F0Ns61hvAEvOAlPIyKeU0KeW0QYMGddrgSs7m3630CAgh1YZsr/QICGlsdGhkUg9fi7EfP6nCt93Z6cak6TAUNf1dg9cGNpmbrh8qfIQ0NJVW+F4C0CKEGGccWxfAggqNp7z4rHGEkMalgwofIRVFe/iSKkVp9s5p1t41/TUAsIZZV68IbyKTthDS0FRUA5FSfgngBgBnCSF6CCFmAtgdwJ8rOS5CCOk06OEjpLI0pQzpzKI8NWdM2mYWUi8mfHTMltmvJYTUPNVQeP04AL8H8BGATwAcK6WsTw+fiyPvAxYtrPQoCCGdzS6/Am45JRyyRQjpfJLW4dN8/WX67+hegpIqWTOC/uADloUipMGpuMInpfwUwOxKj6NirLGBFbJBCKkpDrkVWPZp+uu0xZ8ePkIqjFXfrhy0tAG9VgeWvF9EJxk9fFT2CGl4uKmMEEKKYdQsYOJu6a97+S71+tz10e0IIeUlV9+uzPvcZhypXr/zRsoLg/GlDekcs1XK7yGE1CsV9/A1HPtfA7R1r/QoCCGVZp19gOdvAobNqPRICGls7ILmcWw8F/j3/6X/nlnfAjY5EWhuTXedEEoZ9dXd8zHnepTVa0kIqRno4etsJuwArLlZpUdBCKk03fqrV4Z0ElJhUoZ0Zi2xIER6ZQ8Aug8Mrk+5ZGtqypZRlBBSd9DDRwghlWDAWPU6Ze/KjoOQRifn4UvZvrM4/E7g9QeBZi7ZCCHZ4NODEEIqQc9BwI8+7/zFIyEkjN67V62i2H+0+kcIIRlhSCchhFQKKnuEVJ4eQcjkjKMqOw5CCCkT9PARQgghpHHp0guYt7jSoyCEkLJBDx8hhBBCCCGE1ClU+AghhBBCCCGkTqHCRwghhBBCCCF1ChU+QgghhBBCCKlTqPARQgghhBBCSJ1ChY8QQgghhBBC6hQqfIQQQgghhBBSp7AOHyGEEEJIGkZvCSz7uNKjIISQRFDhI4QQQghJw8E3VXoEhBCSGIZ0EkIIIYQQQkidQoWPEEIIIYQQQuoUKnyEEEIIIYQQUqdQ4SOEEEIIIYSQOoUKHyGEEEIIIYTUKVT4CCGEEEIIIaROEVLKSo8hFUKIJQAWFtlNHwCLSzAck4EAylGUp9RjLcdvL0e/tTLOWuqzXP02cp+1IPe8l2qjz3L1Wyt9lqPfco2zHHJfC7+9VvosV7+10mc5+q2VcZarz2qd6ydIKXslaimlrKl/AOaXoI/LqnFcnTHWcvz2Rh5nLfVZS2OtoT6rXu55L9VGn7U01lr5/WUcZ8nlvhZ+e630WUtjrZXfXyvjLGOfVTnXpxlXo4Z03lzpAaSg1GMt129v1HHWUp/l6reR+ywXpRwr76Xa6LNc/dZKn+Xot1Flvhz91VKf5eq3VvosR7+1Ms5y9VkuOm2stRjSOV9KOa3S47Cp1nERQsoH5Z6QxoNyT0hjUa0yn2Zctejhu6zSA/BQreMihJQPyj0hjQflnpDGolplPvG4as7DRwghhBBCCCEkGbXo4SOkahBCrC6EOFMIsWnwWVR6TISQ8kGZJ6TxoNyTWqel0gMgpMbpC2Am1PN/vpTyq0oPiBBSVijzhDQelHtS09DDR0hGhBBCSvkCgJsATAGwdYWHRAgpI5R5QhoPyj2pB6jwARBC8O9AUhFMAHoD7DUAlgLYVgixmj5fscGRRFDuSRoo8/UB5Z6kgXJf+1DmFQ3/RxBCNEspOyo9DlKd+B7mUkophBgvhNhaSvkpgH8CGAVgB32+80ZJ0kK5Jz4o8/UL5Z74oNzXJ5T5PA2r8AkhmgFAStkuhBgohLhQCHGKEGJSpcdGqofgYe+Tk30A3CqEaANwI4A3AGwmhJgI0PJXjVDuSRyU+fqDck/ioNzXF5T5QhpW4ZNStgOAEGImgAcADAGwG4BfCiGmBuca9u9DFEKIHQCcLYQYGnzeTJ+TUp4N4D0AZwRWvmsA9AOwY3Celr8qg3JP4qDM1x+UexIH5b6+oMwX0jA/1rbACCG6CCH+BuBHAC6SUu4LYC6AVwGcDgB0AxMAzQC2AzBTCLEzgMuFEJsb508CcJoQYoSU8lEA8wGsL4TYsgJjJRaUe5IBynyNQ7knGaDc1zCU+XjqXuETimbbAiOlXAHgQaiMS72CYwsA3A5guBBir+D6uv8bET9SylsB/A/ANgA6oMI55hrnbw7O/yw4dDWAQQA20CEFpPOh3JOsUOZrF8o9yQrlvjahzCenbn+o/k+UinYhRE8hxM+EEN8XQmwfNPstgMcADBJCrBEcewzAPQCOFUJ0bTQLAMljWIx+DWBtACMB/BtAXyHEwUbTBwDsF2zqfhXAqVLKc3VIAek8KPekGCjztQnlnhQD5b72oMynpy4VviAW+xwhxIjg8xEAXoMS5HUBXCSEOCiwCFwBYKPgH6SUiwDcD0AAmFWB4ZMqIdjELaSUCwHcBVV75+vg/VFCiD5B08VQD5GZwXXPAI1lOaoGKPekWCjztQflnhQL5b62oMxno15v0hYot/wMIUR3ANMAnCClnB3E8d4H4BwAkFL+A8BLAHYQQcYlKLf9nlLKezp/6KSaMMIEfg2gL9TD5H4ASwA8IIR4E8DGALaVUp5lXdswlqMqgXJPioYyX3NQ7knRUO5rCsp8BkS9JhcSQlwMoDeAswEskVK+J4QYB+B3AIZBxfReJaU8SQixPoCroDZ3XqMFX7v5mYGpsRFCNEkpO4QQBwE4BCqG/78AdgHQLqW81m5bmZESyj0pBZT52oJyT0oB5b52oMynp+48fFYs9igAWwH4VAgxGsC1AP4tpRwD4DIAc4UQa0opnwBwhJTyavM/PogNbogbgfjRD3Up5Z8BLALwDQDdpJRX6QlA5Gu+cAKoAJR7Ukoo87UB5Z6UEsp99UOZz07dKXxGLPZLUNl4doaK6x0D4FMp5XeDpl0AvAhgz+C6hwAW0CRujPviIgAbQD1oTAsRN21XEMo9KTWU+eqHck9KDeW+uqHMZ6fuFD4g5J69EOo/fQcAkwG0CSEuF0IsBDAUwCZSynM91xKSI3jINEkpH4GSm+318cqOjGgo96SUUOZrA8o9KSWU++qHMp+NulT4gFx89VIAf4bKqPQBVKxvK4BzpZRzpJSLhSLy7yCEaDP7Lee4SfUiVWx/dwBfAVhY6fGQQkop90F/Pc2+yzVuUp1Q5muDEs/3o4UQvYP3DesNaGQo99VPiWV+uhBiQvlHXVnqNmmLiRDiGqh47B9JKT8xjjdHueeFSvn6/wCsBPCOlPKHZR8sqWqEEAcAmAFVf2dVpcdD/GSV+6DNCKg9AqugsrQdxf/vxoQyX1sUKffHA/glgIOllNeVd6SkmqHc1w5FrPGHA7gSwGgA+0sp/1v2wVaQurZYG9a5CwFMh4rxNTfdRt0IxwCYD+B9qBSv+wohfh+cq+u/G4nkKinlyZwAqpdi5D5o9wMATwB4G8BZALYFcLHVN2kcKPM1QLFyHzAVwGdQ6d7HlWOcpGag3Fc5WWReXyOE+AWABQBekFKO1spePc/xda24WLHYAvlY7LgFX18A4wDMlVJ+W0r5JwB7A9hDCNGb2Zkal0aO/64Vsso9kHvYdwDYQUp5opTyWQAPA+gdbBTn/3+Dwf/z2qBIuW8O3r4E4BoAGwKYJYToUq7xkuqGcl/9ZJF54/91GwAPSilPAAAhxIxg7V+3elHd/jBN0lhs/WAPFnwroGp23BEca4IqxPkC1E1FCKli0uzBMGS/JZgMzpdSzhdCbBBs/t4NwGsAZpv7eQkh1UWG+d72BGwMFeJ1C4DdAaxZ1gETQooig8x3DQ4dBGArIcSxQoj/ALgcwG0A/mQYgOqKulf4AmYDeBLADfYJIUS/IFTzUiBXl+MrKeV8KeUXgVW/AyoT0BIASztz4ISQzHjlHnDK/qrgdUXQZCiA/5NS9gBwPlTR1h8IIXqVe+CEkMykme/bg+N6LfQ2gBEArgDQFcD+QoizhRDrdMbACSGZSCPzywPj7gIob/7FUMXaZwH4NlRdv5OCa+vKwdMoSVucoVhCiClQtVYGAPgCwHlSyhtcGz2FEJcA+FpKeWKnDJoQUhRRIZgpZV8EoSN7ATgPwEQp5ZflHj8hJD0Z5vsmvU1DCPEAgMOllK8IIW4GsCOAWwHMCTICEkKqjAwy3yalXCmEaAGwqZTyfmOePxzA6VLKusva2RAevohY7DaolK6HALgXwJHBjdBubOxsCty7G0BZAyCEOEIIcWz5R04IyUrMHoxY2TdoCV6XQCV06F3qsRJCSkOG+b7DCNX+H4B5QohnoeT8YQBvAOhR1kETQjKTQeZXBsbdVQD+FbTV83wHgDeEUZKpXmgIhU8jhFhLCLG5EGJwcOhZANdJKR8HcCcACWCubh68SqgH/0cAhgkh7gNwDlToByGkBsgo+zqt89dCiLWhavzcLqV8vzPHTghJTxqZDxaATQBWBzAJwAVSys0B/BxA/84fPSEkLSnneQnkEr+IYJ6fAOAYAHfWo0e/IRQ+IUSzEOJyAP+F2ofzgBBiVynlSinl4qDZ01A3xGwhxMjA6tcUWA6mAdgJKtb3X1LKIVLKWyrxWwghySlC9kWwV29rIcQ/ADwK4EYp5fcq8kMIIYnIIvOASv4A4AwAM6WUVwTHbpdSHiSl/LDzfwkhJAlFzvPdoMqw3ATl4b9JSnl+RX5ImWkIhQ/KYjcWqkbHdgD+AODXQojNdINAm78XwHsATgmOdQThnJ8DmAdglJTyrE4dOSGkGLLKvoRK0PQy1B6eEVLKn3bqyAkhWcgk8wFvSymX6SQu9Za0gZA6pZh5fjlU0fZ7AQyTUv6sU0feidStwieE6G1k3toIwEgp5ccAOqSUP4eyBHxTCDHauOwlqHIMk4UQPxVC/BvA5lLKx6SUZ9Wji5eQeqOEsr+NlPJ1KeVlUsolnfojCCGJKZHMPwJgayDn7WMtNkKqlBLP869JKS+q93m+7hQ+IcQ4IcSdAP4G4IYgXON5AG8JIabKfNH0nwFYF0Au3bKUciWAdqib55sAfiulvK9TfwAhJBNlkP27O/UHEEJSUWKZv1xKeWen/gBCSCo4z2enrhS+IJ3qfVD1OE4H0A8qJr8FwIdQrl4AgJTyGagNnQcF1zYLIbYFcB2A30gp15BS/qFTfwAhJBOUfUIaC8o8IY0FZb446qoOnxDibABvSikvDz4PA/AigPFQ/+nrw/DaCSF2BfD/AEwP4vbXAPCllPLzivwAQkgmKPuENBaUeUIaC8p8cbTEN6kpLgWwAgCEEF0ALAPwKoBuAP4OtaHzZCHEq1LKNwFMB3CXlHIZAEgp363IqAkhxULZJ6SxoMwT0lhQ5ougrhQ+KeU7gMqsJaVcIYSYCBW2+nZQZ+dCqFpatwohPgcwAcCcyo2YEFIKKPuENBaUeUIaC8p8cdSVwqcxMmttAWBhsFETUsrnhBB7AlgPwCQp5R8rNERCSBmg7BPSWFDmCWksKPPZqEuFTwjRLKVsBzADwB3BsWOhtP1zpJTzAcyv4BAJIWWAsk9IY0GZJ6SxoMxnoy4VPilluxCiBUB/AIOFEA8CGAXgMCnloooOjhBSNij7hDQWlHlCGgvKfDbqKkuniRBiCoCnoVK1nielPLfCQyKEdAKUfUIaC8o8IY0FZT499azwtQGYC1VvY3mlx0MI6Rwo+4Q0FpR5QhoLynx66lbhI4QQQgghhJBGp6nSAyCEEEIIIYQQUh6o8BFCCCGEEEJInUKFjxBCCCGEEELqFCp8hBBCCCGEEFKnUOEjhBBCCCGEkDqFCh8hhBBCCCGE1ClU+AghhBBCCCGkTqHCRwghhBBCCCF1ChU+QgghhBBCCKlT/j+Uhp4tgAG06AAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "valid_start_dt = '2014-09-01 00:00:00'\n", - "test_start_dt = '2014-11-01 00:00:00'\n", - "\n", - "energy.plot(y=['load', 'temp'], subplots=True, figsize=(15, 8), fontsize=12)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load and temperature in first week of July 2014" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "energy['2014-07-01':'2014-07-07'].plot(y=['load', 'temp'], subplots=True, figsize=(15, 8), fontsize=12)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data preparation\n", - "\n", - "For this example, we will set *T=6*. This means that the input for each sample is a vector of the prevous 6 hours of the energy load. The choice of *T=6* was arbitrary but should be selected through experimentation.\n", - "\n", - "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "T = 6\n", - "HORIZON = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data preparation - training set" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Create training dataset with load and temp features\n", - "train = energy.copy()[energy.index < valid_start_dt][['load', 'temp']]\n", - "\n", - "# Fit a scaler for the y values\n", - "y_scaler = MinMaxScaler()\n", - "y_scaler.fit(train[['load']])\n", - "\n", - "# Also scale the input features data (load and temp values)\n", - "X_scaler = MinMaxScaler()\n", - "train[['load', 'temp']] = X_scaler.fit_transform(train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the TimeSeriesTensor convenience class to:\n", - "1. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n", - "2. Discard any samples with missing values\n", - "3. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras\n", - "\n", - "The class takes the following parameters:\n", - "\n", - "- **dataset**: original time series\n", - "- **H**: the forecast horizon\n", - "- **tensor_structure**: a dictionary discribing the tensor structure in the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) }\n", - "- **freq**: time series frequency\n", - "- **drop_incomplete**: (Boolean) whether to drop incomplete samples" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
tensortargetX
featureyloadtemp
time stept+1t-5t-4t-3t-2t-1tt-5t-4t-3t-2t-1t
2012-01-01 05:00:000.180.220.180.140.130.130.150.420.430.400.410.420.41
2012-01-01 06:00:000.230.180.140.130.130.150.180.430.400.410.420.410.40
2012-01-01 07:00:000.290.140.130.130.150.180.230.400.410.420.410.400.39
2012-01-01 08:00:000.350.130.130.150.180.230.290.410.420.410.400.390.39
2012-01-01 09:00:000.370.130.150.180.230.290.350.420.410.400.390.390.43
\n", - "
" - ], - "text/plain": [ - "tensor target X \\\n", - "feature y load temp \n", - "time step t+1 t-5 t-4 t-3 t-2 t-1 t t-5 t-4 t-3 t-2 \n", - "2012-01-01 05:00:00 0.18 0.22 0.18 0.14 0.13 0.13 0.15 0.42 0.43 0.40 0.41 \n", - "2012-01-01 06:00:00 0.23 0.18 0.14 0.13 0.13 0.15 0.18 0.43 0.40 0.41 0.42 \n", - "2012-01-01 07:00:00 0.29 0.14 0.13 0.13 0.15 0.18 0.23 0.40 0.41 0.42 0.41 \n", - "2012-01-01 08:00:00 0.35 0.13 0.13 0.15 0.18 0.23 0.29 0.41 0.42 0.41 0.40 \n", - "2012-01-01 09:00:00 0.37 0.13 0.15 0.18 0.23 0.29 0.35 0.42 0.41 0.40 0.39 \n", - "\n", - "tensor \n", - "feature \n", - "time step t-1 t \n", - "2012-01-01 05:00:00 0.42 0.41 \n", - "2012-01-01 06:00:00 0.41 0.40 \n", - "2012-01-01 07:00:00 0.40 0.39 \n", - "2012-01-01 08:00:00 0.39 0.39 \n", - "2012-01-01 09:00:00 0.39 0.43 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n", - "train_inputs = TimeSeriesTensor(dataset=train,\n", - " target='load',\n", - " H=HORIZON,\n", - " tensor_structure=tensor_structure,\n", - " freq='H',\n", - " drop_incomplete=True)\n", - "train_inputs.dataframe.head(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "X_train = train_inputs['X']\n", - "y_train = train_inputs['target']" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(23370, 1)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_train.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0.18],\n", - " [0.23],\n", - " [0.29]])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_train[:3]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(23370, 6, 2)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[0.22, 0.42],\n", - " [0.18, 0.43],\n", - " [0.14, 0.4 ],\n", - " [0.13, 0.41],\n", - " [0.13, 0.42],\n", - " [0.15, 0.41]],\n", - "\n", - " [[0.18, 0.43],\n", - " [0.14, 0.4 ],\n", - " [0.13, 0.41],\n", - " [0.13, 0.42],\n", - " [0.15, 0.41],\n", - " [0.18, 0.4 ]],\n", - "\n", - " [[0.14, 0.4 ],\n", - " [0.13, 0.41],\n", - " [0.13, 0.42],\n", - " [0.15, 0.41],\n", - " [0.18, 0.4 ],\n", - " [0.23, 0.39]]])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Data preparation - validation set" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", - "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load', 'temp']]\n", - "valid[['load', 'temp']] = X_scaler.transform(valid)\n", - "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)\n", - "y_valid = valid_inputs['target']\n", - "X_valid = valid_inputs['X']" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1463, 1)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y_valid.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1463, 6, 2)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_valid.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quiz: Implement multivariate RNN" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.models import Model, Sequential\n", - "from keras.layers import GRU, Dense\n", - "from keras.callbacks import EarlyStopping" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Fill in your code below and replace the question mark" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Implement your RNN model with the data prepared above and the following requirements:\n", - "1. Use 2 features: past load and temperature\n", - "2. Stack 2 GRU layers\n", - "3. 6 hidden units in the first GRU layer\n", - "4. 4 hidden units in the second GRU layer\n", - "5. 5 epochs\n", - "6. Batch size 32" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "FIRST_LAYER_LATENT_DIM = ? # number of units in the 1st RNN layer\n", - "SECOND_LAYER_LATENT_DIM = ? #number of units in the 2nd RNN layer\n", - "BATCH_SIZE = ? # number of samples per mini-batch\n", - "EPOCHS = ? # maximum number of times the training algorithm will cycle through all samples" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Fill in your code to replace the question mark\n", - "# Hint: there is a parameter you need to add when stacking multiple RNN layers\n", - "model = Sequential()\n", - "?\n", - "?\n", - "model.add(Dense(HORIZON))" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": "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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('./images/one_step_RNN_multivariate_mutilayer.png')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once you done, run the rest of the notebook to check if your model works." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.compile(optimizer='RMSprop', loss='mse')\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specify the early stopping criteria. We **monitor** the validation loss (in this case the mean squared error) on the validation set after each training epoch. If the validation loss has not improved by **min_delta** after **patience** epochs, we stop the training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)\n", - "\n", - "history = model.fit(X_train,\n", - " y_train,\n", - " batch_size=BATCH_SIZE,\n", - " epochs=EPOCHS,\n", - " validation_data=(X_valid, y_valid),\n", - " callbacks=[earlystop],\n", - " verbose=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_df = pd.DataFrame.from_dict({'train_loss':history.history['loss'], 'val_loss':history.history['val_loss']})\n", - "plot_df.plot(logy=True, figsize=(10,10), fontsize=12)\n", - "plt.xlabel('epoch', fontsize=12)\n", - "plt.ylabel('loss', fontsize=12)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the test set" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", - "test = energy.copy()[test_start_dt:][['load', 'temp']]\n", - "test[['load', 'temp']] = X_scaler.transform(test)\n", - "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)\n", - "X_test = test_inputs['X']\n", - "y_test = test_inputs['target']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = model.predict(X_test)\n", - "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n", - "eval_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mape(eval_df['prediction'], eval_df['actual'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eval_df[eval_df.timestamp<'2014-11-08'].plot(x='timestamp', y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8), fontsize=12)\n", - "plt.xlabel('timestamp', fontsize=12)\n", - "plt.ylabel('load', fontsize=12)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Quiz_RNN_encoder_decoder.ipynb b/Quiz_RNN_encoder_decoder.ipynb deleted file mode 100644 index e4e0935..0000000 --- a/Quiz_RNN_encoder_decoder.ipynb +++ /dev/null @@ -1,646 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multi step model (simple encoder-decoder)\n", - "\n", - "In this notebook, we demonstrate how to:\n", - "- prepare time series data for training a RNN forecasting model\n", - "- get data in the required shape for the keras API\n", - "- implement a RNN model in keras to predict the next 3 steps ahead (time *t+1* to *t+3*) in the time series. This model uses a simple encoder decoder approach in which the final hidden state of the encoder is replicated across each time step of the decoder. \n", - "- enable early stopping to reduce the likelihood of model overfitting\n", - "- evaluate the model on a test dataset\n", - "\n", - "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load.\n", - "\n", - "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "import datetime as dt\n", - "from collections import UserDict\n", - "from IPython.display import Image\n", - "%matplotlib inline\n", - "\n", - "from common.utils import load_data, mape, TimeSeriesTensor, create_evaluation_df\n", - "\n", - "pd.options.display.float_format = '{:,.2f}'.format\n", - "np.set_printoptions(precision=2)\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load data into Pandas dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
loadtemp
2012-01-01 00:00:002,698.0032.00
2012-01-01 01:00:002,558.0032.67
2012-01-01 02:00:002,444.0030.00
2012-01-01 03:00:002,402.0031.00
2012-01-01 04:00:002,403.0032.00
\n", - "
" - ], - "text/plain": [ - " load temp\n", - "2012-01-01 00:00:00 2,698.00 32.00\n", - "2012-01-01 01:00:00 2,558.00 32.67\n", - "2012-01-01 02:00:00 2,444.00 30.00\n", - "2012-01-01 03:00:00 2,402.00 31.00\n", - "2012-01-01 04:00:00 2,403.00 32.00" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "energy = load_data('data/')\n", - "energy.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "valid_start_dt = '2014-09-01 00:00:00'\n", - "test_start_dt = '2014-11-01 00:00:00'\n", - "\n", - "T = 6\n", - "HORIZON = 3" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create training set containing only the model features" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "train = energy.copy()[energy.index < valid_start_dt][['load', 'temp']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Scale data to be in range (0, 1). This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MinMaxScaler\n", - "\n", - "y_scaler = MinMaxScaler()\n", - "y_scaler.fit(train[['load']])\n", - "\n", - "X_scaler = MinMaxScaler()\n", - "train[['load', 'temp']] = X_scaler.fit_transform(train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use the TimeSeriesTensor convenience class to:\n", - "1. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n", - "2. Discard any samples with missing values\n", - "3. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras\n", - "\n", - "The class takes the following parameters:\n", - "\n", - "- **dataset**: original time series\n", - "- **H**: the forecast horizon\n", - "- **tensor_structure**: a dictionary discribing the tensor structure in the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) }\n", - "- **freq**: time series frequency\n", - "- **drop_incomplete**: (Boolean) whether to drop incomplete samples" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n", - "train_inputs = TimeSeriesTensor(train, 'load', HORIZON, {'X':(range(-T+1, 1), ['load', 'temp'])})" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
tensortargetX
featureyloadtemp
time stept+1t+2t+3t-5t-4t-3t-2t-1tt-5t-4t-3t-2t-1t
2012-01-01 05:00:000.180.230.290.220.180.140.130.130.150.420.430.400.410.420.41
2012-01-01 06:00:000.230.290.350.180.140.130.130.150.180.430.400.410.420.410.40
2012-01-01 07:00:000.290.350.370.140.130.130.150.180.230.400.410.420.410.400.39
2012-01-01 08:00:000.350.370.370.130.130.150.180.230.290.410.420.410.400.390.39
2012-01-01 09:00:000.370.370.370.130.150.180.230.290.350.420.410.400.390.390.43
\n", - "
" - ], - "text/plain": [ - "tensor target X \\\n", - "feature y load temp \n", - "time step t+1 t+2 t+3 t-5 t-4 t-3 t-2 t-1 t t-5 t-4 \n", - "2012-01-01 05:00:00 0.18 0.23 0.29 0.22 0.18 0.14 0.13 0.13 0.15 0.42 0.43 \n", - "2012-01-01 06:00:00 0.23 0.29 0.35 0.18 0.14 0.13 0.13 0.15 0.18 0.43 0.40 \n", - "2012-01-01 07:00:00 0.29 0.35 0.37 0.14 0.13 0.13 0.15 0.18 0.23 0.40 0.41 \n", - "2012-01-01 08:00:00 0.35 0.37 0.37 0.13 0.13 0.15 0.18 0.23 0.29 0.41 0.42 \n", - "2012-01-01 09:00:00 0.37 0.37 0.37 0.13 0.15 0.18 0.23 0.29 0.35 0.42 0.41 \n", - "\n", - "tensor \n", - "feature \n", - "time step t-3 t-2 t-1 t \n", - "2012-01-01 05:00:00 0.40 0.41 0.42 0.41 \n", - "2012-01-01 06:00:00 0.41 0.42 0.41 0.40 \n", - "2012-01-01 07:00:00 0.42 0.41 0.40 0.39 \n", - "2012-01-01 08:00:00 0.41 0.40 0.39 0.39 \n", - "2012-01-01 09:00:00 0.40 0.39 0.39 0.43 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "train_inputs.dataframe.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construct validation set (keeping T hours from the training set in order to construct initial features)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", - "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load', 'temp']]\n", - "valid[['load', 'temp']] = X_scaler.transform(valid)\n", - "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quiz: Implement an encoder-decoder RNN" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Implement your RNN model with the data prepared above and the following requirements:\n", - "1. Use 2 features: past load and temperature\n", - "2. Stack 2 **LSTM** layers for the encoder RNN\n", - "3. 12 hidden units in the first LSTM layer\n", - "4. 6 hidden units in the second LSTM layer\n", - "5. Repeat the vector output of the second encoder layer 3 times (one for each time step in the forecast horizon)\n", - "5. Add a decoder LSTM with 6 hidden units\n", - "6. 5 epochs\n", - "7. Batch size 32\n", - "\n", - "The model will have the following structure:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABzAAAARTCAMAAAFEz3VHAAAAAXNSR0IArs4c6QAAAARnQU1BAACx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\nAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('./images/encoder_decoder_multilayer.png')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.models import Model, Sequential\n", - "from keras.layers import LSTM, Dense, RepeatVector, TimeDistributed, Flatten\n", - "from keras.callbacks import EarlyStopping" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "ENCODER_LAYER_1_DIM = ?\n", - "ENCODER_LAYER_2_DIM = ?\n", - "DECODER_DIM = ?\n", - "BATCH_SIZE = 32\n", - "EPOCHS = 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "model = Sequential()\n", - "?\n", - "?\n", - "?\n", - "?\n", - "model.add(Flatten())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "model.compile(optimizer='RMSprop', loss='mse')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fit(train_inputs['X'],\n", - " train_inputs['target'],\n", - " batch_size=BATCH_SIZE,\n", - " epochs=EPOCHS,\n", - " validation_data=(valid_inputs['X'], valid_inputs['target']),\n", - " callbacks=[earlystop],\n", - " verbose=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate the model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n", - "test = energy.copy()[test_start_dt:][['load', 'temp']]\n", - "test[['load', 'temp']] = X_scaler.transform(test)\n", - "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "predictions = model.predict(test_inputs['X'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n", - "eval_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n", - "eval_df.groupby('h')['APE'].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mape(eval_df['prediction'], eval_df['actual'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot actuals vs predictions at each horizon for first week of the test period. As is to be expected, predictions for one step ahead (*t+1*) are more accurate than those for 2 or 3 steps ahead" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_df = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+1')][['timestamp', 'actual']]\n", - "for t in range(1, HORIZON+1):\n", - " plot_df['t+'+str(t)] = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+'+str(t))]['prediction'].values\n", - "\n", - "fig = plt.figure(figsize=(15, 8))\n", - "ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n", - "ax = fig.add_subplot(111)\n", - "ax.plot(plot_df['timestamp'], plot_df['t+1'], color='blue', linewidth=4.0, alpha=0.75)\n", - "ax.plot(plot_df['timestamp'], plot_df['t+2'], color='blue', linewidth=3.0, alpha=0.5)\n", - "ax.plot(plot_df['timestamp'], plot_df['t+3'], color='blue', linewidth=2.0, alpha=0.25)\n", - "plt.xlabel('timestamp', fontsize=12)\n", - "plt.ylabel('load', fontsize=12)\n", - "ax.legend(loc='best')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/README.md b/README.md index 7ca1afb..9885c96 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ The following steps will guide you to setup code and data in your Azure Notebook 1. Once you are logged into Azure Notebooks, go to '***My Projects***' on the top left, and then click '***Upload GitHub Repo***'. -2. In the pop out window, for '***GitHub repository***' type in: '***Azure/DeepLearningForTimeSeriesForecasting***'. Select '***Clone recursively***'. Then type in any name you prefer for '***Project Name***' and '***Project ID***'. Once you have filled all boxes, click '***Import***'. Please wait till you see a list of files cloned from git repository to your project. +2. In the pop out window, for '***GitHub repository***' type in: '***jspoelstra/DeepLearningForTimeSeriesForecasting***'. Select '***Clone recursively***'. Then type in any name you prefer for '***Project Name***' and '***Project ID***'. Once you have filled all boxes, click '***Import***'. Please wait till you see a list of files cloned from git repository to your project. 3. Open the notebook '***0_data_setup.ipynb***'. Make sure you see '***Python 3.6***' kernel on the top right. If not, you can select '***Kernel***', then '***Change kernel***' to make changes. diff --git a/ReferenceNotebook/Quiz_1_Answer.ipynb b/ReferenceNotebook/Quiz_1_Answer.ipynb new file mode 100644 index 0000000..57d3264 --- /dev/null +++ b/ReferenceNotebook/Quiz_1_Answer.ipynb @@ -0,0 +1,757 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Practice: Dilated CNN model\n", + "\n", + "In this notebook, we work through implementing a CNN for forecasting. The notebook covers:\n", + "- preparing time series data for training a Convolutional Neural Network (CNN) forecasting model\n", + "- getting data in the required shape for the keras API\n", + "- implementing a CNN model in keras to predict 3 steps ahead (time *t+1* to *t+1*) in the time series\n", + "- enabling early stopping to reduce the likelihood of model overfitting\n", + "- evaluating the model on a test dataset\n", + "\n", + "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load data only.\n", + "\n", + "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import tensorflow as tf\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data from `data/energy.parquet` into a Pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
loadtemp
2012-01-01 00:00:002,698.0032.00
2012-01-01 01:00:002,558.0032.67
2012-01-01 02:00:002,444.0030.00
2012-01-01 03:00:002,402.0031.00
2012-01-01 04:00:002,403.0032.00
\n", + "
" + ], + "text/plain": [ + " load temp\n", + "2012-01-01 00:00:00 2,698.00 32.00\n", + "2012-01-01 01:00:00 2,558.00 32.67\n", + "2012-01-01 02:00:00 2,444.00 30.00\n", + "2012-01-01 03:00:00 2,402.00 31.00\n", + "2012-01-01 04:00:00 2,403.00 32.00" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "# Insert code START\n", + "file_name = os.path.join('data', 'energy.parquet')\n", + "energy = pd.read_parquet(file_name)\n", + "# Insert code END\n", + "\n", + "assert energy.shape == (26304, 2)\n", + "energy.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot energy load and temperature in first week of July 2014" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3gAAAHRCAYAAAA44htBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdd3xV9f348dcne96ELDIhARICIYQRQDYC7o2te7VurVptbWtb+/Vbv9ZftVptHdXWvbHiqop1I0MggUwgrCTkZs+bPe/n90cSmiIj4ybnjvfz8bgPSc4957yvyb057/P5fN5vpbVGCCGEEEIIIYTjczM6ACGEEEIIIYQQtiEJnhBCCCGEEEI4CUnwhBBCCCGEEMJJSIInhBBCCCGEEE5CEjwhhBBCCCGEcBKS4AkhhBBCCCGEk/AwOoChCgsL0/Hx8UaHIYQQQgghhBCGyMzMrNFahx9tm8MlePHx8WRkZBgdhhBCCCGEEEIYQilVfKxtMkVTCCGEEEIIIZyEJHhCCCGEEEII4SQkwRNCCCGEEEIIJ+Fwa/CEEEIIIYQQAqCrqwuz2Ux7e7vRoYwKHx8fYmNj8fT0HPQ+g0rwlFJfAycB3X3fKtVaT+3bdhnwIBAGfAb8WGtd17ctBHgOOBWoAe7RWr8+4LjH3FcIIYQQQgghjsdsNhMYGEh8fDxKKaPDsSmtNbW1tZjNZhISEga931CmaP5Eax3Q9+hP7lKAZ4ArgfFAK/DUgH2eBDr7tl0OPN23z2D2FULYgKWti+tfzuCq57fxdUEVWmujQxJCCCGEsIn29nZCQ0OdLrkDUEoRGho65NHJkU7RvBz4UGu9oS+Ie4HdSqlAwApcCMzQWjcDG5VSH9Cb0P3qePtqrZtGGJcQAiipa+XHL26nqLaFcX5eXPPCdpLGB3DdkkmcNzsabw93o0MUQgghhBgRZ0zu+g3ntQ1lBO9BpVSNUmqTUmpF3/dSgOz+J2itD9A7YpfU9+jWWu8dcIzsvn1OtK8QYoSyShq44KlNVDa28/KPF7Dxlyt59KI03JTiF+/ksPSPX7GnotHoMIUQQgghHFpAQIBNjnPffffxpz/9acTHGWyC90tgEhADPAt8qJSaDAQAliOeawEC+7YdefXYv40T7PtflFI3KKUylFIZ1dXVgwxZCNe1Pq+ci5/Zgq+XO+tuWczCyaF4ebixZk4sn9yxlFevXYBVwx1vZNHe1WN0uEIIIYQQwkYGleBprbdqrZu01h1a65eATcCZQDNgOuLpJqDpBNsYxPaB539Wa52utU4PDw8fTMhCuKz3dpZy82s7mB5t4r1bFjMl4r/vKimlWJIYxp9+OJOCyib+uH6PQZEKIYQQQjgPrTV33303M2bMIDU1lbfeeguA5uZmVq1axZw5c0hNTeX9998/vM8DDzxAUlISS5YsoaCgwCZxDHcNngYUkA+k9X9TKTUJ8Ab20rsGz0Mplai13tf3lLS+fTjBvkKIYcgqaeAX7+QwPz6El348Hx/PY6+xWzE1gqsXTuSFTUWcPDWCZUly80QIIYQQjut/P8xnV5ltl59MjzbxP+eknPiJwLp168jKyiI7O5uamhrmzZvHsmXLCA8P591338VkMlFTU8NJJ53Eueeey44dO3jzzTfJysqiu7ubOXPmMHfu3BHHfMIRPKVUsFLqNKWUj1LKQyl1ObAMWA+8BpyjlFqqlPIHfg+s6xvtawHWAb9XSvkrpRYD5wGv9B36mPuO+FUJ4YIqLO3c8HIG403ePH3F3OMmd/3uOXMaiREB/OztbOpaOscgSiGEEEII57Rx40YuvfRS3N3dGT9+PMuXL2f79u1orfn1r3/NzJkzWb16NaWlpVRWVvLtt99ywQUX4Ofnh8lk4txzz7VJHIMZwfME/g9IBnqAPcD5/cVTlFI30ZushQKfAz8asO8twPNAFVAL3Ky1zgfQWuefYF8hxCC1d/Vw4ysZtHR088q1iwnx9xrUfj6e7jx2ySzOf3IT96zL4W9XzHXqSlRCCCGEcF6DHWkba6+99hrV1dVkZmbi6elJfHz8qDZmP+EInta6Wms9T2sdqLUO1lqfpLX+bMD217XWE7TW/lrr8wY2Ktda12mtz+/bNmFgk/MT7SuEGBytNb98J4dss4U/XzyLqZHfq1N0XCnRQdx92lQ+za9kbUbJKEUphBBCCOHcli5dyltvvUVPTw/V1dVs2LCB+fPnY7FYiIiIwNPTk6+++ori4mIAli1bxnvvvUdbWxtNTU18+OGHNoljpH3whBAG+9s3B3k/q4yfn5rEqSmRwzrGdUsm8eWeKh74aDenz4giyNfTxlEKIYQQQji3Cy64gC1btpCWloZSioceeojIyEguv/xyzjnnHFJTU0lPTyc5ORmAOXPmcPHFF5OWlkZERATz5s2zSRxKa22TA42V9PR0nZGRYXQYQtiFQ7WtrHzka05NGc+Tl80Z0fTK/DILZ/1lIz85eQo/P22qDaMUQgghhBgdu3fvZtq0aUaHMaqO9hqVUpla6/SjPX8ojc6FEHbmsS/24u6m+N3ZKSNeO5cSHcQ5adE8v6mQ6qYOG0UohBBCCCHGkiR4Qjio/VVNvLezlKsWTiQyyMcmx7zrlCQ6uq08+dV+mxxPCCGEEEKMLVmDJ0ZFZ7eVupbO/zxaO+no6iHC5ENk38Pk6yEVG0fg0c/24uvpzs0rptjsmAlh/lyUHsvrWw9x3dIEYsf52ezYQgghhBBi9EmCJ2zC0tpFRnEd24rq2F5YR26pha6e46/v9PV05/zZ0fz2rOn4e8uv4lDklVr4OLeC21clDrolwmDdviqRd3aU8vjn+3j4h2k2PbYQQgghhK1prZ120GA49VLkqloMW2N7Fx/nlPPODjMZxfVoDZ7uitSYIH68OIEJoX6E+HkR4t/78PJwo6qpgwpLO5WN7eytbOLN7SV8d7COxy+ZxczYYKNfksP4078LCPbz5LqlCTY/dlSQL1edNJHnNxVy4/JJTIkYWtsFIYQQQoix4uPjQ21tLaGhoU6X5Gmtqa2txcdnaEtxJMETQ7ZxXw1vZZTw7/wKOrqtTA73545ViSxICGVWXDC+Xu7H3HdiqP9/fb1mTix3vpXFmqc2c9epSdy4bDLubs715rS17UV1fF1Qza/OSMbkMzrtDG5eMZk3th3i0c/28tTlc0flHEIIIYQQIxUbG4vZbKa6utroUEaFj48PsbGxQ9pHEjwxaFprHvn3Xp74aj9Bvp5clB7HhXNjSYsNGvYdk5MmhbL+jmX8+t1cHlpfwLd7a3jumnT8vORX82i01jz8aQHhgd5cvTB+1M4TGuDNdUsn8fgX+8gsrmfuxHGjdi4hhBBCiOHy9PQkIcH2M5ocmVTRFINitWr+98NdPPHVfi6ZF8e236zi/vNnMCsueMTD4UF+njxx2Wz+eGEq3xXWcu97+TaK2vls3F/DtsI6bls55bgjpbZw3dIEYoJ9ufW1HVQ1tY/quYQQQgghhG1IgidOqLvHyt3/zOHFzUVcvzSBB9ek4u1h2+RCKcXF8yZw+8pE3tlh5u2MEpse31m8uKmIiEBvLp4XN+rnCvTx5Nmr5mJp6+LGVzJp7+oZ9XMKIYQQQoiRkQRPHFdHdw+3vr6Dd3aY+dkpSfz6zGmjuoD19lWJLJwUyr3v57G3smnUzuOIyi1tfFVQxQ/TY22eYB9LSnQQj16Uxs5DDfzm3bxhVXISQgghhBBjRxI8cUwd3T3c+Eomn+ZXct8507ltVeKoVydyd1M8fuksArw9ufW1HbR2do/q+RzJ2u1mrBoumTdhTM97RmoUP13dO7L63MbCMT23EEIIIYQYGknwxFF191i5/Y2dfF1Qzf9bk8o1i8du8WpEoA+PXzKL/dXN/O59WY8H0GPVrM0oYWliGHEhY998/PaViZwxI5I/fLybrwqqxvz8QgghhBBicCTBE9/TY9X87O3swyN3l8wf2xEjgMVTwrhtZSL/zDTz6L8LXL7Ix4Z91ZQ2tI356F0/NzfFIxelMTXSxB1v7KS0oc2QOIQQQgghxPFJLXrxX7TW/ObdXN7PKuMXp08d05G7I92xKpFdZY385cv9PPHVfk6aFMo5adGcnhLJOH8vw+IywpvbDhHq78Up08cbFoOflwd/u2IOZz7+LXe+lcUb158kPQtHUXNHN1/uqaKwuoWi2hYKa1oorm0hItCHZUlhLEsKZ158CD6eY7MeUwghhBCOQTla0YT09HSdkZFhdBhOSeveVggvbi7itpVT+NmpU40OCYB9lU18mF3GhznlFNa04OPpxtobFzIzNtjo0MZEVWM7i/7fl1y7JIF7zpxmdDj8M9PMz9/O5u7TpnLryVOMDsfpaK35V0459/9rF1VNHQDEBPsSH+bHhBB/DtW1sL2wns4eKz6ebixNDOcPF6QSHuhtcORCCCGEGCtKqUytdfrRtskInjjs/awyXtxcxLVLErjrlCSjwzkscXwgd506lTtPSSKvtJGrX9jGw58W8Mq1C4wObUy8nWmm26rHpDXCYFw4J4avC6r482d7WTwljFlxrpFoj4UD1c38z/v5bNxfQ0q0iccvmc3sCcHfG6Vr7exm68E6vtlbzVvbS7jq+W28ecNJBPl6GhS5EEIIIeyFrMETQO8F4//7ZA+pMUH8ZpRbIQyXUorU2CBuXDaJb/fVkFFUZ3RIo85q1by1vYQFCSFMCg8wOhyg9+fwwPmpRAR689M3d9LSIZVOR6qju4dH/l3AGY99S7a5gd+fl8IHP1nCwsmhR52C6eflwcnJEdx3bgp/u3Iu+6uauPbF7bR1Sq9CIYQQwtVJgicAeHbDQSoa27n37Om42fm6qisXTiQswIs/f77X6FBG3ZaDtRyqa+VSAwrdHE+Qnyd/vngWxXWt/O+HUul0JPJKLZz710389cv9nJkayRc/W85VC+MHvb5xeVI4j108mx2H6rnp1Uw6u62jHLEQQggh7JkkeIJySxvPfHOQs1KjmJ8QYnQ4J+Tn5cFNyyezaX8t3x2sNTqcUfX6tkME+Xpy+oxIo0P5ngWTQrllxWTWZphZn1dhdDgOp7PbyqOf7eW8JzdR39rJ89ek89gls4kI9Bnysc6aGcUfLkjlm73V3Lk2ix6rY62tFkIIIYTtDCnBU0olKqXalVKv9n29QillVUo1D3hcPeD5IUqpd5VSLUqpYqXUZUcc77K+77copd5TStl/duGEHl5fQI/W/OqMZKNDGbQrTppIeKA3j362F0crFDRYTe1d/Du/ggtmx9htpcSfrk4iOTKQ+/+1i/YumR44WIU1LZz/5Cb+8sU+zk2L5rM7l7MyeWQVUi+ZP4Ffn5nMRznl3LMuB6skeUIIIYRLGuoI3pPA9iO+V6a1DhjweOmI53cC44HLgaeVUikAff99Briyb3sr8NQwXoMYgeySBtbtLOXaJQmGNNAeLh9Pd25dMZlthXVsOeCco3gb99XQ1aM5MzXK6FCOydPdjf85J4XShjae3XDQ6HAcQlePlZtfzaTM0sYzV87lzxfPIsjPNsVRblg2mdtXJbI2w8zd/8yRkTwhhBDCBQ06wVNKXQI0AF8M8vn+wIXAvVrrZq31RuADehM66E34PtRab9BaNwP3AmuUUoFDeQFi+LTW3P+vXYQFeHPLislGhzNkl8yfQKTJx2lH8b7cU4XJx4M5E+y7SuXCyaGcmRrJ018foNwiDdBP5NkNB9lT0cRDF87ktBTbT72965Qk7lydxDs7zNy1NovuHlmTJ4QQQriSQSV4SikT8HvgrqNsjlBKVSqlCpVSf+5L7ACSgG6t9cBKGNlASt+/U/q+BkBrfYDe0b7v1edXSt2glMpQSmVUV1cPJmQxCB/llpNRXM/PT00i0Mfxyqv7eLpz68opZBTXs3F/jdHh2JTVqvmqoJplSeF4uNv/Utl7zphGj9b88ZM9Rodi1w5WN/P4F/s4MzWSU0chuet3x+pE7j5tKu9nlXHHW1l0SZInhBBCuIzBXjneDzyntTYf8f09wCwgClgJzAUe7dsWADQe8XwLEDhgu+U42w/TWj+rtU7XWqeHh4cPMmRxPFar5k+fFpAcGcgP0+2jv9pwXJQeS0ywL3/75oDRodhUXpmFmuYOViZHGB3KoMSF+HHjskm8l1VGZrHzt68YDqtVc8+6XHw83Ljv3JQT7zBCt5485fCavNte30lHt6yRFEIIIVzBCRM8pdQsYDXw5yO3aa0rtNa7tNZWrXUh8At6p2UCNAOmI3YxAU2D3C5G0Tf7qimqbeXmFZMHXY7dHnl7uLN6WgRZhxqcqqjEV3uqUaq3BL6juGn5ZMabvPnfD3c51c/CVt7KKGFrYR2/OWvasCplDscNyybzu7Onsz6/goue+Y6yBplCK4QQQji7wYzgrQDigUNKqQrg58CFSqkdR3muHnDMvYCHUipxwPY0oL9pVn7f1wAopSYB3n37iVH2ypZiwgK8OWOG/RbwGKzkKBMtnT2UOtHF65cFVaTFBhMa4G10KIPm7+3Br85IJsds4Z0dRw72u7bKxnb+8PFuFk4K5aIxHjH/8ZIEnr58Dgeqmjn7rxvZ5GTTmYUQQgjx3zwG8ZxngTcHfP1zehO+m5VSJwMHgUNALPD/gPcBtNYtSql1wO+VUtfRO5XzPGBR33FeA7YopZYCO+hd47dOay0jeKPsUG0rXxVUcdvJU/DysP/1XScyNbJ3Vu/u8kaHqgR6LDXNHeSYG7hz9feWo9q989JieHlLMX9cX8DSxHAig8ZmpMre/c/7+XR2W/nDmlSUGvsR8zNSo0iKDOSmVzK58rmt3H1aMjctn2RILEIIIQRAdVMHeWUWuns0PVYrPVbo0ZpAbw/CArwJD/QmNMALTweoRWBvTpjgaa1b6W1hAIBSqhlo11pXK6VmA68C44Ba4F3gNwN2vwV4Hqjq236z1jq/77j5Sqmb6E30QoHPgR/Z4kWJ43t1azFuSnHZgolGh2ITU8f3JngFFU2jWrhirHxdUI3WOMz6u4Hc3BR/uCCVHzy9mauf38baGxfarAWAo8osrmd9fgV3nzaVhDD/E+8wSiaHB/DerYv55Ts5/HH9HgprmnnoB2kn3lEIIYSwkZK6Vj7Nr+DT/AoyiusZTBH08SZvlieFc/qMSBZNDrPb3sD2ZDAjeP9Fa33fgH8/yn+KqhztuXXA+cfZ/jrw+lBjEMPX1tnDW9tLOC1lvNOMrvh7ezAhxI89Fc4x+PtVQRXhgd5MjzpyiapjmBZl4tmr0vnRC9u59qXtvHLtAny9XPfD+IVNhQT6eHDNonijQ8Hf24O/Xjqb8SYfnttYyI8WJzDNQX/PhBBCOI6a5g5ueiWTjOJ6oPda4Y5ViSyeEoavpztuSuHhrnBT0NTeTXVTB9XNHdQ0dbKvqolPcitYm2HG38udFckRXD5/AoumhBn8quzXkBM84dg+zC7D0tbFVQvjjQ7FpqZGBrKn4siirY6nq8fKhr3VnDEjEjcHLn6zeEoYf754Fj95Ywc/eX0Hz1w51yHaPdhauaWNT/Iq+PHiePy97ePjVinF7SsTeXPbIZ755gCPXTLb6JCEEEI4MUtrF1c+t43CmmbuOSOZM2ZEMSF0aEtqOrp72HKglk/zK/lsVwUf5ZRz3qxofnvWdMIDHadewVhxvSsuF6a15qUtRSSND2BBQojR4djUtMhACmtaaO9y7FLwmcX1NLV3O+T0zCOdNTOK35+bwhd7qrhnXa5TNqM/kVe/K0ZrbXc3VIL8PLl0/gQ+zCmnpK71xDsIIYQQw9DS0c01L27jQFUzz16Zzo3LJw85uYPequkrpkbw4JpUNv5yJbevSuST3ApWPfI1r20tlurdR5AEz4XsONRAflkjVy2Md7riClMjTVg17K9qNjqUEflqTxWe7orFTjLt4MqF8dy+KpG3M838/duDRoczptq7enh96yFWTxtvl8V/rl2agJuCf7jYz0UIIcTYaO/q4fqXM8gxW/jLpbNZZqPWTz6e7tx1ShKf/HQp06NN/ObdPH7wt83SCmgASfBcyCtbigj09uCC2TFGh2JzyVG9hVYcfR3eVwVVzIsPIdDHeQqT3Lk6kcVTQnlps2vdYfsgq4z61i6uWRxvdChHFRXky/mzYngro4Ta5g6jwxFCCOFEunqs/OT1HWw+UMvDP5jJ6TNsXwRvcngAb1x/Eo/8MI19lc2seWozBQ5+HWgrkuC5iOqmDj7KLefCubF2sxbIluJD/fH2cGNPueOuwzPXt7K3stkppmcOpJTiovQ4Shva+O5grdHhjAmtNc9vKiQ5MpCFk0KNDueYblw+ifYuKy9tLjI6FCGEEE6io7uH29/Yyee7q7j/vBTWzIkdtXMppbhwbixrb1qIVWt++LfNbHWRa43jkQTPRby3s5SuHs0VJzlHa4QjubspEscHUFDpuHduvtpTBcDJTpbgAZw6PZIAbw/+6SIN0LcW1rGnoolrFtn3dOgpEYGcOn08L20ppqWj2+hwhBBCOLjWzm6ueymDT/IquPfs6Vw5RmvQp0WZWHfLIsICvbny+W2sz6sYk/PaK0nwXMRHueWkRJuYEhFgdCijJjnS5NBTNDfsqyF2nC+TDOyVNlp8vdw5KzWK9XkVLpFIvLCpkGA/T853gOnQN62YjKWtize3lxgdihBCCAfW2N7FVc9tY9P+Gh66cCbXLkkY0/PHjvPjnZsWkRJt4pbXMvn7hoP0uNDSkIEkwXMBpQ1tZJU0cGZqlNGhjKrkyECqmzoccj1Rj1Xz3cFalkwJs+sRn5G4cG4srZ09Tn9XraSulc92VXLp/AkO0Yx1zoRxLEgI4R/fHqSz22p0OEIIIRxQTXMHlz77HdnmBv566RwumhdnSBzj/L147boFrEwezwMf7+asv3zrMstDBpIEzwV8klsOwFlOn+D1Nmx2xAW2+WUWmtq7WTjZftdrjdS8+HFMCPHjHSefpvnKd8UopbjSgaZD37RiMuWWdv64fo9LFcIRQggxcpa2Li5+ZgsHqpv5+1XpnDXT2OtNPy8P/n7VXJ68bA5N7d1c8ux33Pr6DkpdqMqmJHgu4KPccqZHmYh3wql/A02N7K2kudsBE7zNB3rvLjlzgqeUYs2cGLYcrHXaD9nObiv/zDSzeloE0cG+RoczaCuSwrnypIk8t7GQu9ZmyUieEEKIQXvgo10U1bbywjXzWTHVPuoIKKU4a2YUn9+1nJ+uTuSL3ZWseuRrdhyqNzq0MSEJnpMra2hj56EGw++mjIXwQG9C/b0oqHC8SpqbD9SSGBFARKCP0aGMqgvnxKI1vOuko3if766krqWTS+ZPMDqUIVFK8fvzUrj7tKm8l1XGNS9so7G9y+iwhBBC2Llv91WzNsPMDcsm2eVNal8vd366OokvfrYCbw93XtxUZHRIY0ISPCf3Sd96J2dff9cvOSrQ4QqtdHZb2V5YxyI7/GC0tbgQP+YnhPDOjlK0dr6pgG9uLyEqyIdlibZp5jqWlFLcevIUHr0ojW2FdVz0ty1UWNqNDksIIYSdauno5lfv5DIpzJ87ViUaHc5xxQT7cm5aNJ/mV7jEDUxJ8Jzcx7nlTIsykeDk0zP7TR1vYm9lk0NVTco2N9DW1cPCyWFGhzImfjAnlsKaFnaWNBgdik2Z61v5dl81P0yPw93NcQvlrJkTyws/moe5vo1Lnt1CV49M1xRCCPF9D39aQJmljT/+YKZDFBVbMyeGjm4rH+eUGx3KqJMEz4mVW9rILK7nrNRIo0MZM8lRgbR3WTlU12p0KIO2eX8tSsFJk0KMDmVMnJEaiY+nG+9kOtc0zX/2vZ4fzh29hq5jZWliOI9clEZRbW9FUCGEEGKgjKI6XtpSxFUnTWRevGNcv8yKC2ZSuL/TF3sDSfCc2ie5rjU9E3pbJQDsKXecdXibD9SQEm0i2M/L6FDGRKCPJ6enRPJhdhntXT1Gh2MTPVbN2xlmlkwJIy7Ez+hwbGL1tPHEBPvy8pYio0MRQghhR9q7evjFOzlEB/nyi9OTjQ5n0JRSXDgnlu1F9RTXthgdzqiSBM+JfZxbTnJkIJPCnbe5+ZESIwJRCodZh9fW2cPOQw0scpHpmf3OmhlNY3s3OWaL0aHYxMb9NZQ2tHGxQX1/RoO7m+KKkyby3cE69lY6xvtJCCHE6Hvqq/0crG7hwTWp+Ht7GB3OkKyZE4NSsG5HqdGhjCpJ8JxUhaWdjOJ6lxq9g95qSQmh/uxxkEqamcX1dPZY7bLy1GhKiwsCILfUORK8tdtLGOfnySnTxxsdik1dlB6Ll7sbr2wpNjoUIYQQdqClo5sXNhVxVmoUy5Icr6BYVJAviyeHsW6n2an7vkqC56Q+yetdQOpqCR709sNzlGbnmw/U4OGmHGb+uq1EBPoQafIh1+z4hVZqmzv4964K1syJxdvD/heZD0VogDdnz4xi3Q4zzR3dRocjhBDCYOt2mGnq6ObapQlGhzJsF86NoaSuje1FdUaHMmokwXNSn+RWMHV8IFMiXGd6Zr/kSBPFda20dtr/BenmA7WkxQUT4GBTHGwhNTaIHCcYwXt3ZyldPdqppmcOdOXCibR09jht70IhhBCDo7Xmxc1FpMUGMTsu2Ohwhu20lEj8vdydutiKJHhOqLG9i4ziOk5Nca7pYoM1NTIQrWFvZbPRoRxXY3sXOeYGFrvY9Mx+qTFBFNa00OTA/Wi01ry1vYTZE4JJGh9odDijYlZcMKkxQby8pdgpexcKIYQYnI37azhQ3cI1i+NRynHbAfl5eXBGahQf51bQ1ukcxd6ONKQETymVqJRqV0q9OuB7lymlipVSLUqp95RSIQO2hSil3u3bVqyUuuyI4x1zXzF8mUX1WDUsnOSaicO0KMeopLm9sK735+RiBVb6pcYGoTXkl9n3z+l4cswW9lU1c3G6c47eQW/VsSsXTmRfVTPfHXTe6SxCCCGO78VNRYQFeDvF8p8L58TS3NHNp/kVRocyKoY6gvcksL3/C6VUCvAMcCUwHmgFnjri+Z192y4Hnu7bZzD7imH6rrAWT3fF7AnjjA7FEHHj/PD3cme3nSd4mw/U4u3hxuwJjjvNYSRSY3oLreQ58DTNL3ZX4qbg9BnO3Wvy3LRoglyM1UEAACAASURBVP08eeW7IqNDEUIIYYCimha+LKji8gUTnGK9+YKEEGKCfZ12muagEzyl1CVAA/DFgG9fDnyotd6gtW4G7gXWKKUClVL+wIXAvVrrZq31RuADehO64+478pfl2rYerCMtNhhfL8d/Aw6Hm5tiWpSJXQ6Q4KXHj8PH0zV/TmEB3kQH+Th0q4SvCqqZM2Gc0/cw9PF056L0OD7Nr6TC0m50OEIIIcbYy1uK8XBTXL5ggtGh2ISbm+LM1Ei+O1hLZ7fV6HBsblAJnlLKBPweuOuITSlAdv8XWusD9I7YJfU9urXWewc8P7tvnxPte+T5b1BKZSilMqqrqwcTsstq6egmt9TCgkmuPdt1erSJ3eVNdlsCt76lk93ljS7X/+5IM2KCHHYEr6qxndxSCycnRxgdypi4YsFEeqyadTud826nEEKIo2vu6ObtjBLOSo0iwuRjdDg2MyMmiK4ezYFq+67ZMByDHcG7H3hOa33kX/YA4MirMwsQ2LftyCGU/m0n2ve/aK2f1Vqna63Tw8Mdr+fGWMosrqfHqlmQ4Jrr7/pNjzLR3NFNSX2r0aEcVX/1SFedntlvZmwQB2taaHTAQitfF/TebDp5qmskeBNC/ZgSEcCOYsdvbSGEEGLw+lsjXLPYcVsjHE1KtAmAXQ5cC+BYTpjgKaVmAauBPx9lczNgOuJ7JqDpBNtOtK8Ypq2Ftbi7KeZOdM31d/2m2/mbtn/UKiU6yOBIjDWjbx1efql9/pyO58s9VUQF+Rwu6uMKZsYEkeMEvQuFEEIMjtXa2xphVlwwsxy4NcLRJIQF4OPpZvdLeoZjMCN4K4B44JBSqgL4OXChUmoHkA+k9T9RKTUJ8Ab29j08lFKJA46V1rcPJ9hXDNPWg3WkxgTh74J91QZKGh+Iu5uy2zftrrJGJob6EeTraXQohuovtJJb6lhJQ2e3lY37a1gxNcKhS0UPVWpsEFVNHVQ2yjo8IYRwBZsO1HCwuoVrFsUbHYrNubsppkaa7HYwYCQGk+A9C0wGZvU9/gZ8BJwGvAaco5Ra2ldU5ffAOq11k9a6BVgH/F4p5a+UWgycB7zSd9xj7mvD1+dS2jp7yDY3uPz6O+gtCjE53N9u37R5ZRZmuPjoHUBogDcxwb7kOtgIXkZRHc0d3ax0kfV3/WbG9t69deTCOEIIIQbvze0ljPPz5IxU56wWnRLdW5TP2fq8njDB01q3aq0r+h/0Tq1s11pXa63zgZvoTdaq6F0/d8uA3W8BfPu2vQHc3LcPg9hXDNGOQ/V09WhOcvH1d/2m22klTUtbF8W1rYenkbq61Jggch1s2t+Xe6rwcndjkYs1qZ8eZcLdTck0TSGEcAH1LZ18ll/J+bNjnKI1wtFMjzJhaeuizMkqRA95Hp/W+r4jvn4deP0Yz60Dzj/OsY65rxi6rQdrcVOQHu/a6+/6TY828V5WGXUtnYT4208Z+/5Rxf71Z64uNTaI9fkVWNq6HGbK6pcFVSyYFOJyU6F9vdxJjAiQETwhhHAB7+4spbPHysXz4owOZdQMrNkQE+xrcDS2M9RG58KOfVdYR0p0EIE+jnGRPNqmR/UmUPbW8Dy/rL/AiozgwX/W4eU7SLuE4toWDla3uNz0zH4zY4PILbU43XQWIYQQ/6G15q3tJaTFBpEc6bzXK8mRgShlv0X5hksSPCfR3tVDVkkDCxJk/V2//uqG9vamzSu1EBXkQ1iAt9Gh2IX/FFpxjATvqz1VgOu0RzjSzNhg6lo6KW1oMzoUIYQQoyTbbKGgsomL5zlHY/Nj8fPyICHMn13ljnENMliS4DmJrJIGOrutLJjkWmuCjic0wJvxJm+7G8HLK2t0+fYIA43z9yJ2nO/h3oD27suCaiaF+RMf5m90KIaYGdv7uyvTNIUQwnm9tb0EX093zkmLMjqUUWevNRtGQhI8J7H1YB1Kwfx4GcEbyN7etK2d3RyobmZGjPNOdxiOmbFB5DpAwtDa2c13B2s52UWnZwJMjQzE011JgieEEE6qtbObD7PLODM1yiWW/UyPNlFS14alrcvoUGxGEjwnsbWwluRIE0F+zv9GHIrp0Sb2VzXT3tVjdChA73pAraXB+ZFmxARxqK4VS6t9f7hu3l9LZ7fVZdffAXh7uDMtyuRwvQuFEEIMzkc55TR3dDt1cZWBpkf13nTfY0cDAiMlCZ4T6Oy2suNQvay/O4rpUUF0WzX7q5qNDgWAvNL+CpoygjfQzJje/mr2vg7vy4Iq/L3cmefiI+WpMUHkmC1YrVJoRQghnM3ajBImhfkzz0Wqsh+upOlECZ5r1fh2UjnmBtq7rJwk6+++Z2D5W3toS5BfZiHU34tIk4/RodiV/oQ3t9TCksQwg6M5tu8O1LJwchheHq59b2xmbBCvbT1EcV0rCS6wFnFXWSPv7DDzdUEV400+JI0PZGpkIEnjA5keZcLXyzn7QwnhKrTW7CpvZMPeGjbsrcbb040/XJBKtBOVzR+sA9XNbC+q51dnJKOUMjqcMRER2Fv4zt6K8o2EJHhOYGthHQDzZQTveyaG+OHn5W43d2XyShtJiQlymQ/NwQr282JCiJ9dT/uztHZxsKaFC+fGGh2K4WbG9o645pgbnDbBq2/p5J0dZt7ZUcru8kY83RWLJofR0NbF2owSWjt7p31HBHrz7FXpzIoLNjhiIcRQNXd088BHu/h8dxXVTR1Ab9n8krpWzvnrRp64bA4LJ7vWzfO120twd1OsmRNjdChjanq0fdVsGClJ8JxAjrmB+FA/u2rmbS/c3BTTokx2cVemo7uHvZVNrJgabnQodik1JogcO07w+mOTC3lIjAjA28ONXLOF82Y530VAbXMH5z+1iZK6NtJig/j9eSmcMzOacX2fsVarprShjfwyCw98vJuLntnC/1uTypo5kvwL4SisVs3P1mbx2a5KzpoZzbLEMJYlhTPe5MP+qiZueCWTK57byq/PnMaPF8e7xI1ZrTX/yilnRVI4EYGuNdNoepSJ5zcW0tltdYpZOpLgOYFcs4V0F18TdDzTo0y8u7MUq1Xj5mbcB/Teima6rdouporao9TYID7KLae+pfPwhbQ9yS7pTfBSY+Xn5+HuRkq0ySkraXZ093DjK5lUNXbw1g0nHbX1jJubIi7Ej7gQP+YnhHLLa5nctTabgoomfnF6Mu4Gfs4IIQbnL1/u49P8Su49ezrXLkn4r21TIgJ5/9bF3LU2m/v/tYsccwP/c06K099IL6xpobShjZtXTDY6lDE3PdpEZ4+VA9XNTIty/DoJjp+iurjqpg7KLO2He1OJ75sWZaK5oxtzvbGNmfPKei+GZ0gFzaOa2Zf49v9/sjdZJRYmh/tjcoGS0YMxMzaYvDILPU5UaEVrzT3rcskorueRi9IG1Vc0xN+LV65dwJUnTeSZDQe59qXtNLbbdzVYIVzd+rwKHvt8HxfOieXHi+OP+pxAH0+euWIuPzsliQ+yy0j/v8/44d8288w3BzhQbR+F22xt0/4aAJZMsd+18KOlv5KmPcz4sgVJ8Bxc/5qlVBkVOqb/VEcyNnHIK7UQ6ONBXIjrLdoejJQY+22grbUmq6SBNJmeedjM2CBaO3s46EQXOk9/c4B1O0q5c3USZ8+MHvR+nu5u3H/+DB64YAYb99VwzfPbaO7oHsVIhRDDtaeikbvWZpEWF8wDF8w47tRLNzfFbasS+fj2pfxkZSItHT08+MkeVj3yDec9ucnpbuZ8u6+G2HG+TAz1MzqUMZcQ5o+Pp5vTrMOTBM/B5ZgtKPWfi2PxfVPHB+KmYFd5k6Fx5JU1khJtcol5/MMR5OtJfKgfeXbYKqHc0k5Nc4esvxugf9ZAth0m5MOxPq+ch9YXcG5aNLevmjKsY1y+YCJPXDabbLOFa1/cTlunffTfFEL0qm/p5PqXMwjw9uDZK+fi4zm4CrjTokzcdUoSH9+xlE2/Wsm9Z08nr9TCb9/NQ2vnmMXQ3WNly4FaliaGueR1irubIjnSPmo22IIkeA4u12xhSngAAd6ynPJYfL3cmRQeYOibtqvHyu7yRpmeeQIz+vqr2Zv+9XdpsZLg9UsIC8Dfy51cs/0WxhmsA9XN3PlWNrPignnoBzNHdHFz+owo/nzxLLYX1XHDKxm0d0mSJ4Q96OqxcuvrO6i0dPDMlXMZP8x2RTHBvly7JIE7VyfyQXYZ7+wotXGkxsgptdDU0c1iF5ye2a+/kqYzJO2S4DkwrTU5pRYp+jAI06NM5JVaDHvTHqhuprPbKgVWTmBmbBClDW3UtXQaHcp/yTI34OXuRnJUoNGh2A13N9WbkNvhiOtQvb71EN1WK88M4Y7+8ZybFs1DP0jj23013PxqJp3dVhtEKYQYLq01976Xx+YDtTy4JpXZE0bewPvmFVM4aVIIv3s/zymmqm/cV4NSsGiyCyd4USYsbV2UWdqNDmXEJMFzYJWNHVQ3dRwuTiGObV5CCBWN7RTWtBhy/rzS3tHD/obe4uj6E+BcO0sasksamBYViLeHNLQeaGZsELvKGunqcdwEpseq+SC7jJOnRgz7jv7R/GBuLA9cMIOvCqq55bVMp1urI4Qj+fu3B3lzewm3rZxis16m7m6Kxy6ejZeHG7e9sZOObscerd+4r4aUaJPTVwo9nsM1G5xgmqYkeA4s29xftl2mjZ3I8sTe3nMb9lYbcv68Ugu+nu4khAUYcn5HcTjBs6Npfz1WTV5poxRYOYqZscF0dFspqDB2fetIbD5QQ3VTBxfMtn0/v8sXTOT356Xw5Z4qznz8WzKL62x+DiHE8X2aX8GDn+zhrJlR3Lk6yabHjgzy4eEfpJFf1shD6wtseuyx1NLRzY5D9SyZ4tp9epMje2s22NM1yHDJwi0Hlmu24O6mSImWUaETmRDqR3yoHxv21XDN4oQT72BjeaUWpkebpD/WCZh8PEkI87erEbyD1c00d3TL+ruj6C86s7OkwWGnH7+7s5RAHw9OTo4YleNftTCelGgTd7yZxUXPfMdtK6fwk5On4OF+9PurJXWtvJ1p5t2dZprbuxlv8iEyyIdIkw+TwwO4atFEGUkWYpDySi389M0s0mKDeeSHaaPSC/eU6eO5euFEnttYyLKkcJYnOV6StLWwlm6rZmmi607PBPDz8iA50kTmoXqjQxkxSfAcWE6phaTxgTZZM+IKliaG889MMx3dPWN6gdTdYyWvzMKl8yeM2TkdWWpMEBlF9jPSkdVfYEVG8L4ndpwvYQFeZB1q4MqTJhodzpC1dfbwaV4FZ8+MHtXP0bkTQ/j4jqX87r08Hvt8Hxv31fDD9Fh8vTzw9XTH19Od6uZ2/plpZtP+WpTq7UM1IcSPysZ2KhrbyStt5M3tJeSUWnj84lmjcqEqhDMprm3h2pe2E+Lvxd+vSh/V9/g9Z07j0/xK3tx2yCETvG/31eDt4cbciSNfm+jo0uPH8c9MM9091mPeiHMEkuA5KK01ueYGTp0eaXQoDmNZUjivfFdMZlE9i8awStTeymbau6xSYn+QUmOC+CC7jJrmDsICvI0Oh2xzA4HeHkwK8zc6FLujlGJWXDBZJY55t/Oz3ZW0dPZw/ihMzzySyceTxy6ZzYqpEdz7Xh6/fCf3e8+JC/HlZ6ckceHcWKKDv98v8+mvD/DH9XuIDvbhnjOmjXrMQjiqnYfque6lDHq05uUbFhAeOLp/S3w83Vk0OZQN+2rQWjtcm4FN+2uYnxAiAwbA3InjeHlLMXsqmhx2ZgoMMsFTSr0KrAL8gQrgIa31P5RS8UAhMLByxR+11vf37ecNPA38AGjt2+/RAcddBTwJTAC2AtdorYtH+Jpcgrm+jfrWLqmgOQQLJ4fi4ab4Zl/1mCZ4OX1zuWfKFL9B6f+dzi21cPLU0Zk2NxTZJRZmxgXJiMkxzIoL5vPdVVjaugjy9TQ6nCF5b2cpUUE+LEgIGbNznj87htNSIqlr7aSts4f2rh5aO3vw8nBjZszxf89uWj6J0oZWnvnmIDHBvly1MH7M4hbCUXyaX8Edb+4kItCHF380j0nhY7P2PT0+hHU7SymqbSXBgW4IVja2s7eymTVzbFN8xtGlx/f+PcgsrnfoBG+wY48PAvFaaxNwLvB/Sqm5A7YHa60D+h73D/j+fUAiMBE4GfiFUup0AKVUGLAOuBcIATKAt0byYlxJf6+wmZLgDVqAtwdzJ45jw96aMT1vtrkBk48H8aF+Y3peR9W/pjTPDvrhtXf1sLu8UdbfHcesuN4pPTkOtii9trmDb/ZWc+6s6DFP3n293IkJ9mVKRAAzYoKYnxDCrLjgE8ahlOK+c1JYlRzBfR/k89muyjGKWAjH8MKmQm56NZPkSBPrblk0ZskdwPyE3s/C7YX2s8RgMDbt770mWuLC/e8Giu5b85xR7JgzU/oNKsHTWudrrTv6v+x7TB7ErlcD92ut67XWu4G/A9f0bVsD5Gut39Zat9ObDKYppZKHEL/Lyint7cs1NVL6cg3FsqRwdpc3UtU0dj1OsksspMUFO9yUDaME+ngyKdzfLvqr7SpvpNuqZfT1OGbGBaEUZB1yrATvo9xyeqx6VKpnjiYPdzf+etlsUmOCuO2NHex0gmIAQoyE1arJKKrjrrey+N8Pd3HKtPG8cf1JYz7Ff3J4ACH+XmyzozXkg7FxXw0h/l5Mj5KCfdB7I21u/DgyHezneKRBrx5USj2llGoF9gDlwMcDNhcrpcxKqRf6RuZQSo0DooDsAc/LBlL6/p0ycJvWugU4MGD7wHPfoJTKUEplVFcbU+be3uSaLSRLX64h61/8/O0YjeK1dfZQUNkkI0BDlBoTRJ4dJHjZfQVWZP3ksZl8PJkcHnC4GI2jeHdnKcmRgSRHOt5FjZ+XB/+4eh4RgT5c8Y+tfC4jecLFWK2aXLOFP3y8myV//JIf/G0LH+WWc+OySTx9xVx8vcb+2kgpRfrEcXZVJOxEtNZs3F/DosmhsgxhgPSJ4yiztFPW0GZ0KMM26ARPa30LEAgspXdqZQdQA8yjdwrm3L7tr/Xt0j8uPvAqzdL3nP7tR17BDdw+8NzPaq3Ttdbp4eGOV53I1qxWTW6phVQHnhtslOlRJkL9vdiwb2xuFOwqt9Bj1TKVdohSY4Iot7RT3dRx4iePouySBsabvIkMsl0DbGfUW2ilAa210aEMSlFNCzsPNYxJcZXREh7ozdobF5IQ7s/1r2TwzDcHHOb/vxBD1drZzeb9Nfz1i3386IVtzL7/M855YiMvbCpkWpSJxy6eRea9p3DPmdMMbUc0PyGEotrWMZ0lNBL7qpqpaupw+fYIR0qf2LsOz5GnaQ6piqbWugfYqJS6ArhZa/0XetfOAVQqpX4ClCulAoHmvu+bgPYB/+7viNvc9/VAA7eLYyiqbaGpvVuShmFwc1MsTQzj2301WK161O9YZZX03sOQEvtD0z8lMq/UMmr9yQYjx2yR0ddBmBUXzD8zzZjr24gLsf+1pu9nlaEUnJsWbXQoIxIZ5MPbNy7iZ29n8eAne9hX1cwDF8yQmR3CaRyqbeX5TYWszSihtbMHgKTxAZwxI5L0+BBWT4sg2M/L4Cj/o79Ax/bCes6aGWVwNCf23cFaABZNlgRvoGlRgfh6upNZVOewfyeG2ybBg6Ovweu/feimta5XSpUDacBnfd9PA/L7/p1P7xo9AJRS/n3H7N8ujqG/CbSsCxqeZUnhvJdVxq7yxlGvkJRjbiDS5MN4k4wADUVKtAmlehMsoxI8S2sXB2tauHCuVBY7kYENzx0hwfsgu5QFCSFHbUXgaHy93Hni0jk8FrGPv3yxj+LaFp67Zh4mH8eqaCrEQDsO1fOPbw+yPq8CdzfFOWnRnJsWzey4cQT52e/vdkq0CV9Pd7YX1TlEgpdV0kBYgDex4xz/s9CWPNzdmBUX7NAjeCecoqmUilBKXaKUClBKuSulTgMuBb5QSi1QSk1VSrkppUKBvwBfa637p16+DPxWKTWur3jK9cCLfdveBWYopS5USvkAvwNytNZ7bPwanU6O2YK3hxuJEWNXHcqZLOmbivDN3tGfppld0iAjrcPg7+3B5PCAwzczjJBTKuvvBis5MhAfTzeHKLRyqLaVA9UtnJbiPD1E3dwUd52SxF8vnU1GcT1PfLnf6JCEGJaWjm5uf2Mna57azMZ9Ndy4fDIbf7mSRy+axYqpEXad3AF4ursxZ2Iw2x1kHV52SQOz4oKkCNxRpMePY3d5Iy0d3UaHMiyDWYOngZsBM1AP/An4qdb6A2ASsJ7eaZV59K7Lu3TAvv9Db+GUYuAb4GGt9XoArXU1cCHwQN9xFwCXjPwlOb9cs4WUaBMe7oNeQikGiAj0YVqUiQ2jnOBZWrsoqm2V6ZnDlBoTRG6pcQlDfysSR+6DM1Y83N1IjQlyiIbn3/Stv+0vuORMzkmL5oLZMby0uYgKi2OsARKi377KJs59YiP/yinjztVJbLlnFb88PdnhZsDMiw9hd3kjTe1dRodyXI3tXRyobpFlCMcwd+I4rBqHKyDW74QZgta6Wmu9XGsdrLU2aa1TtdZ/79v2htY6QWvtr7WO0lpfpbWuGLBvh9b6x337jR/Y5Lxv++da62Stta/WeoXWusjmr9DJ9Fg1eWUWmZ45QsuSwsgsrqd5FO/M9I8AyYfn8KTGBFHZ2EFVozEXqjnmBhLC/B2uebdRZsUFk1fWSGe31ehQjuubgmriQnwdqhHxUNy5Ogmr1jz+xT6jQxFi0N7PKuXcJzZhaevi1esWcMfqRPy9h7uKyFjz4kOw6t5G2fYs1yw1Ao5nzsRxKAUZRfb9czwWGQJyMAeqm2nt7JEKmiO0PDGcbqtmy4HaUTtHf4n9VJmiOSxpcb3/37Yb9OGaa5ZKtUMxK24cnd1W9lQ0Gh3KMXV2W9l8oIblSeFOOyUpLsSPy+ZPYG1GCQerm0+8gxAG0lpz3wf53PFmFqkxQXx0+1KHL/gxe0IwHm7K7qdp9o9MyU3oozP5eDJ1fCAZxfb9czwWSfAcTM7hOy5y4TkSc+PHMc7Pk1e+Kx61c2SbLUySEaBhmxU3jlB/L9bnV5z4yTZW3dRBmaVd1k8OwawJvRcJ9jydJaO4jtbOHpYnGVeZdSz8ZGUi3h5uPPrZXqNDEeK4vtxTxYubi7h64UReu36Bw03HPBo/Lw9SYoLYXmjfIz/ZJQ291yh2vq7RSHMmjmPnoQZ6rI7XgkYSPAeTa27A38udhDApsDIS3h7u3LR8Mhv2VrP14OiM4mWXNMjUhxFwd1OcMn08X+6upL2rZ0zP3d9kXUbwBi86yIewAG+7LrTyzd5qPN0VCyeHGh3KqAoP9ObHixP4V0754d9lIeyN1ap55N97mRjqx2/Pno6nE9UVmB8/jixzAx3dY/u3a7C01mTJNcoJpU8cR3NHNwUVjtfBzXneTS4ip9RCSkyQoY08ncVVC+OJCPTmT/8usHmD4ApLO1VNHTICNEKnz4ikpbOHTftrxvS8OWYLSkGKJHiDppQ63PDcXn1TUE36xBACHHRtz1Bcv2wSQb6ePPxpgdGhuIz9VU384ePd3LU2ix+9sI3zntjIioe/4sGPd9vthb6R1udXsKu8kTtWJTpVcge9/fA6u62H17nZm4rG3muUNLlGOa7+hueZh+x7NPZonOsd5eS6eqzsKmtkplx02oSvlzu3rZzC9qJ6m7dMODy3Xe6OjciiyWEE+njwSd7YTtPMLW1gcniASyQCtjR7QjAHa1qwtNpf9bjKxnb2VDSxfKrzVc88miBfT25ZMZlvRnGWgviPzOI61jy1mRc3FbH1YB3VzR2YfD2ZGOrPMxsOcsGTm9lf5XijAKOlx6r582d7mRzuz3mzYowOx+bm9TU832an6/Cy5RplUOJCfAkP9CbTTn+OxyNXLw5kb2UTHd1WZsob0mYunjeBZzYc5E//LrBp4YUccwMeborpUSabHM9VeXm4sXraeD7bVUlXj3XM7vLmmC0smeLYC/2N0N8zMMvcYHdtCPpv4thbXKPp6kXxPL+pkEc+28vaGxcaHc6oa2rv4pu91d9bLzMrLpiJoaNXNXXD3mpufCWT8SZvXr1uAbHj/P5r+xe7K7n7nzmc9ZeN/Pbs6VyxYILTFvkZrA+zy9hX1cwTl812yhlJIf5eTIkIYHthHawwOprvyyqx4OmumCbXKMellCJ94jiHbHguCZ4D6R/qlxE82/HycOOnq5P4+dvZrM+r4IzUKJscN9vcQHJUID6e7jY5nis7fUYk7+4sZevBusNN6kdTZd/UFal+OnQzY4NQCrIO2WeCFxHoTXJkoNGhjBkfT3euWhjPw58WYK5v/V7i4Uw2H6jh7rdzKG1o+942DzfF1YviuX1los0LSnycW84db+5kSkQgL/94PuGB3t97zqpp41n/06X8/O0c7n0vj09yy0mLCyY8wJuwQG/CA7yZGRvksG0Bhqq7x8pjn+8lOTKQM2fY5m+uPZoXH8K/csqwWjVudpbEZpc0MC3KJNcogzB34ji+2FNFfUsn4/y9jA5n0Fzj08RJ5JRaCPTxYGKo8/6RNsIFs2N4+uv9PPLZXk5NiRzx3USrVZNjtnBOWrSNInRty5PC8fV055O88jFJ8Por1cr6yaEL9PEkOdLEdwdruYNEo8M5rLvHysZ9NZw6fbzLjZycPTOKhz8t4JPcCq5fNsnocGyuvauHh9YX8PymQiaF+fPadQuICvpPJcbOHisvbS7i+U2FrNth5s5Tkrhs/gQ8bDAb4K3th7hnXS6zJ4zj+WvmHbdickSgDy9eM48XNhfx/MZCthfV0dXzn5HGiaF+PHf1PKZEOH8BtXU7SimqbeXvV6XbXeJjS/Pix/HGtkMUVDbZ1UhZj1WTW2rhgtnONzV2NFw6fwJXLpyIt4djJcOyBs+B5Jgb+u6QO+8HohHc3RQ/O3Uq+6uaeW9n6YiPd7Cmhab2bmZJbxmb8PF0IqjSTgAAIABJREFU5+TkcD7NrxyTUsW55gbcFEyPkgRvOFZMDWd7UR1N7fazDi/bbMHS1uUy6+8Gmhjqz4wYE//KLTc6FJvLMTdw1l++5flNhVyzKJ6Pbl/K4ilhTAoPOPxIjjTx4JqZfHTbUpIjTfzu/XzOePzbEVcX/fuGg/zynVyWJIbzyrXzB9UOx81Nce2SBDb9aiV7/+8Msn53Cp/duYy/XTGHlo5u1jy1acwLSo21zm4rj3+xj7TYIFZPc+52Jf3r8OytH97B6maaO7pl/d0g+Xt7OFxyB5LgOYz2rh4KKppIjZE35Gg4PSWSlGgTf/58L22dI6t29vrWQ7i7KRZNce5S7GPp9BlR1DR3sGMMKlnllFpIGh+Ir5fjfaDbg5OnRtBt1WzcZz8Xqt/srcZN4bLrKs9KjSa7pIGSulajQ7GZpvYuLv/7Vlo7e3j12gXcd27Kcd+z06NNvH79Ap69ci7NHd2seWozr35XPOQKylprHv50Dw98vJuzUqP4x1Xp+HkNfTKUUopgPy8Sxwdy+owo3r1lMZFBPlz1/DZe33poyMdzFGszSihtaOOuU6c6/c3q2HG+RAX5sK3QvhK8/iJws6SfslOTBM9BFFQ00dWjpaTtKHFzU/z2rOmY69v4v492Dfs4VU3tvLa1mAtmxzj1epextjI5Ai93Nz7JHd1qmlprcs0W6X83AnMmBGPy8eCrgiqjQznsm73VzIoLJtjPcdZP2NJZfWuLP8lznlG8D7LLaOro5ukr5g566rZSilNTIvno9qUsnBzKb9/L4/Y3s2ju6B7U/lar5nfv5/PkVwe4ZF4cf7l0Nl4etrmMigvx452bF7FkShi/fjeX//vXLpu377EHb2eUkBJtYtkYTLc3mlKKefEhbC+qs6ufZba5gQBvDyZJP2WnJgmeg8jpb7wsCd6oWTg5lBuWTeK1rYf4bFflsI7xj28L6eqxcuvJU2wcnWsL8PZgaWIYn+ZXjOofyjJLO7UtnbL+bgQ83N1YlhTOVwXVWMdgSu2J1LV0kmNuYHmSc08HO54JoX6kxgTxUY7zJHhvbishOTJwWDc9Q/y9eOGaedx92lQ+yinj3L9uJLP4+BfhXT1W7lybxSvfFXPjskk8uCbV5tUfA308ee7qdK48aSL/2FjIp/lj2x5mtJVb2sg2WzgzNcrpR+/6zYsfR2VjB+b67xf/MUp2iYWZsUFOvf5RSILnMHLNDYT4exET7Gt0KE7tZ6cmMT3KxC/fyaGqsf3/s3ff8VGWWf/HP1d6gQBJIJQEEnov0kFEFLFjxbrWta1tq9t+66777LN93cdV1y723jtipUgJIBCq1EBCDS0JIT3X748rUUBKyszck5nv+/XiFc3Mfd8Hx0zm3Ne5zmnQsbv2l/Pc3E2cN7gTWan+a8kdrs7o354t+0pZ1sS9M8eyLN+VrgzU/skmmdCrHQXF5azcVuR1KMxaW4C1hOX+u4OdPbADS/MLQ6JMc/mWQpZtKeTyEY0fNxARYbhtQndeunEU+8uruOjhuUz415fcO/0b1uxw8+oKSyv5cNk2fv1GDuP/8QXvLNnKL8/oxW/O6uO3BCUqMoJ7JvcjvU08U2fn+uUaXpm+wt04Pb1fe48jCZzhWbXz8IKkTLOssppV24q0/y4MKMHzEX8vv+fUlo2Fy10vr8RGRXL/5UM4UFHFz19b2qAViCdmbaSsqprbT9HqnT+c1jeNyAjj16HnOfluNlDvDuHTSt8fxvdqizHw+WrvyzSnr9hBaouYsC+7rSvT/DAEmq28vGAzsVERnO+DAdkju6bw6c/H8/eLBpDeJoH/frGOSf83k7F/+5wh/zOdW1/4mg9ytjEgvRWP/GAot57s//f3yAjDtWMyyc7d8+14pFAwbfl2urVNDItOoXV6tmtJq/jooGm0snJbEVU1lkG6iRnylOD5wPQV2znt/2bWu46/oUorqlmzo1j77wKke7sW3H1OX2at3cXUrzbW65g9JRU8OzeXcwd2pFvb8PnlFUitE2IY0y2FdxZvobyqaY1wjmbZlkJ6tW/ZLDtmBZPUFrEMTG/t+T68sspqvvhmp0/GnzR3GckJDExvxQfNPMErrajmncVbOXtAB5/NtEuKi+bS4Z15/oaRzP/tRP7nvH7065jEbRO689oto/n696fx6FXDOKN/4FaeLhmeQWJMJE/V83dQsNtbUkF27p6A/jcMBhERblB2sCR4S79tsKIEL9QpwfOBdklxrNu5n5ez/dP5auW2QmosDNAdl4C5YkRnTuubxj+mfcOKrce/g/rk7A2UVlZzh1bv/Oqmk7qytbCMl7PzfH5ua23tSrl+znzhlF7tWJK3j937yz2LYeaaAg5UVHNGGJWEHcvZAzqQ08zLND9Yto3i8iouHZ7hl/O3bRnL1aMzeezqYfx8Ui+GZyYT7YOZeQ2VFBfNlGEZvJeztcHbBYLRp6vcmJtwKs+sMywzmfUFJZ6+F9ZZmrePtKRY2h80K1JCkxI8Hxic0ZoRWclMne0abPiaBi8HnjGGv180kDaJ0Vz1ZDY5tXuzjmTfgQqembOJswZ0oEeaSvv86cTuqYzMSuaBz9dxoMK3K+Z5e0opLK3Uz5mPTOjdFmth5toCz2KYtnw7reKjGd1NI0sAzqot02zOq3gvZ2+ma9tERtTubQpl14zJpKrG8vy8TV6H0mQfr9hBx1ZxYVkqPSKrDQALcv0/5ud4luYXavUuTCjB85FbxruVBX90KcvJL6Rdy1jSknTHJZCSE2N4+abRJMREcvlj844416u6xvKfz9ayv7yKO0/p4UGU4cUYw12n92LX/nKemePbDz05W1wSH44fQPyhf8dWpLaI5YvV3iR4FVU1fLpqBxP7pHmyAhOMMpITGJTefLtprt1RzMJNe7lseEZY7EfPSk3k1N7teGH+Zsoq/VOWHggl5VXMXFvApH7tw+J1O9yATq2JjYrwvExz9/5yNu4qYXBGG0/jkMDQbz0fOblnO3q0a8EjM9b7vOFKTv4+dfXzSFZqIm/8aAwZyQlc93Q27+dsBaCquoY3FuVz2r9n8NRXuVw4pBO92mv1LhCGZSYzoVdbHpmxnqKySp+dd1l+ITFREfTUKqxPREQYTu7VlhlrCqjyQ2XD8czdsJuisqqw2/NzPGcP7MCyLYVs3t38yjRfXpBHdKThwhPSvQ4lYK4fm8XukgreXbrV61AabcaaAiqqasKyPBMgJiqCwRmtWehxgvf1ZncTc1imErxwoATPRyIiDDee1JXV24uZdYSVnsYqLqtkw64SlY15KC0pjlduHs3gjNbc8dJifv/Ock65dwY/f20psdGRPHzlCfxryiCvwwwrP5/Ui8LSSp6YucFn58zJL6RPhySfDS4WNy6hsLSSJXlHL3H2l2nLt5EYE8m4MBio3BBnNdOh5+VV1bz5dT6n9U0jtUWs1+EEzOhuKfRu35KpszcG1bDshvh4xXaSE2MYHsaJxfDMZJZvLaLET8346mPRpr1ERxpVqYSJen2SMcY8b4zZZowpMsasMcbccNBjpxpjVhtjDhhjvjDGdDnosVhjzNTa47YbY3522HmPemxzdN7gjqQlxfKYDz90rthahLUacO61VvHRPPfDkZzaux3Pzt1E64RoHr96GB/eeSJnDuiggaEB1r9TK84a0J4nZ2/0ycb17YVlLNq8l6Gdw/cDiD+M65lKZIQJ+LiE6hrL9BU7mNC7HXHR6oh6sPQ2CfTtkMSnq3Z4HUqDTF+xg70HKrlseGevQwkoYwzXj81i9fZi5m7Y7XU4DVZRVcPnq3YysU87osK4VHp4VjLVNZbFmwN/s6vOok176N+pld4Tw0R9f9r+CmRaa5OAycD/GmOGGmNSgTeBu4FkYCHwykHH3QP0ALoAE4BfGmPOAKjHsc1ObFQk143NYva6XSz30TDmuhk4uuPivbjoSB69ahjTfjKOd24by2l908JyP0Gw+NlpPSmtrObhL9c3+VyPzFhPTY3lurGZTQ9MvpUUF82wLm344pvA7sNbkLuH3SUVKs88iol901i0aS97Siq8DqXepq3YTruWsZzYPfxWZCcP7khyYgxPf5XrdSgNNmf9LorLq8K2PLPOCZ1bE2Eg26MyzYqqGpbmF+omZhipV4JnrV1hra27TW5r/3QDLgRWWGtfs9aW4RK6QcaY3rXPvQb4k7V2r7V2FfA4cG3tY8c7tlm6YmRnWsRG+WQVz1rLu0u30jU1MaxKUoJZZIShd/skJXZBoHu7llwwJJ1n521iW2Fpo8+zo6iMF7M3c9EJ6WQkJ/gwQgGY0Lsdq7YVBbQ1/7Tl24mNimBCr3YBu2ZzMrFPO2osfBEEg+jro7rGMnvtLk7q2TYsqyXioiM5f3AnvlxT4GmJX2N8vGIHiTGRjA3DxPxgLeOi6dsxybN9eMu3FlJRVcPQLkrwwkW918uNMQ8ZYw4Aq4FtwIdAP2Bp3XOstSXAeqCfMaYN0OHgx2v/uV/tPx/12Eb9TYJEUlw0V4zszAfLtpG/t2kfaD5avp1lWwr50cndfBSdSGj5ycQeRBj48ctLGj2i5JEZ66musdw2QTMM/eHcQR2JjjQ8OrPpK631UVNjmbZ8Oyf1bEtibFRArtnc9O/YirSk2GZTppmTv4/C0kpO6tnW61A8c1rfNCqqapi5xruxIw1VXWP5ZOUOTlapNOD24S3evM8v47SO5+tNbkSDErzwUe8Ez1p7K9ASGIcrrSwHWgCH1yIW1j6vxUH/fvhjHOfYQxhjbjLGLDTGLCwoCP43t+vGZmKABz9f1+hzVFXX8K/p39CjXYuw6hgm0hAZyQn8/aKBZG/cw/++v7LBx+8sKuPF+Zu5cEgnOqdo9c4fOrWO5+KhGby6IL9JK631tTR/H9uLyjTc/BgiIgyn9klj5poCyquCv/3+zDW7MAbGhfEq0PDMNrROiOaTlc0jKQf3s7hrfzmT+qZ5HUpQGJ6ZTGlltc+28DTEwty9ZCTH007jtsJGg3a8WmurrbWzgXTgR8B+IOmwpyUBxbWPcdjjdY9xnGMPv+5j1tph1tphbdsG/x28Dq3iuW5sJi8vyGt0a+M3vs5nQ0EJP5/Ui8gwLEkRqa/zBnfixnFZPDN3E68uyGvQsY/M2EBVjeX2U7R650+3TehGjbU+2S95PNOWbycqwjCxjz5UHsvEPu0oqahm3gZvW7fXx8y1BQzs1Io2iTFeh+KZqMgITundjs9W7/RkBagxZtUm5if1CP7PbYEwPDMZIODz8Ky1LNq8l2FdkgN6XfFWY1saReH24K0Avu0Pb4xJrPu+tXYvrpTz4P7xg2qP4VjHNjKmoPLLM3ozrEsbfvV6Dmt2fC9nPaayymru+3QtgzJac3o/fUgROZ5fndGbE7un8ru3l7N48956HbOzuIwX5m/igiGd6JKS6OcIw1t6mwSmDEvn5ew8v67i1dRYPlq+nTHdU2mVEO2364SCMd1SiY+O5NMgXxGqG7MxTkkCk/q2p7C0kgUbgz8pB5ilxPwQbVvG0jU10aejtOojb08pBcXlnKDyzLBy3ATPGNPOGHOZMaaFMSbSGHM6cDnwGfAW0N8Yc5ExJg74PZBjrV1de/izwO+MMW1qm6fcCDxd+9jxjm3WoiMj+O+VJ5AYG8Utzy2iuAEDmZ+ft4lthWX86vReauYhUg9RkRE8cPkQ0lrFcsvzi9hZXHbcYx6rW73T3ruAuPXk7tRYyyN+XMX7fPVONu85wAVDOvrtGqEiLtrNCPxs1Y6gnq82d/0uqmtsWO+/q3NSz1RioyKYHuRJOUBRWSWLlZh/zzkDOzB73S627PN/uXqdRZvdDYFhSvDCSn1W8CyuHDMf2Av8C/iJtfZda20BcBHw59rHRgKXHXTsH3CNUzYBM4B/WmunAdTj2GYvLSmO/14xhE17DnDXazn1+iVaXFbJf79Yx7geqYwJ4/0GIg3VJjGGx64aRlFpFddOXUBO/tHnDe0sKuP5+Zs4b3BHMlO1ehcIGckJXDw0nZcW5LGj6PgJeENZa3nwi3Wkt4nn3IFK8OpjYp80thaWsXJbkdehHNWMNbtoERvFkM6tvQ7FcwkxUYzrkconK4M7KQeYu363EvMjmDIsA2vh9YX5Abvmwty9tIyNomfa91pcSAg7boJnrS2w1o631ra21iZZawdYax8/6PFPrbW9rbXx1tqTrbW5Bz1Wbq29vva4NGvtvw8791GPDRUju6bwmzN7M23F9nqNTnhi1kb2HqjkrtN7BSA6kdDSp0MSD14xhB1FZUx+8Ctue/FrcneVfPv4+oL93PPuCk65dwZV1ZY7TunhYbTh57YJ3amp8c9evLnrd7Mkbx+3jO8W1gOVG2JC73YYA5+uDM5xCdZaZq4pYEy3FKL1mgKuTHPLvlJWbA3epBxceWZiTKQS88NkJCdwYvdUXl2YR01NYJL0RZv2Mrhza/VzCDN6xwyAH56YxVkD2vP3aav59ydrKKv8ftcya1074SdmbeDM/u0ZmK43RZHGOLVPGl/edTJ3ntKdz1ftZOK/Z/CbN5dx1ZPzOfXeGbwwfxOn9U3j7dvGkqXVu4DKSE7gwhM68WL2Zp+v4j34xTratYzl4qHqOlxfbVvGMjijddCOS9iwq4Qt+0q1CnSQU/u0I8IQ9N00Z67ZxehuqUrMj+CS4Rls2VfKV+v9vxevqKySb3YUazxCGNJPXgAYY/jnxYM4d1BH7v9sLWf+ZxZz1n33g71uZzFXT83mxmcX0r5VHL89q4+H0Yo0fy3jovnZpF7MuOtkLhmewasL81i7Yz8/P60nc359Kv936WD6d2rldZhh6fYJPaiusdz/2VqfnfPrzXuZs343N47rqnlbDTSxTxrLthSyvdD3ZbNNVTfzbbwSvG+ltIhlaJc2Qb0Pb9PuEjbvOcBJPbXN5Egm9U2jVXw0rzSw63NjLNm8D2tRB80wpCmwAZIYG8V/LhvCxUPT+d3by7niiflcMKQTyYkxPDMnl/iYSO4+py9Xj+6iO14iPtIuKY6/XDCAX53Rm4SYSP1sBYHOKQlcPboLT32Vy6CM1lwyLKPJ53zoi/W0TojmipGdfRBheDmtbxr//PgbPlu9gytHdvE6nEPMXFNAVmoiGcmaUXmwSX3b8+cPV5G350BQ/reZWdslUg1WjiwuOpILhnTixfmb2VtS4dcuows37SXCwGCVyoYdfdoJsHE92vLxT07ijlO6837OVqZ+tZEpw9L54hcn88MTs/QBVMQPWsVH62criPz2rD6c2D2V3765jLnrdzfpXKu3F/Hpqh1cNyaLxFjds2yoHu1akJEcH3TjEsqr3Iy+cT20CnS402oHhwdrmeasNQWkt4knMyX4ks9gcenwDCqqa3hr8Ra/XufrTXvp3T6JFnpvDDv6xOOBuOhIfj6pF5/8dDwf/+Qk/nrhQFJbxHodlohIQNSNkclMTeSW5xexoWB/o8/10BfrSYyJ5JoxwbX61FwYYzitT3u+Wr+bwgP1H+fjbwtz91JaWa0h2UeQmZpIz7QWTF+53etQvqeyuoa563czrkdbjXk6hj4dkhiY3opXF+b5rSNqVXUNizfv1f67MKUEz0PuTVpta0Uk/LSKj2bqNcOJjDBc//QC9pZUNPgcubtKeD9nKz8Y1YXWCRqm3FgXntCJiqoa3lnq39WEhpi5poDoSMPobilehxKUJvVtz4LcvY36ufGnpXn7KC6v4iStvB7XJcMyWL29mJz8Qr+c/5sdxZRUVDMsUwleOFKCJyIinuicksDjVw9la2EZNz+/iNKK73cYPpLqGsvri/K57LF5REdG8MNxWX6ONLT165hEnw5JvLrQ/00f6mvm2l0M7dJGZbdHMalfGtU1lmkrgmsVb+baXUQYGNNNCd7xTB7ckbjoCF72U7OV7I1uwPkJnZXghSMleCIi4pmhXZL558UDyd64h9Pvm8mstQXHfP5X63Zx7gOz+cVrS0lLiuXFG0fRrmVcgKINTcYYLh2WzvItRazY6p/VhIbYVljKqm1FGo9wDAM6taJHuxYB6cTYELPWFjA4ozWtEqK9DiXoJcVFc9aADry3dCsHKqp8eu6aGssL8zfTp0MS6W3ifXpuaR6U4ImIiKfOG9yJl24cRWSE4aons/nZK0vYU1t6Zq0ld1cJz8/bxFVPzufKJ+ZTWFrJ/ZcP4a1bx2p/iY+cP6QTMVERvBoECcP7S7cBcGb/Dh5HEryMMVw6PIMleftYvT04hp4XHqhkad4+dc9sgCtHdmZ/eRWPz9zo0/N+vnon63bu55bxXbUXMkyp9kFERDw3ulsKH/14HA9+vo5HZqzni292MqFXO+Zv3MOWfaUAdGgVx2/O7M01YzI1787HWifEcHq/9ry9ZCu/OauPp/993126lQGdWpGVmuhZDM3BhSek849p3/DKgjz+cG4/r8Phq/W7qLFo/l0DDO2SzDkDO/DfL9dx/pCOdEnxzf/zj85cT6fW8Zw1QDdJwpVW8EREJCjERUfyi9N78cGd4+jWtgWff7OT/p2S+NN5/fjs5+OZ8+tTuHl8NyV3fnLJsHQKSys9HaK9cVcJy7YUMnlQR89iaC6SE2OY1C+NtxZvoayyfvtX/WnW2gJaxkYxKF0z1xri7nP6EhMZwe/fWeGTjpqLNu1hQe5ebhin0VvhTCt4IiISVHq1b8nrPxrjdRhhZ2y3VDq1jufVBXmeJVjvLd2KMXDOIK081Mdlwzvzfs42Pl6xnfMGd/Isjuoayycrd3JSz7ZEKalokLSkOH56Wk/+9P5KPl6xnTOaWJr86IwNtE6I5tLhGT6KUJoj/RSKiIgIERGGKcPS+Wr9LvL2HAj49a21vLt0K8Mzk+nQSo0h6mNMtxTS28R73mxl/obd7NpfztkDlZg3xjWju9C7fUv++N5KSsob33Bl3c79fLJqB1eP6kJCjNZwwpkSPBEREQHg4qHpALy+KD/g1161rZh1O/erPLMBIiIMlw7LYM763WzaXeJZHO/lbCMhJpIJvdp5FkNzFhUZwZ8v6M+2wjLu/2xto8/zxKwNxERGcPWYTN8FJ82SEjwREREBIL1NAid2T+X1RflU1zR9P1BDvJezlcgIw5n92wf0us3dlGEZRBg8W8WrrK5h2vJtTOyTRnyM9sc21tAuyVw6LIMnZ2/km+3FDT5+Z1EZb369hSnD0kltEeuHCKU5UYInIiIi37pkWAZb9pXy1bpdAbumtZb3lm7lxO6ppOjDaYO0bxXHhF7teG1RPlXVNQG//tz1u9l7oFLlmT7wqzN70yIuih+/vJjthWUNOvapOblU1dRww4ld/RSdNCdK8ERERORbk/qlkZIYw4NfrPNJV7/6+HrzPvL3lqo8s5EuHZ5BQXE5X3xTEPBrv5+zlZaxUYzXYPomS06M4T+XDSFvzwEmPzibnPx99Tpu9tpdPDd3E2f270CmxosISvBERETkILFRkfx8Ui+yN+7hw2XbA3LN95ZuJTYqgkn90gJyvVAzoXc72raM5aXszQG9bkVVDdOWb+e0vmkaX+Ij43u25Y1bxxAdGcGUR+byfs7Woz63qrqGf338DVdNnU+HVnH8+szeAYxUgpkSPBERETnEpcMz6NMhib98uMrvM9aqayzv52zjlN7taBkX7ddrharoyAiuGtWFz1fvZPbawJXWzl5XQFFZlcZa+Fjv9km8c/tYBnRqxe0vLubfn6xhT0nFISvq2wpLufzxeTz4xTqmDE3nndvHkpGc4GHUEkzUQ1VEREQOERlh+P05fbn88Xk8NnMDd57aw2/XmlfbYv9clWc2yU0ndeWtxVv4f28v4+OfnBSQFbX3l24jKS6KE7urPNPXUlvE8sKNI/ntm8u5/7O13P/ZWhJjIslITqBzcgLZuXuorKrhvksHc/4Q72YgSnA67gqeMSbWGPOkMWaTMabYGLPEGHNm7WOZxhhrjNl/0J+7Dzt2qjGmyBiz3Rjzs8POfaoxZrUx5oAx5gtjTBff/xVFRESkoUZ3S+GsAe15+Mv1bCss9dt1XsreTGJMJKf0Vov9poiLjuTP5/dn0+4DPPB541vt11dZZTWfrNzB6f3aExOlgjB/iI2K5F9TBvLCDSP5/Tl9mTIsg06t49m4q4SeaS15744TldzJEdVnBS8KyAPGA5uBs4BXjTEDDnpOa2vtkSYz3gP0ALoA7YEvjDErrbXTjDGpwJvADcB7wJ+AV4BRjfy7iIiIiA/95sw+fLpqJ3//aDX3XTbE5+efv2E37+ds4/YJ3bWHywfGdE/lohPSeXTGBs4d1JHe7ZP8dq2ZawooLq/iHK28+pUxhrHdUxnbPdXrUKQZOe4tF2ttibX2HmttrrW2xlr7PrARGFqP818D/Mlau9dauwp4HLi29rELgRXW2testWW4ZHCQMUY7REVERIJARnICN5/UlbeXbGXRpj0+PXdldQ2/f2cFnVrHc9uE7j49dzj7f2f3ISk+mt++uYwaP84yfD9nG20SohnTLcVv1xCRxmnwmroxJg3oCaw46NubjDH5xpinalfmMMa0AToASw963lKgX+0/9zv4MWttCbD+oMdFRETEYz86uRvtk+L443srKa/yXcOVZ+du4psdxfz+3L4akO1DyYkx/O7sPny9eR8v+KmrZmlFNZ+u2sEZ/dsTHanyTJFg06CfSmNMNPAC8Iy1djWwCxiOK8EcCrSsfRygRe3XwoNOUVj7nLrHD37s8McPvu5NxpiFxpiFBQWBn/EiIiISrhJiorj7nL7k5Bdy3VMLKC6rbPI5dxaVcd8naxjfsy2T+mo0gq9dMKQTY7un8I+PVjd4YHZ9vJi9mQMV1Zw7UOWZIsGo3gmeMSYCeA6oAG4HsNbut9YutNZWWWt31H5/kjGmJbC/9tCDC8CTgOLaf95/2GOHP/4ta+1j1tph1tphbduqU5OIiEggnT2wA/++ZBDZG/dw6aPz2FnctKThrx/voGb1AAAgAElEQVStpryqhnsm98MY46MopY4xhj+fP4DKmhquf3oBhQeanpTXyd1Vwj8/Xs2EXm0ZrfJMkaBUrwTPuHffJ4E04CJr7dHeKeqKvSOstXuBbcCggx4fxHelnSsOfswYkwh049DSTxEREQkCF56QzpPXDid3dwkXPTyHDQX7j3/QEczfsJu3Fm/h5vFdyUpN9HGUUiczNZFHrxrGup37ueapbPaXH6kXXsPU1Fh++UYO0ZER/PXCgUrORYJUfVfwHgb6AOdaa7/tlWyMGWmM6WWMiTDGpAD3A19aa+tKL58FfmeMaVPbPOVG4Onax94C+htjLjLGxAG/B3JqSz9FREQkyIzv2ZaXbhzFgfJqLn5kLgtzG9Z4pbSimj+86xqr3HqyGqv42/iebXnwiiEs21LID59eQGlF0/ZQPjdvE9kb93D3OX1p3yrOR1GKiK/VZw5eF+BmYDCw/aB5d1cCXYFpuLLK5UA5cPlBh/8B1zhlEzAD+Ke1dhqAtbYAuAj4M7AXGAlc5qO/l4iIiPjBoIzWvP6jMbSIjWLKo3P53dvLKCw9fgng8i2FnPPALFZvL+aeyf3UWCVAJvVr78prc/dw8/OLGt0oZ/PuA/x92mpO6tmWKUPTfRyliPiSsdZ/LXT9YdiwYXbhwoVehyEiIhLW9pdXce/0b3hmTi4pLWK5+5y+nDuww/fK9qprLI/MWM//fbKG1Bax/GvKIE7soZlegfbqgjx++UYOp/Rux98uGkC7lvVfgaupsVzxxDyWbyli+k9PomPreD9GKiL1YYxZZK0ddsTHlOCJiIhIYy3LL+S3by1j2ZZCRnVNZlB6a9okxpCcGENSXDRPzt7Agty9nD2wA38+vz+tE2K8DjlsPT9vE398bwUxkRHcOqE7Pzwx67gD5ovKKnnmq1zu/WQNf7twAJeN6BygaEXkWJTgiYiIiN9U11iem5vLE7M3srO4nIqqmm8faxkbxf+c34/zB3dSU44gkLurhL98uIrpK3fQqXU8vzmrN6O7plBaWU1ZZTWlFTXsKCojO3cP8zbsZvmWQmosnNyrLU9dO1yvoUiQUIInIiIiAWGt5UBFNXtKKthTUkF6m3hSWsR6HZYcZs76Xfzp/VWs2lZ0xMdjIiMY0rk1o7qmMKprCsMy22iouUgQUYInIiIiIoeorrF8tHwbu/dXEB8dSVxMJPHRkbROiGZAp1bHLd8UEe8cK8GLCnQwIiIiIuK9yAjDOQM7eh2GiPiY1tpFRERERERChBI8ERERERGREKEET0REREREJEQowRMREREREQkRSvBERERERERChBI8ERERERGRENHs5uAZYwqATV7HcQSpwC6vg5AGawUUeh2ENIhes+ZHr1nzpNet+dFr1vzoNWuevH7dUoFEa23bIz3Y7BK8YGWMWXi0YYMSvIwxj1lrb/I6Dqk/vWbNj16z5kmvW/Oj16z50WvWPHn9uh0v71CJpoS797wOQBpMr1nzo9esedLr1vzoNWt+9Jo1T0H9umkFz0e0giciIiIiIv6mFbzAeczrAEREREREJOQdM+/QCp6IiIiIiEiI0AqeiIiIiIhIiFCCJyIiIiIiEiKU4ImIiIiIiIQIJXgiIiIiIiIhQgmeiIiIiIhIiFCCJyIiIiIiEiKU4ImIiIiIiIQIJXgiIiIiIiIhQgmeiIiIiIhIiFCCJyIiIiIiEiKU4ImIiIiIiIQIJXgiIiIiIiIhQgmeiIiIiIhIiFCCJyIiIiIiEiKU4ImIiIiIiIQIJXgiIiIiIiIhQgmeiIiIiIhIiFCCJyIiIiIiEiKU4ImIiIiIiIQIJXgiIiIiIiIhQgmeiIiIiIhIiFCCJyIiIiIiEiKU4ImIiIiIiIQIJXgiIiIiIiIhQgmeiIiIiIhIiFCCJyIiIiIiEiKU4ImIiIiIiIQIJXgiIiIiIiIhQgmeiIiIiIhIiIjyOoCGSk1NtZmZmV6HISIiIiIi4olFixbtsta2PdJjzS7By8zMZOHChV6HISIiIiIi4gljzKajPaYSTRERERERkRChBE9ERERERCREKMETEREREREJEc1uD56IiIiIiISfyspK8vPzKSsr8zqUgImLiyM9PZ3o6Oh6H6MET0REREREgl5+fj4tW7YkMzMTY4zX4fidtZbdu3eTn59PVlZWvY9Tiab4R8EaKN/vdRQiIiIiEiLKyspISUkJi+QOwBhDSkpKg1csleCJ7839L/x3BPy7L3z8/2DfZq8jEhEREZEQEC7JXZ3G/H2V4Inv1FTDR7+Gj38Lvc6CHhNh3sPwn0Hw6tWweR5Y63WUAu512LlKr4eIiIhIiFGCJ75RWeqSuPkPw6jb4NLn4eKp8JMcGHMnbJgBU0+HxydAzmtQVeF1xOGrqgLevhUeGgVv3qTXQkRERKSe9u3bx0MPPeR1GMekBE+armQXPHMurP4AzvgbnPEXiKj9X6tVOpz2R/jZSjj7Xrcv780b4D8DYea/oKzI29hDUU0N7Fjhvh6urAhenAJLX4Qek2DZq/D8hVC6L/BxioiIiDQzSvAk9O1eD0+eBtuXwSXPwqgfHfl5MYkw/Aa4LRuufB3a9YHP/wQvXgLVlYGNOdTlvAIPj3H7IBc8ARUl7vtFW+GpMyF3Npz3EFz5GlzwmCudnXoG7MvzNm4RERGRIPfrX/+a9evXM3jwYO666y7++c9/Mnz4cAYOHMgf/vAHAHJzc+nduzfXXnstPXv25Morr+TTTz9l7Nix9OjRg+zsbADuuecerrrqKkaPHk2PHj14/PHHfRKjxiRI4+UtgJcudfu4rnkPMkYc/5iICOhxmvuT8yq8eSN89j8w6U/+jzdcfPMBJKRCbAv44Ofuv+/gH8DKt90K3hWvQvdT3XMHXQot28MrP4AnJrqkr8NAb+MXEREROZ6Pfu0WGHyp/QA482/HfMrf/vY3li9fzpIlS5g+fTqvv/462dnZWGuZPHkyM2fOpHPnzqxbt47XXnuNqVOnMnz4cF588UVmz57Nu+++y1/+8hfefvttAHJycpg3bx4lJSUMGTKEs88+m44dOzbpr6EVPGmcVe+7sszYJLjh0/old4cbeAkMux7m3A+rP/R9jOGoutLtd+x9Ntz4BVw/Hbqe7PZG2hq4/qPvkrs6XcfD9dPAGHj3di+iFhEREWl2pk+fzvTp0xkyZAgnnHACq1evZu3atQBkZWUxYMAAIiIi6NevH6eeeirGGAYMGEBubu635zjvvPOIj48nNTWVCRMmfLu61xR+X8EzxmQCDwGjgXLgdeAn1toqY4wFDgB1rfxettbe4O+YpInmPwYf/RI6DYXLX4YWbRt/rtP/ClsWwdu3wM0zoU2mz8IMS3nzobzIrZAaA51Huj9F2yAqFhKSj3xcWj8YMAXmP+q6oUZEBjZuERERkYY4zkpbIFhr+c1vfsPNN998yPdzc3OJjY399t8jIiK+/feIiAiqqqq+fezwMQi+GAMRiBW8h4CdQAdgMDAeuPWgxwdZa1vU/lFyF+y2fA0f3eXGIFzzXtOSO4DoOJjyjEvxX7sWqsp9EWX4WvsJRERB1vhDv5/U4ejJXZ2U7lBdrrmFIiIiIkfRsmVLiouLATj99NOZOnUq+/fvB2DLli3s3LmzQed75513KCsrY/fu3Xz55ZcMHz68yTEGIsHLAl611pZZa7cD04B+Abiu+MPi5yAqHi54GGISfHPO5Cw4/yHYutgNRpfGW/cpZIyCuKSGH5vaw33dvc63MYmIiIiEiJSUFMaOHUv//v355JNPuOKKKxg9ejQDBgzg4osv/jb5q6+BAwcyYcIERo0axd13393k/XcQmCYr9wGXGWO+BNoAZwJ3H/T4TGNMBDAH+Jm1NvfwExhjbgJuAujcubO/45WjqSyFZW9An3MhrpVvz93nHBh1K8x7CEbeAqndfXv+cFC0DXYsh4n3NO74lNoEb9daV+IpIiIiIt/z4osvHvLvP/7xj7/3nOXLl3/7z08//fS3/5yZmXnIYwMHDuTZZ5/1aXyBWMGbiVuxKwLygYXA27WPjQcygd7AVuB9Y8z3kk5r7WPW2mHW2mFt2zaxJFAab/UHUF4IQ37gn/MPvc593TzHP+cPdes+dV+7NzI5S0x1ifvutb6LSUREREQCyq8JXu3K3DTgTSARSMWt4v0dwFo701pbYa3dB/wYV87Zx58xSRMsfh5ad4bMcf45f0p3iG/jGoVIw637BFp2dA1TGsMYt4qnEk0RERERv7vnnnv4xS9+4fPz+nsFLxnoDDxorS231u4GngLOOsrzLdD01jHie/vyYMOXMOgKN8vOHyIiIGMkbFaC12DVVbD+SzcCoSndl1J7wC4leCIiIhKcrLXHf1IIaczf168JnrV2F7AR+JExJsoY0xq4BsgxxvQzxgw2xkQaY1oA9wJbgFX+jEkaaelLgIXBV/j3OhkjXIlgyW7/XifU5Ge78tmm7p1L6Q7FW6F8v2/iEhEREfGRuLg4du/eHTZJnrWW3bt3ExcX16DjAtFk5UJco5VfAdXA58BPcfvyHgbSgRJck5VzrLWVAYhJGqKmBpa8AFknQZsu/r1Wxkj3NX8B9DrDv9cKJXXjEbqe3LTzHNxJs+PgpkYlIiISPPYXwPacQ78X08LdXPbB7DHxv/T0dPLz8ykoKPA6lICJi4sjPT29Qcf4PcGz1i4BTj7CQzuAXv6+vvjA5jmwNxdO/q3/r9XxBJeo5M1TgtcQ6z5xyXFTu5umKMETEZEQlLcAXroUDhyhQmjiH+HEnwQ+Jmmw6OhosrKyvA4j6AViBU+au8XPQ2ySG4/gbzEJ0H4g5GX7/1qhong7bF8Gp/6h6edK7goYNypBREQkFKx6D964AVp2gAsfd6t2dWb+A2b+CwZdDi3TvItRxIcCMSZBmrPyYlj5DvS/0HeDzY+n8yjYsgiqVa1bL3XjEXwxuy46znVK1agEEREJBfMfhVeugrT+8MNPXDOyziO/+3PG36GqFL74X68jFfEZJXhybCvegsoDMNhPs++OJGMEVJXBtpzjP1fc/ruWHdwvL19I7aEVPBERad5qauDj/wcf/RJ6nQXXvActjjBLObU7jLgZvn5OnzskZCjBk6OzFhZOhdRekD4scNeta7SieXjHV1YE6z6D7hN9t0E8pTvsXu9efxERkeZo2Wsw90EYcRNc+tyxq5DG3+Xm8E77jX73SUhQgidHlzsbti6GUbcEtrtUUkdo1dk1WpFjW/Q0VBTD8B/67pwp3aGyBIq2+u6cIiIigWItfPUfaNsbzvwHREQe+/nxbWDCb2HTbFj9fmBiFPEjJXhydHMegIRUt/E40DJGuEYrupN2dFUVMO9hyBwHHYf47rzfjkpQmaaIiDRD6z+HnStgzB31v0E99DqXEE6/G6rK/RufiJ8pwZMj27ka1n4MI2+G6PjAX7/zKCjeBoV5gb92c7HiTTeUfOyPfXveulEJ2ocnIiLN0Zz7oUV7GDCl/sdERsHpf4G9G11jFpFmTAmeHNmcByAqHobf4M31M0a4r5u1D++IrIWv7od2fd3+O19K6gjRiW4WnoiISHOybSls+NJtL4mKbdix3U+FHpNg1r1QWeaX8EQCQQmefF/RNsh5BYb8ABKSvYmhXT+XZKjRypGt/8yVn4y+3ff7I42BlG5awRMRkeZnzgNuzt3Q6xp3/KgfQdk++OYD38YlEkBK8OT7sh8FWw2jb/Muhsgo17lTCd6RfXW/G43QkPKThkjtoT14IiLSvOzLg+VvwtBrIb51486RNR5aZcDiF3wamkggKcGTQ5UXw4Kp0GcyJGd5G0vGSNix3MUk39m2FDbOgJG3QFSMf66R0sP9oqws9c/5RUREfG3ew+7ryFsaf46ISNdcbv3nUJjvm7hEAkwJnhzq62ehvBDG3ul1JNB5JNga2LLI60iCy5wHIKYlDGtk+Ul9pPYALOzZ6L9riIiI+ErpPvj6Geh/EbTOaNq5Bl8BWFj6kk9CEwk0JXjynepKd/ery1joNNTraCB9OGDUaOVg+zbXlp9cA3Gt/HedlO7uq8o0RUSkOVj0FFTs980N6uQs6HIiLHlR45qkWVKCJ99Z8bYbSzAmCFbvwCUwaf1hzTS9wdZZ8hJg3SZwf6pL8NRoRUREgt3eTTD7Puh2CrQf4JtzDvkB7NkAm+f65nwiAaQETxxrYc5/ILWXaxEcLIZeA1u/1htsnY0zoP1AaJXu3+vEtoCWHTUqQUREgltVBbx2rfscc/a9vjtv38luO8Ti5313TpEAUYInzoYvYfsyGHM7RATR/xaDr4T4ZNc1MtxVlkL+AsgaF5jrpXbXCp6IiAS36b9zN4LP/y8kd/XdeWMSod/5rrqpfL/vzisSAEH0SV48NecBaJEGAy/1OpJDxSTAiBthzUdQsMbraLyVNx+qKyDzpMBcL6W724On8lhvVFXAzlVeRyEiErxWvOVGO42+Hfqc6/vzD/kBVJbAyrd9f24RP1KCJ7B9uRucPfJmiIr1OprvG3ETRMXB3Ae8jsRbG2eBiYQuowNzvZQeUFYIJbsCcz35jrXw5o3w0CiY8Q8l2SIih9u1Dt65A9JHwMR7/HONjJHuZqdm4kkzowRP3OpddCIMu97rSI4sMdW1LF76MhTv8Doa7+TOho5DILZlYK6X2sN9VSfNwMt+zN0xThsAX/wZ3r3DdbkVERG3ZeG1ayAyGqY85b76gzFuq8jmObB7vX+uIeIHSvDCXWE+LH8dTrga4tt4Hc3Rjb7dfcDNftTrSLxRUeLmAWaeGLhrpvZ0X3euDNw1BfIXwsf/D3qeCTfPhPG/gsXPwYuXQnmx19GJiHjvw7tgxwq48HH/Nx0bdDlg3IgikWbC7wmeMSbTGPOhMWavMWa7MeZBY0xU7WODjTGLjDEHar8O9nc8cpj5j7jyr9G3eh3JsaV0gz7nwIInw3Oz8+Z5UFMZuAYrAK07Q2Jbl3BIYBzY47rBJXWACx52DY8m/BYmP+AaIT11JhRtO/Y5irerrFZEQteSF91Nr5N+AT0m+v96SR3cyKbcmf6/loiPBGIF7yFgJ9ABGAyMB241xsQA7wDPA22AZ4B3ar8vgVBWCAufhn4XuA/zwW7MnVC2LzxbFufOgogoyBgVuGsa4/YfbJ4XuGuGs5oaeOtm2L8Dpjx96Ir6CVfDla/Cno3wxMSjN19Z/zk8MMyt9omIhJodK+D9n0HmODj5N4G7btY4yMuGqvLAXVOkCQKR4GUBr1pry6y124FpQD/gZCAKuM9aW26tvR8wwCkBiEkAFj0NFcUw5g6vI6mfjBEuwZn3X6iu8jqawNo4CzoNdfPpAiljJOzdCPt3Bva64eir+2DtdDj9L+61Plz3iXDdh1BTBU+eDhsPu5u8+AV4YQpgYctC90FIRCRUlBfDq9dAXBJc9CRERAbu2pnjoKrMjSoSaQYCkeDdB1xmjEkwxnQCzuS7JC/H2kPaw+XUfv8QxpibjDELjTELCwoKAhByGKipgezH3ZtWx2ZUGTv2x7Bvs/swHC7Ki2HrYvdaBVrGSPc1Lzvw1w4nB/bAF3+BvufB8BuO/rwOg+CGTyGpIzx3IeS86kqsv/wbvHOr26N5y2yIiFbXNxEJHdbCez+GPevh4qnQMi2w1+8yBjDuZqtIMxCIBG8mLmkrAvKBhcDbQAug8LDnFgLfaxForX3MWjvMWjusbdu2fg43TGycAYV5MOw6ryNpmF5nQv+LXGfBcHmj3TQXbHVg99/V6TAIImMgT2WafvXNh26P5Yk/daWxx9I6A66fBp1HuVEKU0+HL/8Kg66AK16D5Cz3c5LzijpvikhoWPgkLH8DTvldYJuN1YlvDR0Guu0SIs2AXxM8Y0wEbrXuTSARSMXtt/s7sB9IOuyQJEBt4gJh8fMQ1xp6ne11JA1jDJz7H0juCm/8MDzGJuTOcklW3WpaIEXHudEMWsHzr5XvuH2wHeq5mh7fGn7wBgy4BPLmw/hfw/kPQVTtFuYhP4ADu2DNx/6LWUQkEKoq4JM/QLdTYexPvYsjc5wr0aws9S4GkXry9wpeMtAZeLB2n91u4CngLGAFMNCYQ25XD6z9vvhT6T5Y/T4MmOI+wDc3sS3hkmehrMgleTXVXkfkX7mzIH04RMd7c/2MEa5EVJvL/aOsENZ/AX0mH3/17mBRsXDhY/DTlTDhN4ce2+1UaNE+PBsSiUho2bIIKva7iqMID6d7ZZ0E1RXupppIkPPrT4q1dhewEfiRMSbKGNMauAa31+5LoBq40xgTa4y5vfawz/0Zk+DKHKrKYMiVXkfSeGn94Ox7XfLz5V+9jsZ/ygph21JvSlLqZIxyv9S2LvEuhlD2zTRXntn3vIYfawy06vT970dGwaDLXNOWcFjlFpHQlTsLMNBlrLdxdB4NJhJyZ3sbh0g9BOJWyIXAGUABsA6oBH5qra0AzgeuBvYB1wPn135f/Gnx826mS33LwYLVkCtdKdrMf8LaT72Oxj82zQFb402DlToZI9xX3bX0j1XvQsuO0GmYb8875Adu72bOK749r4hIIG2cCe37Q0Kyt3HEJbmmdOGy/98ru9a5SjNpEr8neNbaJdbak621bay1qdbaS6y1O2ofW2ytHWqtjbfWnmCtXezveMLezlWw9WsYfGXDysGC1Zn/hHb9XLOJwnyvo/G9jbMgMtaVaHqlRTtok6UEzx/K98O6T6HPub4vPUrtAekj3A2dQ5oVi4g0E5Vlbg945kleR+JkjqstGS3xOpLQVLgFHh0H7zaT8V1BzMNiZvHE4ufdwOyBl3gdiW/EJLj9eNWV8Np1odc1MHemW0Hzeq9k51EuwVOi4Ftrp7ty6b6T/XP+IVfCrm/cBxIRCSxr3f7lsiKvI2m+tiyE6nJvukgfSdY4V1K/WZ2l/eKzP0LlAVj1Huxe73U0zZoSvHBSXenKtXqeAYmpXkfjO6ndYfL9kJ8Nn97jdTS+U7oPti/3tjyzTsYIKClwQ8/Fd1a9C4lt3d4Of+h3IUTFq9mKSCBVlcOSl+DRk+Cxk+HFS6G6yuuomqeNs8BE1M6hCwIZo9xNco1L8L38he4z6pCrIDIa5j7odUTNmhK8cLJ2uvuQPuQqryPxvf4Xwoib3BvCqve8jsY38hcC1q2eeU0Dz32vshTWTIfe50BEpH+uEZfkmrcsfwMqDvjnGiLi7C+AL/8O/9cf3r7FNacacTNsngOf/8nr6Jqn3FluHmtcK68jcWJbQMcTtA+vsXatPfLvImth2q+hRRqc8VcYeCkseRFKdgU+xhChBC+cLH7B/fB0n+h1JP4x6X/dG+/bt8GeDV5H03R581zHrk5DvY4E2vaB2CSVpfjSus+gsqRx3TMbYvAVUF7kbvCIiO9tXw7v3Ab/1w++/ItrxHHVW3DrPDjrHzDsevjqPtcxV+qvstTNnQuGKpaDZY1zpbflGtvcIPvy4KFR8OQkKNp66GPL33Cv9Sl3u1FYY+5w2xeyH/cm1hCgBC9c7N8Ja6a5uyKRUV5H4x9RsTDladc85tVr3Obs5ixvvuscFtvC60hcA5D04VrB86VV70J8G/+PwOgy1t39XveJf68jEm7Wfw7PnAuPjIXlb7rOtbctgCtfg26nfNfI7PS/ulWot26GvZu8jbk5yZvvVkGDLcHLHOc6FOuGZ8MsfQlqqtxWjydOgx0r3fcrDrhB9u0HuhuSAG17Qc8zIfsxVZ80khK8cLHqPfeGVPfDE6radIELHoXtOTDrXq+jabzqKshf9F1pZDDIGAk7V7rZfNI0VeXwzUfQ62y318CfIqOg6wS3YqgmOSK+kZcNz13gGkFMvAd+ugLO+Te07fn950bHwZRn3M/fa9e6n385vo2zXBVLFz/tUW6sjJEQEe3GN0j91NS4veBZJ8F1H7lEb+oZsGGG21pTlO9KMw/erjDmDijdA0tf9C7uZkwJXrjImw+J7aBtb68j8b9eZ0D/i2DOA813dMKO5a58L5gSvM4jAevKKKRpNsxwZZP+Ls+s030iFG9z/1+JSNPU1NTuF2oPt2XDiT89/oy25Cw4/79uTNH03wUmzuYudxZ0HOJK9oJJTIKraFGjlfrb9BXs2wSDfwAdBsINn0JSR3j+Inczvs/k71ezdBnjtqjMeRBqqr2JuxlTghcu8ua7D+ihMPuuPibeA1j49I8eB9JIdaWQwZTgdRrqupmpTLPplr8Bsa2g6/jAXK9u3+1alWmKNNny193okYl/aFgJfZ9zYeSPXNlZwRr/xRcKyve7/8bBMh7hcJljYdtSzcOrryUvuH38fc51/946A66f5prImQg47X++f4wxMOZOV9K5+v3AxhsClOCFg+IdsDc3uJIFf2vd2S3vL3sV8prhilPePGjZEVqlex3Jd2JbQlp/DTxvqrIiWPmO6/waFRuYayZ1gLQBbqi6iDReRYnbL9RhMAy8rOHHj73TfV31jm/jCjV581wZX7Dtv6vTfgDYGij4xutIgl9ZEax42/3Oi0n47vvxreHqd+Any90K95H0ORfaZMJX92uLQQMpwQsHdR/IwynBAxj7E1dCM+3Xze+NIS87OFdcO492sVWWeh1J87XiLagqdQ0ZAqnHRNcUQHsoRRpvzgNQvBXO+JtrPtVQSR0hfYS7ySNHt3GW2+cWDGOCjqRdP/d150pv42gO6n7nDT7C77yISEhMOfqxEZEw6jY38H57jv9iDEFK8MJB3nyIjHVdvMJJbAtXQrNlISx73eto6q9wCxTmBWdC3utMqDzgGnZI4yx+HlJ7BX78RffTXKOlDV8G9roioaJwC8y+D/pd0LTGH33Pg+3LQmOcj7/kznLvkTGJXkdyZMlZEBX/XSdIObolL7jfeenDGnd8/4tcsx3dFGkQJXjhIG++26gcqHKwYDLwMldK8+kfmk+r3W9XXEd4G8eRZJ4I8cmuxb80XMEayM92q3eBXp3NGOH2QGgfnkjjfAggVlIAACAASURBVPZHV5Y3sYl7u+v2Ia3U++gRlRXB1iX+HyHTFBGRrpX/zhVeRxLcCta4zzRDrmz877zEFLfnceW7za8ay0NK8EJdZZl7o+wchKtBgRAR4UppirbAnPu9jqZ+8rLdncH2A72O5Psio6H3Wa7Fv1p9N9ySF9ydyIGXBv7akdHQ9WSNSxBpjPyFkPMKjL7NjeNpijZd3E1X3Sg7ss3zXLVBsDZYqZPWTyt4dax1/Q4OH/7+7e+8RuxXPVifybB7LRSsbtp5wogSvFC3bQnUVAZnuV+gdBkN/S6EGf+ARc94Hc3x5c13pSn+no/WWH3Ocy3+N8zwOpLmpbrKDXrtMQlapnkTQ4/T3P4h7RsRaZh5D7vqhXE/8835+kx2XSL35fnmfKFk1bsQFRf8n1va9YWSnVCyy+tIvLf4OXhyIvy7L0z7rWvsV10FS1/2ze+8PucCRmWaDaAEL9Rtnue+pgdhuV8gTX4Auk2A9+6Ez/83eFcwKg64jcTBWJ5Zp+t41+Jfb7QNs/4z2L/Dlap4ReMSRBpny0JXMuirmWx1MzBXveeb84WK4h1upXTwFRAd73U0x5bW133dEeZlmtuXwYd3QZcTXTKX/SjcPwSePgv2b/fN77yW7V3DHZU115sSvFCXlw3J3aBFW68j8VZsC7j8ZTjhapj5T3jrFqiq8Dqq79v6tWsNHaydw8Dt5ex1BnzzAVRXeh1N87H4eUhIhR6nexdDUkc36kLjEkTq78AetyLR6QTfnTOlm/tZVJnmobIfdb9XRt/udSTHp06arivzq1dDfBuY8jRc/CT8OMd1Md+1xo178tXvvL7nuT2Pu9b55nwhTgleKLPWlfsFe5lDoERGw7n3wym/g5yX4YWLgq9l/LcrrsO9jeN4+kyG0r2QO9vrSJqHkt1u3+LASyEqxttYuk+EzXNdIwMROb4tX7uvHX2Y4IF7H908D4q3+/a8zVX5fljwJPQ+2yXAwa5FO0hICd8VPGvhndth7ya4+KnvFhJadXIdzH+2Cm6d47vfeXXNieo7Q7J4R1iXQCvBC2V7NsCBXcFd7hdoxsBJd8EFj0LuV/DZn7yO6FB52ZDaExKSvY7k2LqfCtGJKtOsr2Wvur2wXpZn1ulxmlsl3qg9lCL1srUuwRvs2/P2PQ+wKtOss/h5KNsHY3/sdST1Y4zbh7dzldeReGP+o24FeuIfjjw2JDrerez5Sqt015+gPmWam+bCf0fA1NODs1orAJTghbK6dvvBXO7nlUGXuT+Ln3erK8Ggpsa10G8OK67R8dBzEqx+H2qqvY4muFnr/j/rMNh1XfNaxkiNSxBpiC1fuxtvca18e952vd15daPMNeSY91/3/tScbkqn9XMJXk2N15EEVv5CmP476HkmjLkzcNfte55rHrg39+jPWf4mPHueq9oq2gLL3whYeMFECV4oy5vvmmGk9vI6kuA05g6oKoUFT3gdibN7rSt7bA4JHrjyopICV+4nR7dnA+xY7poGBIPIaNcoZ/3nwdtsSCRYWOtW8Hxdnlmn73mw6St1Ylz5NuzbHNhkwRfa9YXKEti3yetIAuvzP7kS1QseDuxM1z6T3dcjrXpbC1/dD69f5/bL3jof2vaBOQ+E5e86vyZ4xpj9h/2pNsY8UPtYpjHGHvb43f6MJ+xsng8Zw90sOPm+dn1qOz49BpWlXkdz0IDzZpLg9ZjkWlmrq9Wx1SXAWeO9jeNgWeOhMO/Yd0FFBIq2uu63vmywcrA+k93w9NUf+Of8zYG1bk5tSnfodZbX0TRMWhg2WqmucjPvep/j2xLM+kjOcjOCD1/1riyFj34Jn9wN/S6Aq952A9LH3OEas6z/LLBxBgG/fvK31rao+wO0B0qB1w57WuuDnhdkG6KasdJ9ULAKMlSeeUxj7nT7FJe+5HUksHGm27Cd2sPrSOontoVr2LHq3fArT2mIvPmutCu1p9eRfCezdoBw7ixv4xAJdlv91GClTvsB0LoLrJnmn/M3B7mzYNtS1zmzud2QbltbIRVOA893LHerll6V0vadDPkLoHALFG1zvRT+3dfdrB99O1w0FaLj3HMHXAwt2ruVvTATyJ+ki4CdgD5RBEL+Qve1OdWyeyHzROg4BOY86O1esqpyWPMx9DozsOUOTdVnMhRvczOi5MjyavdVBtMHl7a9ILEtbNTbscgxbfkaIqJcIuYPxrjfQ3nzw7KMDHAfvhNS3b745ia2pUvQd4ZRJ02vq4361M6QfOlSuG8AzLoXuoyBaz+A0/986O/aqFgYdYtrKrZ1iTfxeiSQnziuAZ619nvvYJuMMfnGmKeMMalHOtAYc5MxZqExZmFBQYH/Iw0FefPARLqOQ3J0xrgl/D3r4ZsPvYtjw5dQXgR9z/cuhsbodYbrplnXKlkOdWAPFKwOvhstdR8qc2eF74dKkfrY+rXbZ1W3IuAPGSPhwG63Xzfc7C+AdZ/A8B8G/2Dzo0nrF14reHnzIakTtM7w5vpte7qmZXtyYcSNcOdiuOwF9zvtSIZeBzEtYO6DAQ3TawFJ8IwxXYDxwDMHfXsXMBzoAgwFWgIvHOl4a+1j1v7/9u47Sqoq2+P4d3eTcw4qGSQnRUHMAcWIOaGiYn46jpOf44zjmMZx3phzwJwwgTgqMhgRUJJIVFFyEATJkvq8P3aVNNBAN3TVrbr1+6zVq+mqW1UbLl119j3n7B26hRC61a2bgQ27162C8UWGHp05o6FBB19GJzvWtg/UaOwbcaMyZbAXxMmkfVrFUaE69H0FVi2EJ3rB/PFRR5RZfplJz8Cl0k0P9tnXXBxUihRHCP6elqr9d0nJmZBkH9RcMm+sf8+2z77C6rWDH7/1lTi5YM7n0V+0vHAI/G469L7d9+XtSMUasO+FXl3zp9lpCS8TpGsG73zg0xDC98kbQgirQghjQggbQwiLgKuBo82sappiKj3jn4NBV8E3w6KOxBVs8mUle2XYrEGmyi/j67bnjPbCNOm2aYO3G2jdO/om2Lui6UFw8VDILwcDjoevh0YdUeaYMzoxk57iAeKuaHaIf//+42jjEMlUS7+Dn5enbv9dUrIFw5wIPn+iNn8cWB407Bx1JLuufjsIm2Dx9KgjSb3l87xAV9QXLctXhXKVi3989yv8+6iHUhNPBkpXgncBW87eFSW5TiiDNqoUU7eLoGYzeO96ry4UtSVfw/pVsFe3qCPJHl3P82pQn0WwEXfmJ97ctV2f9L92aanXBi4ZBrVbwItnw9inoo4oM8wZ7Xt3SvJBlC61W/rmcxVaESnavESBlVRfoMnL81m8OZ+n9nUy0bxxULdNdq82qpespJkDDc9/2X+XZRMINRpBh9Ng7NPejioHpDyZMrOewJ5sVT3TzLqbWWszyzOz2sC9wIchhOWpjqnUlSkPR98CS6bD2AFRR7N5yUOqrzrGSbnKsN+lXqo63RtxpwzyfWwtjkjv65a2qg3gonf87/HWtTD9nagjitamDf672DgDl2eC78NrdrAXWtE+PJFtzR8HZSp6L61Ua7S/V77OkcEnkPoeg+lSu4WvYMmFQitzRkPZSqkrOpRKB/7Kq3+OyYBxehqkY7asH/B6CGHlVrc3B94FVgKTgHXAOWmIJzXaHO97Wj64Lfo36HnjoHw1v0Ivxdfzam9T8N71JRvwLp+7601qCzZ5Urn3Mdm7wbyw8lXgrOe8T80bV+R24ZWFX8GGNZl9pbPpwbD6B5/1F5EtzRvnSwfzy6T+tZL78ObmUEXin2Z7cZk9u0Ydye7JLwt1WudGoZU5o714X37ZqCMpuQYdofnhMPrhnNgvmfIEL4RweQjh/CJufzGE0CyEUDmE0DCEcEEIYWGq40kZM9/suXYZfHRntLHMT3woZVJZ9mxQoToc8WeYNcJ7uxXHxvXwxNHw6GFeMbGkZo+E1Yu9r0tclK0AZz7tSfLAC3PijbRIyeVWUe9V2JFmiX542ocnsqVNG703W7r2z+65r+/XzaV9eKnuMZhO9dvFv9n5+tWwYGJmX7TcmZ7XwKpFMPGVqCNJOWUApalBR9jnAvj8EVjybTQxbFwHCydlZlGHbND1Al9PP/QvsOHnnR8/6VVYMc9n8d64vOQNv6cMgjIVoGWvXYs3U9VqDic/4B/gQ2+IOppozBkN1faC6ntGHcn21WzmMWofnsiWFk+DjWvTl3yUq+xjiFyqpDlvnC9trN8h6kh2X712PhaIegVXKs0b58Vkoup/VxpaHAH1O3rV9JKO17KMErzSdsQNvmY/qkHtoklQsCEeV8SikF8Get8GP82C0TupthSCv0nUaw/H/hO+GQoj7i7+axUUwNS3oOVR2b3BfHvanujVST9/1MsT55o5ozP/Sucv/fA+jf2HnUiJzE9TgZXCGnX3fbuZUKwtHeaP9+QuG6tHb61+DhRaSc4u77VftHHsjmTv4yXTvf9ijCnBK21V6sEhv4Wv34EZw9P/+umq+hVnzQ+D1sfBx/8HKxdt/7hvh/mSjJ7XeLPN9qfA8Jt9sFwc88Z4H7Jsrp65M0f9zdt1DL4GFuXABvSk5XP9am6mFlgprNnBvg9mcYwHJiIlNW+cL9uv1Tx9r9m4u+/bXfRV+l4zKgWbvKBZXMYq9dr59zh/zs0Z7XsNK9WKOpLd0+FUX7kyIoKq6WmkBC8Vul8JNZt6JcF0T9fPHw+V6kD1Rul93bjpdbMvz/nglu0f89m9UHUPL71rBifd54OBV/vDqh92/hpTBkFeWS+wElf5ZeGMAV5p9uGD4KW+MOuz+FdtzKZS0k2T+/C0TFPkF/PHwR5d/b09XZJL33KhXcKSb2D9yvisNqq2B1SoEd8Er6DA/182zuLlmUn5ZaHHlTDr081V52NICV4qlK0Apz4OK+bDm/+T3sHsvHF+RSydH0pxVKcl7H85jHvWN9pvbf4EL0zR44rNy0vKV4Uznvaedq/19yuU2xMCTBns68ErVE/N3yFTVN8LrhgBB/7aC9gMOBYePRS+ejW+id7sRCnp+llQSrpmE6jRWPvwRJI2/OwD9XQnH9X3gmp75kahlSiWwKaSme+hXBjT2dcfv/GxTTbvvytsnwu82vxn90UdScoowUuVRvv5LND0t2Hk/el5zXWrfF1xXK6IRe3Q33vbhBfO9sI1hX12L5SrCvteuOXtDTrAcXd68jflze0/99wxsHx2vJdnFlatIRx1I1w3BU642wdQr/WHaUOijiw1fiklnYby6qWh6SHahyeSNPszKNgYzV6jRt39AlHczRvn/V/r7B11JKWnQSe/MLCji7vZKln8Jy4JXoVq0O0iX0m19Puoo0kJJXip1ONKaHsSvH9jeipjLfgSQkF8rohFrWJNuGCQ//nJ3pv3VC6bBZPfhG4XFj371qWvr+8e//z2n3vCcz7DE6f2CMVRrpK/qV75mTcPHnpD/NoorF/tV3GzYf9dUrOD/ersokk7P1Yk7sY/78vtWhyR/tdu1B1WzPV9vHE2fxzs0QXy8qOOpPQ06OhbO36MqIp6Ks35HCrWild/5e5XemuSEffEcjWRErxUMoM+9/vyp4EX7Xoz7OKKU0+ZTNGgA1wyzM/h82f4B/+oh/zcdr+y6Mfk5UOXczwhLOpDev0a+Oo1n70rXzW18WeqZLXSZTO96WiczBubfaWkmx3i379+L9o4RKK29idfWdDxDN9ukW7Jfbtx3oe3cb1fBNsjyxucb61hJ/++YGK0caTCnFH+mRan7T/VGkKXc2HsAHjoQN+SU5z2WFlCCV6qVagOZz7jVepevzS1U/fzxnlxlSp1U/cauaj6nnDxO9DkQBh0FXzxOHQ4fcf9zbqcCwT48sVt75v6lm8u73peykLOCi2OgL17w8f/glWLo46m9PxSSrpbtHGURLU9vNjKhOdjeSVTpNgmvQYbf4aufaN5/QYdfXVHnPfh/TAZNq33ZexxUmdvyC8PC4vYt5/NVv/os5LZUDSspI77F/R5wP88+Gq4qz18cJtf6MlySvDSoWEnOO6fPqPz8b9S9zrzxsbvilimqFAd+r4Knc/1K1gH/mrHx9dqDk0O8hm/rQfME57zKqtNDkxZuFnj6Fu8LPgHt0YdSemYNRJGPgANO/sS32zSpS8s+96rnIrkqvHPeW+2hl2ief38sp74xDnBi2s7p/yyUK9t/AqtzPzYvzc+INo4UqFMOb/YfuUIuGCw77v96J/wRC/fjpPFlOClyz79oNPZ8OHt8N2Hpf/8q3/05txxe8PMJGXKwSkPwe9nbG5quiNdixgwL5vpBVi69I3XUoddVacV7H8ZjHt620I22WbyG/BMHy/Mc8bTUUdTcu1O8sJBE3awd1QkzhZN8a0OUb8/N9rfl/mtXx1dDKk0f5zv56rRJOpISl/DTn7u4rQSYspgb78Vxxm8JDNofiic+xL0ewtWLYLHj/LWY1lKCV66mMEJ/4a6reG1S2DFgtJ9/uR/wrgtechEFaoV77h2faBclS0HzBNeBAw6n5OS0LLSoX/wGdL3rs/OD8UQvNTywAu9aED/96FWs6ijKrlylaHDKV5AaN2qqKMRSb8Jz0NeGeh0ZrRxND3Y9/HGbX9y0rzx8W3n1KATrF3qbbLiYMNa+GYotD0hXgVxdqTZwXDxUO/fO+A4+Hpo1BHtEiV46VSusu/HW78GXr0YNm0sveeePw6w6JaVyLbKVYb2hQbMBQUw4QVofhjUUCP6X1SsCYddD99/BNP/E3U0JVNQAO/80auBtuvjVVcr1Yo6ql3X5TzYsHrHLT5E4mjTBpj4MrQ+FirXiTaW5odBh9Ng+C3eviRO1q+GxVPjWwyuQaLQysKYFFqZMRzWr/KK8LmkXhsvsFe7Jbx4NowZEHVEJaYEL93qtoYT7/E+O8NvLr3nnTfOl7sVd3ZJ0qNroQHzzE+8912uF1cpSreLoG4bePt3qa82W5qmvAGfPwI9roLTn4KyFaOOaPc02h9qt/J9SCK55JuhsHqxX+SImpmPE2q18IvBKxdFHVHpWTAx3u2c6rcHLD6VNKcM9pYhyUrLuaRqA7joHS8I984f4ac5UUdUIkrwotDpDNj3IhhxN0x/d/efL4RET5mYvmFms0bd/QrQ+Of8q0J1aHN81FFlnvyycOpj6ak2W1pC8P45tVvB0bdCXgzeTs28AuzskfDjjKijEUmf8c9BlfrQ8qioI3Hlq8KZT8PPK+C1/tnxnlgccW/nVL4K1G4Rjxm8jeth+js+ZskvG3U00ShfBc55ySupZ9nKqxiMSLJU73/4VP7ga2Ddyt17rhXzfUNoXK+IZTMz37A/e6TP4nU4PftneVIlXdVmS8vMT2DBl9Dz6ngkd0mdzwHLU7EVyR0rF3kPyM5ne4/OTFG/PRz/f/5e8+HtUUdTOuaNg2p7QtX6UUeSOg06xSPB+/4jWLc895Znbi2/TFbWt4jRqCTLlK0AJ94Nq3+AT/69e88V9yti2S45YN60PrreStlin37Q6SwfzMz4IOpodmzEvVC5rlfHjZNqDX0WY8KL8Zk1ENmRiS97UZNMWJ65ta59fVn/x3fCN8Oijmb35UI7p4ad4KfZsHZZ1JHsnimDvLJyi8OjjkR2gRK8KO25rw8ORz7g5fN31aTXoUxFaNCh1EKTUlStIbQ+zgvgKAnfMTM44a7UVZstLYumwLfvw/6X+8WauOnSF1bOz/wkW2R3fT0UPvyH9/iqu3fU0RTt2DuhXntvxJzNrRPWLPXWQXFfbdSgo3/P5tY/mzbCtLehdW+vJilZRwle1I78q5eeff/GXXv87NEw+XVvvK2lf5nrtCfgov/Esyx0aUtWm92QrDa7IeqItvXZfVC2EuzXP+pIUqP1sV7ddIKKrUiMjX3KK+TVbgFnPBV1NNtXrpJf+Fq5wPf9ZqtkO6e4X+iMQyXNWZ96u4d2faKORHaREryoVd8TDvy1788q3BC7OAoK4N0/QdWGcOC1qYlPSkfZCp64SPEUrjb74tm7v0+1NK2YD18NhK7nZ3dLhB0pUx46n+sV1GaNjDoakdIVAvz3ZnjrWl9+dtF/vGJeJmvc3VsnjLgn66r5/eKX7SQxX6JZpR5UaZDdlTSnDPaLmC2OjDoS2UUpTfDMbNVWX5vM7L5C9x9pZtPMbI2ZfWBmTVIZT8bqeY1vOn73T560FddXr/gb5lF/U/Ig8dPpTDjxXl8mOODYzFmuOfph369zwFVRR5Jah/0RajaBVy+CVYujjiYzrVgAS76NOgopiU0b4Y0r4JN/wT4XeIW88lWjjqp4jvqbf//vTVFGsevmjfeq0hVrRB1J6jXsBAu/ijqKXVOwCaa+Ba16+eyxZKWUJnghhCrJL6ABsBYYCGBmdYDXgb8AtYAxwMupjCdjlasER93kFfm+fLF4j1m/Gob9zZc6dDwzpeGJRGbffnDuK7D0e3iiF/wwNdp4fl7hDU/b9YGaTaONJdUqVPelsmuWwuuXqODK1maPhod6whNHwbpVUUcjxfX1OzDxJTj0T34BKZvKv9doDAdc7SsI5nwedTQll0vtnBp0gsXTYMPPUUdScnNGewFALc/Maulconka8APwSeLnU4HJIYSBIYSfgb8Bnc2sTRpjyhwdT4e99vMrc6t/3PnxI+7x9fi9/xGvEu0iW2t1lC+h2rQenjgGvngcJg7c/DXpteL9zpSGcU/DuhXQ81fpeb2oNegIx90J333oVfzETRkEz5zkS1nXLitZS4mCApg5Atb+lLr4ZPvmjwfLh4Ouy8490Qdd5/363v3fkq34idqKBT5miXuBlaQGHX2lxw9Too6k5KYMhvzy0OroqCOR3ZDOzKAf8EwIISR+bg98mbwzhLAamJG4fQtmdpmZjTGzMYsXx3SpkJkna6uXwN0dYMhvYMk3RR/70xxP8Dqc5uvyReKuYWe4ZJhXJH37tz6jlPx69WK4qx0M/hX8MC11MYTgs3dNDsydQQr4MrbO53ilwRnDo44meiMfhFf6+RX6K0ZAo+4w8n5f+rcj61bC6Efg/n3hqeNg6A3piVe2tGAi1G2TvdVvy1eBI2+EeWNg0qtRR1N8udbOqWGy0EqWLdMsKICpg71VTrYsXZYipaWjZ2Jv3aFA4ZJzVYCts7XlwDb/o0IIjwKPAnTr1i1sfX9s7NUNrvgERj0I45+DMU/4FZR2J0N+uc3HffWKfz/qb1FEKRKNGo3hik9h2awtb/95OYx/Br58yWfYWhzhV7mbHVK6r79oMiyd4Xtmc4mZN1uePwFeu9Tfo6rtEXVU6VewCd77M4x+CNqeCKc+5pWLe/4KXu4LUwf5RbetrV4Cn94F4571psF77Q9V94DJb8Cxd2j/dLot/Cr7+3p1Pgc+f8Srb7c5Pjv+D80b5zOnyRYCcVejKZSvln2VNOePgxXzvMK7ZLV0zeCdD3waQvi+0G2rgGpbHVcNyKByeRGo3x76PADXTYbDrvdB1aCrtpyx+GYoHPw7H/CK5JL8slCn5ZZfe+3rFTevmwJH/MV71D19orcyCKV4PWjqYG9Y3+aE0nvObJFsXbF+FXzy76ijSb8Na2FgP0/uelwFZzy9uS1N6+O8cMSIe7f9/7ZxPbxwJox6yJcaX/JfuOR9OOIG/7ecMij9f5dctuoHWLUw+5OMvDzofYcveXzudN8nm+nmj4N67XKnaEdeHtTvkH2VNKe8CXllYe/eUUciuyldCd4FwNNb3TYZ6Jz8wcwqAy0St0uVul7F7rrJcPXYLb+unQiH/j7qCEUyS+XacMjv4NovfdZ76A3wzh9LrzjIlEG+PLNK3dJ5vmxTd29fUTD1reza+7O7Vv8IT58EU4fAMbdD79u9d2lSXp4XvlgwAWZ+uuVj3/8LzBsLZwyA05/0VRoAjXtArRYwvgR792T3JQfbyT5l2azJAf5/at4YeOJoWDYz6oi2LwTf+7hnzNsjbK1hJ1/5kS0FqkLw/XfND8uNSqcxl/IEz8x6AnuSqJ5ZyBtABzM7zcwqAH8FJoYQUriJJguVKbftjEXN3OwmIVIsZSvA6QN80P35I/DKBbB+ze495+LpXhGt7UmlE2O2atfHZ0DmZmEFv13x4wyvkrlwIpz59PZbY3Q+GyrVgc/u3Xzb5De8pUaPq7atRmcGXft6M+Gl36UuftlScrlcts/gJXU4FS4YBKsXw+O9fBlkJlr2vRcjypX9d0kNu8CG1dmzD2/hRPhpFrTL8c+5mEjHDF4/4PUQwhZLL0MIi/HKmrcCy4DuwNlpiEdE4i4vD465FY79J0x725ds7k5z4CmD/XvbE0snvmzV6mjfD5z894iLtctg0usw8ZXNX2Of8tYca3+CCwbvuGR42YrQ/XJfPv/DVE8MB13jlZGP2k7Pss7n+JLfCS+k5K8kRVg40bc2xGl2oklP6D/UL2w9dTxMfrN0l6aXhmTimUvFqcDfLy3fVz1kgymDPN7Wx0cdiZSClBdZCSFcvoP7hgG52RZBRFKv++VQbU947RK4p7MP0ntcBY32K9nzTB3k1RKrNUxNnNmiQjVocaTvRzzm1uwsM1/Ykm99lm3CC36lfWs1m0HfV33lxM7sd4kXU/n4Tlj8NeSX8ZnkMuWKPr7aHl4QaMKLcNj/brnsU1JjwcR4LM/cWt3W0H+Y7/cc2M/3uvW4EjqesXmvaJTmjYMyFTyuXFK5NjQ90BOnI27I7PfL5PLMpgd53JL11EBNROKt7Qlw9ec+4Pl2mC+5e/yo4s9CLf3Ol9jk+vLMpHYnwfI5m8ueZ6M5X8ALZ3nLgnFPQ/uTof/7cM24Lb/+Z3TxkjuASrWg63nel3HRV15ls0ajHT+mS19YMdf7DEpqrVvpv8sNO+/82GxUtb7P5PV50GeGB18Dd7WH4bd6kaAozR/ny2Kzqal8aWnXB378xpf4Z7LF0zxOLc+MDSV4IhJ/NRr7jNNvpsCxd8KaH+GV8+Hd63deMCSZCOqDz7U+FvLKZO8yzQUTfSnbvLE+6lKB2gAAIABJREFUc3bdZDj5QWi0P9RuseVXmfIle+4eV0G5qnDoH6FVr50f3+Z4qFizZI3SZdcsmgyE+Oy/K0qZ8r6384pPod8QaNQDPv6nVxSOyqaNsODL3Nt/l9TmRMAyv2LulEGAJeKVOFCCJyK5o3xV6H4ZXD0Gul8Box7wJU07usI9dTDs0VVtSZIq1oRmh/qAoKi9PgsnwYr56Y+rOH5e7ue7Ui24ciQc9ieoUq/0nr9WM/j9N3D49cU7vkx5X0Y3dYjvA5TUiVMFzZ0xg2YHwzkvwJ7d4Ot3o4tlyXTYsAb23De6GKJUtT40PiDzL4hNGexxVq0fdSRSSpTgiUjuycuH3v+Ao2/1BO6ZPl4Of2s/zfGZnh0V2MhF7U7yyniLJm15+4Iv4bEj4OGDfBlkJgnBl60tm+V741LV7qKke5669IVN6+CrV1MTj7iFE6FiLd/7mEtaJaprrl4SzevnaoGVwtqdBD9M9j2/mWjJtx6fPudiRQmeiOQmM+h5NZzxFMyf4BUTF03Z8phk9TPtv9tSmxN8n0/hq9I/L4dX+kGl2lC+Gjx9QmZVjxv9iM86HvU37yGWKRp2hvodtUwz1RZO9L5kmVzoIhVa9gICzBgezevPH+fvB7VaRPP6mSBZfXlqhi7TTMaV61WiY0YJnojktvanQL/BsHYpPHQAPHc6fPtfn/GZOhjqd/D9WLJZ5Tre9D25ryQEePMqL75yxlNesKR+B3j5fE+sojZ3jDe+b30c9Lwm6mi2ZAb7XOCNoL98Oepo4mnTBm9fkQvLM7e2R1e/6PLN+9G8/rxxsEcXb12Tq6rv5UtlM3WZ5pTBHl/1PaOOREpRDv/GiYgkNO7h+/IO/7MvM3zuVHigO8wepWUr29Ouj++vWTwdRj0E04b47Fjj7r78sd9bnlC98wd49393v9n8rlqzFAZe6C0uTn4wM2dwul0MjXvCkF/DDxlebS8bLZ4Om9bnZoKXl+etTWb8d+cFpUrbxnVe3CZXC6wU1u4kWDABls2MOpItLZvpcelzLnaU4ImIgM9KHfoHuG4SnPyw9y/LKwPtT406sszU5gTA4L9/h/f/4j8fcPXm+8tVgrOehf0vg1EPwl3tYNhN6S3A8tMcGHAsrFoEZzztBWIyUX4ZOP1JKFvJi8CsL6Inn+y6hYkCKw1zMMED34e35kefJU6nOZ9DwQbYq1t6XzcTJZf5Z9KydfA+nKAq0TGU8kbnIiJZpUx56HIOdD7be2dVqBZ1RJmpWkNv/j5tCNRoAn0e2HZ2LC8fjrvTl8GOehBG3A2f3es/tzjC9/EllangLRhK2ppgexZ8Cc+f6RVSz3st84s8VGsIpz0Oz54CQ66DUx7JzNnGbLTwKyhTEWoXs6dh3LQ4EjDvA7pXGqtZTh3sv9fND0/fa2aqWs18BnnKoMxZJr5+DXz+qK+0qNk06miklCnBExEpipmSu53pdKYnUmc+DRVrbP+4Jj39a9lMGP0ojHsGvhpYxHEHwdnP7f5M2zfDfCasQg24+F2o3273ni9dWhzuvfk+vM3/vfa9MOqI4mHBRKjf3i845KLKtf0Cx7fvw2F/TM9rFhT4bFXLo6B8lfS8ZqZr1weG3wzL52XGfrcJz/ve80xJOKVUKcETEZFd0+1iT/LKVy3e8TWbQu/b4Ig/+7LJwmZ9Bm/9Gp7sDX0H7nrfwXHP+PPUbwfnDvSZsWxyyO9hzij4zx+8OEi5QoPjshV9uV25ytHFl21C8Bm8jqdFHUm0WvaCj+7wPamVaqX+9eaNgZULVIG4sGSCN/Ut6HFFtLEUbIKRD3hxlcYZVFVYSo0SPBER2TVmxU/uCitXGWo13/K2Ws09qXvpPHi8F/R9xVsIFFcI8MFt8PE/fUnamU/vWmxRy8uDUx+Dxw6H//xu2/srVPeZvf0v8+p8smM/zYJ1y3OzwEphrXrBR//wdgkdT0/9600ZBHlloXXv1L9WtqjTyt/TPrgV6rWF5odGF8u0Id7LtNdNWgoeU0rwREQkMzQ7BPq/560qBhwHR9zgyyyT8vKh2aFQtf6Wj9u43puYT3wJup4HJ9wN+WXTG3tpqlwH/udzWLlwy9tXzIPPH4PP7oPP7vcZgYN/Cw06RBNnNliQKLCS6wneHl290fs376c+wQvBS++3ONwvSMhmZz0Pz58Bz50Gfe73vd7pFgKMuBdqNksUy5I4UoInIiKZo15buGQYvHAmvPunbe/PK+sD1B5X+tXwn5d7v73vP/I2F4f8Ph5XpMtW9MIMhdVqBk0Pgp9me3GEsc/A9He8OEtbDdSKtHAiWH727MNMlbx8aHmkF1opKEhtX7oFE2D57PTt98smNRr5vuCXz4M3LvfeoQf/Lr3vWbNH+hLa4/6Vu/tSc4ASPBERySzVGsKlH/jgp7B1K2D88zD+OfjyRW+2vnYZLPkaTn4IupwbTbzpVqMxHH0L9LwWXjzLB4vH3gHdL486ssyz8Cuos7cnzLmuZS8vbrRgwuaqsgWbYNYIqN+h9PbmTRnkSXXr40rn+eKmYg0473UYfDUMv8Uv2Bz/7/StOhhxL1SqDV36puf1JBJK8EREJPPkl9l2Bgt81u7w62H8s16Rc+0yL8rS4oj0xxi1KnWh3xB4rb83lP9pNvS6ObWzM9lk0WT47kPoeEbUkWSGloXaJdRu6VUURz/s1W33vQhOvHv3XyMET/CaHZKeYi7Zqkw5b4VSvRF88i/vD3rGU6nfN7x4Onz9Dhz6J+9VKrGlBE9ERLJLxRpe2rv7lbBxbXYWUykt5SrBWc/BO3+EkffD8rk+cCxbIerIovXzCnjlAt/DeeRfo44mM1Su43vxkvs4162ARj2gXFVPhEvDosmw9DuV3i8OMzjyL14s6e3f+r7jvgOhaoPUvF7BJvjk396bcP9LU/MakjF0mU9ERLJTfpncTu6Skg3le90MU96EZ0/2cvi5KgR461pPNE5/EqrUizqizNH+FO99tvcxcMlwL2rU5RyvqLh87u4//9TBYHkq3lES3S6Cc16CH2fA40fBD9NK9/nXrYTRj8B9+3ohqn0v8mRfYk0zeCIiItnODA78lTdQfuMKeOJoOO9V7z2Ya754HCa/DkfeCE0PjDqazHLA1bBf/y17KTY92L/P/HT3qzpOGQyNeyqpLqm9j4aL/uPFpZ442gsntepV/OIrG9f70tu1y7a8/Ycp3ht03QrYa3+fzVZvwpygBE9ERCQuOpwGVRvCi+f4bMC5r2wuqJEL5o2Fd/8XWh0DB/466mgyT17elskdeIGVijXh+092L8Fb/DUsngrH3rl7MeaqPbpA//e9jcILZ3hrjx5XQYdToUz5oh+z+kcYO8AvaqxcsO39lg/tT/bn2atbauOXjJKWBM/MzgZuBBoDC4ELgTnA98DqQofeEUK4OR0xiYiIxFKTntB/KDx/Ojx1PJw+IDcaTq9dBgMv9D1MpzysYjPFlZfnFWlnfrx7zzN1kH9Xy45dV7MJXP4RTHwZRj0Eb14Bw26EfS+EGk0KHRhg7hfw5Uuw8WcvMnXiPVC3zZbPV6GaJ++Sc1Ke4JlZL+AO4Czgc6Bh4q5kPdgaIYSNqY5DREQkZ9RtDf0T/QRfOsd7Xu3XP+qoUmvYTV6N8OL3VMGxpJodAtOGwLJZnmSU1Ir5MOphTxSr7VH68eWSshU9odunH8wY7oneR3dse1yZCtDpLO8JWq9t2sOUzJaOGbybgL+HEEYlfp4HYGZN0/DaIiIiualqfbjwbXj1Ynj7N95X8Ii/xnNma+EkGPc07H+5lqLtiqYH+feZn5Q8wdu00f+PbVgLJ9xV+rHlKjNvbdHySFi1GDas2fL+ijV9hk6kCCl9lzezfKAbUNfMvjWzuWZ2v5kV7jg6K3H7ADMrsqyPmV1mZmPMbMzixYtTGbKIiEh8lK8CZ7/glfM+vQveuAw2ros6qtIVArx3PVSoDof+IeposlPdtt78+vtPSv7Y4X+H2SMTSwRbl35s4j0vazbZ8kvJnexAqi/j1ceXYp4OHAx0AboCNwBLgP2AJsC+QFXg+aKeJITwaAihWwihW926dVMcsoiISIzkl/GZlSNvhK8GwrOnblttL5tNfwe+/wgOu15LM3dVXp7P4s38xBPm4pr+Doy4B7pdDJ3UUF4kU6Q6wVub+H5fCGFBCGEJ8G/guBDCqhDCmBDCxhDCIuBq4GgzU1MjERGR0mQGB/8GTn0M5oyGJ3vDT7Ojjmr3bVwPQ2+AOq29n5jsuqYHw4p53hOvOJbN8pYcDTvDMbenNjYRKZGUJnghhGXAXKDw5aDtXRpK3h7DzQEiIiIZoNOZcP7rsGKBt1GYPyHqiHbPF4/B0hlwzG2QX3bnx8v2NTvEvxdnmeaGn71iaQhwxtNQtkJKQxORkklHMjUAuMbM6plZTeA6YIiZdTez1maWZ2a1gXuBD0MIy9MQk4iISG5qdgj0fw/yysKA4+Cb96OOaNes/hE+vANa9oJWR0UdTfarszdUqe/LNHdkzVJ47lSYPw5OfgBqNUtPfCJSbOlI8G4GvgC+BqYC44FbgebAu8BKYBKwDjgnDfGIiIjktnpt4ZJhULs5vHAWjHs26ohK7sPbYP0qOObWqCOJBzPfh/f9DvbhLZsFTx7jPdhOewLanpjeGEWkWFKe4IUQNoQQrgoh1AghNAgh/CqE8HMI4cUQQrMQQuUQQsMQwgUhhIWpjkdERESAag3hond8Ru+ta33wni1W/QBjn/J9d6rcWHqaHgyrFsKP32573/zxvqx31SI4/w3oeHr64xORYtF+NxERkVxVvir0ud9nb0Y9GHU0xTfxZSjY6H3vpPQ0Pdi/f//x5ttCgMlvwoDjvbl2//c3980TkYykBE9ERCSXVd8LOpzuyzTXLI06mp0LAcY/D3vtB3X3jjqaeKndAqo29H1469fAmAHwYA8Y2A/qtIRL3teMqUgWUIInIiKS63peAxtWw5gno45k5+aPg8VToet5UUcSP2Y+i/fN+3BXexjya8gvB6c8Av2HQdUGUUcoIsWgBE9ERCTXNegALY6E0Y94CfxMNv45KFMR2p8adSTx1OZ4WL8amvT0PZqXfwydz4Yy5aKOTESKSQmeiIiI+Cze6h98f1um2rAWvnoN2p0EFapFHU08tesDf14AZz/vSZ5Z1BGJSAkpwRMRERFofhg06Agj74eCgqijKdq0t2HdcujSN+pI4ssMylaMOgoR2Q1K8ERERMQH9j2vhSVfwzfvRR1N0cY/CzUab672KCIi21CCJyIiIq79yVC9EYy4N+pItvXTHPjuI5+9y9PwRURke/QOKSIiIi6/LPS4CmZ/BnPHRB3Nlr58EQjQ+ZyoIxERyWhK8ERERGSzfc6HCjVg+C3ecy4TFBTAhOeh2aFQs0nU0YiIZDQleCIiIrJZ+apw2J/guw/gm6FRR+NmjYBlM9X7TkSkGJTgiYiIyJb2uwRqt4L3rodNG6KOBr54HMpXhzYnRB2JiEjGU4InIiIiW8ovC8fcCj9+68lVlJZ+D1MHQ7eLoFylaGMREckCSvBERERkW62OhuaHw4e3w5ql0cUx6kGwfOh+RXQxiIhkkTJRByAiIiIZyAyOuQ0ePhA+/Acc98/N9xUUwHfDvXVBYZVq+TLKvPzSiWHNUhj/HHQ6E6o1LJ3nFBGJOSV4IiIiUrT67WDfi3yZ5n79odqeMOEFGP0QLP2u6Mcc+kc4/PrSef0vHocNa6DnNaXzfCIiOUAJnoiIiGzf4dfDV6/CS+fCqsWwbjnsuS+c9gQ06QnY5mP/+3f46J/QqDu0PHL3XnfDWhj9iC8Vrdd2955LRCSHKMETERGR7atcB464Ad79E7Tr443QG+1X9LHH/x8smACvXwqXfwLV99z11/3yJVizBHr+atefQ0QkB1nIlCamxdStW7cwZsyYqMMQERHJLRvWQtmKOz9uyTfw6GFQvwNcOMQrcpZUQQE8sB+UqwKXfej7AUVE5BdmNjaE0K2o+1RFU0RERHauOMkdQJ1WcOI9MGcU/PemXXut6f/xFg0H/krJnYhICaUlwTOzs81sqpmtNrMZZnZw4vYjzWyama0xsw/MrEk64hEREZEU6ng6dOsPn90HU4eU7LHr18CIu6FGY2jbJzXxiYjEWMoTPDPrBdwBXARUBQ4BvjOzOsDrwF+AWsAY4OVUxyMiIiJp0Pt22KMrvHqRF2nZmRXzYdhNcFc7mPsFHPQbyFepABGRkkrHO+dNwN9DCKMSP88DMLPLgMkhhIGJn/8GLDGzNiGEaWmIS0RERFKlTHk4/w146Tx4rT8snwsHXrvtkst5Y2HUQzD5DSjYBG2Ohx5XQpMDo4lbRCTLpTTBM7N8oBsw2My+BSoAbwK/B9oDXyaPDSGsNrMZidunbfU8lwGXATRu3DiVIYuIiEhpqVgTzn8d3rwSht0IP82GYxMN06e95YndnNFQrirsf5l/1WoWbcwiIlku1TN49YGywOnAwcAGYBBwA1AFWLzV8cvxZZxbCCE8CjwKXkUzhfGKiIhIaSpTHk59HKo38r11P0yF5XP8q0YT6P0P6NIXKlSLOlIRkVhIdYK3NvH9vhDCAgAz+zee4H0MbP1uXg1YmeKYREREJJ3y8qDXTVB9L3jnj9D4AE/sWh8LeflRRyciEispTfBCCMvMbC5QeNYt+efJQL/kjWZWGWiRuF1ERETiZv9Loet5xW+5ICIiJZaONgkDgGvMrJ6Z1QSuA4YAbwAdzOw0M6sA/BWYqAIrIiIiMabkTkQkpdKR4N0MfAF8DUwFxgO3hhAWA6cBtwLLgO7A2WmIR0REREREJJZS3iYhhLABuCrxtfV9w4A2qY5BREREREQkF6RjBk9ERERERETSQAmeiIiIiIhITCjBExERERERiQkLIbv6hpvZYmBW1HEUoQ6wJOogpMSqA8ujDkJKROcs++icZSedt+yjc5Z9dM6yU9TnrQ5QOYRQt6g7sy7By1RmNiaE0C3qOKRkzOzREMJlUcchxadzln10zrKTzlv20TnLPjpn2Snq87azvENLNCXXvRV1AFJiOmfZR+csO+m8ZR+ds+yjc5adMvq8aQavlGgGT0REREREUk0zeOnzaNQBiIiIiIhI7O0w79AMnoiIiIiISExoBk9ERERERCQmlOCJiIiIiIjEhBI8ERFJKTNrbWbXRB2HlIyZ7WNmd5tZm6hjkZIxs/yoY5CSMbOqUccg8aEET3KamTUxs7ZmVi7xs0Udk+yYmbUys55mVivxs85ZBjOzKsDjwD1mdkDU8cjOmVkVM3sG+ARYFkKYFnVMUjxm1sXMXgPOjzoWKR4z62Bm44AXEz/rMy0LmFk7MzvDzDomfs6o85YzCZ6ZVY86BskcZlbJzJ4DPgYeA140sw4hhGBmOfN7kU3MrLKZPYufs+uAN81s38Q5y6g3VtkshLAK+AKYCtytc5XZzKwR8C3QPIRQOYRwU9QxyY4lf6fM7E/4++Nk4DPN4mU2M6ua+Ez7BFgBVDKzekHVDzOamZU1syeBT4HzgJFmdlqmjUViP5A1s4pm9gTwnZk1jjoeiV7i/8FAoALQCvgNsBS4ESCEUBBddFIUM2sGvA+UB5oAVwMFQB8AfSBmDjOrY2ZlC/1cF9gbODjx/dKoYpPtM7O6iYTgB+A9YGbi9mPN7EYzO97Mmidui/3YIZskBpblgO7AySGEv4YQvg4hbIo6NimamR0OfId/ptUB/gRUBTZEGZcUyylAY6BBCKEP8HfgDsissUis36QTS4Mewn95fgBujjYiiVJiAGP4//sPgMtDCOtDCJ8Do4CfzSw/k67A5LrEOcsDFgO/DiGcGUJYDxwJdAPmJ69S67xFy8yamtkI4E3gLTPrbGZlQwiLE4eUA/4C3JQ4vqqZlYkoXEnY+rwBbYD7gO5mNhu4C9gD+Cvwspnl6SJYZtjqYsr+QLsQwnAz62Nmw83sHjPrG2WMsqXEBS+AucBBic+0TcA3QDugdeI4fZ5lkMTvWvnEj52BmomxCPh4cnzydzFTzl0sE7zkm15iadAbwJ/xK/3nm1nPaKOTdCs0gBmED2BqAa+EEH5M7r0DqgONQgibMukKTK7aatA5BGgBTEjc93/AAOCJxOFPmVlFnbfomFlFfJ/dWOA0fLnR34BzEx+K9YClIYT7gdVm9hV+/hpEE7HANuftVGANnoTvD9wK/CeE0CaEcDlwLLAW+EfisRkxiMlFW78/JvYAzcN/t/4O/A54GVgC3GlmZ+liSrQKj0PM7D9AxRDC9MR95fAVKe8DXSGzZoJy2Va/a4PMrDMwGlhpZheYWXvgKaAu8JCZtY4u2i3FKsHbaiA/OHEi3gshTAohfI0PKO6ONEhJq60GMKfgA5S/AoclDkleie6C/79JPk6Dl4gUMehcjScLySvRD4QQyocQrg0hPAw0BR5NPDZW72lZZC98yfODIYRFwCXAOOAiPIkbDTQ2szOBSvhV6mdDCHMjilfc1uetP34h5TRgWgjhCgAzKx9CWIpfqW5pZuU0AI3Gdi6m/BX/XRsKnAM8HEJ4JIRwM/7eeBag1SkRKWIcsgq4ycwuBkisJFoG1MaXaaoKagYo4ndtFXA9/pl2HXA8MB54B+iHTxTcAdSPIt6txWYwVMSgcBU+KDyn0GH/A7Q3s36FHhebfwMpUlEDmLHAhWbWMoSwMXFcY2BMocdVTG+YUkhR52wccEHinH0HXnQlcfzzQA8zq6SlY5ExoAOwHCCEsAJfPTELv6jWExiMfzieBYwAfhtJpFLY1udtOX6leh5+nkjcvi7xx72BjwotTZL0K+piygR8/101fJBZuN7Aq8DRQG0l5ZHZ3gWw88ysZaHjPgROAND+yYxQ1FhkEnAuMB+/oPJkCOF3IYRZeNLXG18lFrk4JTfbGxSen/wFSnwo3UBiiUlCZdCMTYwVNfB8HR/AXAVgZvWB6iGET8zsUDObAfw6onhl++dsLolzlrh9deKPXYDXQghr0hynJCRWSHyFzyQkTQU+Bxbge6AfA3qGED4hMcuQ7jhlSzs4b6OAJuYtSaqZWXszG4LvEfogglBls6KS8reA7/Gl0AOBvma2d+L4TvjFlsXbPpWkyfY+0+YDVxY67ltguZlp6XpmKOp3LTkW+RvQHE8fKiSO74kn6T+mO9CixCnB2+kvUGJz+F3AUjMbbGZrgNsTx+vKVgxtZwAzDRgJNDUvCX44UCWxLv5V4M4Qwm1pD1aAYp2zvc2sYaKIxxD8/A0q4qkkvf4BnJIcWCauQE/Hi+FcGEK4K4SwxszyQwgLQwgvRRms/KKo8zYFH7z8jPdTexOYGULoHEKYGFmksr33x8mJ234G3sbP3yAz+wyfQX8thKDqjBHZwWfaZ/hnWovEbcvxz7MV6Y1QirKTsUglYCO+ReSlxFjkPnxGb1GaQy1SbBK8YvwCNQ8hFJhZPTwZ3Af4bQjh6vRHK2m2vQFMK7yoQHO89P6kEELdxL4uidaOBp3r8BmgF4DvQwitQwgjI4tUkoYlvp4pdNsEvAx4svqYlh5lnqLO2yQgH39/HAQcoM/KjFLU++NUoCO+/PnsxNf9IYR6IYQ3I4tUknY0DlmZOOZd4O+JC2FaVZYZijpv0/DVDI/ibbY+wpeu1w8hvBJZpFuxOE1cmdnxeCGVQxIJH2Z2GHAvcEQIYYmZLQD+G0I4L7pIJZ0SVfyeAFqGEHokbquDLzU6CGgJzAshLIwuSilsJ+fscKAssD6EkBFLIcSZWSXgS+BrvHnv+fje1ku1bytzbee8jQX6a+Yn8+zg/XE4cGJiP5BkkJ2csxNCCLOjjE+KtpOxyIkhhJkRhrdDcUvwdvQLdEoIYYaZVUm0T5AcsoMBzMWFCq1IBtGgMzuZWRvgALys/sjEsnjJcDpv2UUXU7KPzll2ytbzFqsED3btRJhZQ+BS4INEoQ3Tnrz40QAm++icZS+9j2YnnbfsoffH7KNzlp2y8bzFLsGDkp8IM2uLb0T+DPhnCGFt6qOUqGgAk310zkREiqb3x+yjc5adsum8xTLBSyrOiUgeY2ZXAkcCT4UQhqQnQhERERERkdITmyqaRSlucpf48WW8OXqvZA8SVTESEREREZFsEusEL2l7iVpi5m5vMzsyhLAUGIz3tOidvD99UYqIiIiIiOyenEjwEonc9v6uZwJvm1k54A1gJnCImbUDzeKJiIiIiEj2yIkEz8x6A7eY2R6Jnw9J3hdCuAWYD/wlMWP3MlATL9CiWTwREREREckaOZHgAfnA0cCBiWboj5nZoYXuvxb4vZk1DiF8hrdV2MfMDo8gVhERERERkV2SEwleCOFt4HPgKKAAX4p5daH730rcf3vippeAusC+Zpaf3mhFRERERER2TewTvEJ76O4B2gJNgJFADTO7oNChHwFnJwquzAB+F0L4VwhhU3ojFhERERER2TWxT/ASBVYshDAdGIr3utuQ+PNlZlY9cehy4AvgwMTjJgLsoDiLiIiIiIhIRol1o/OtmVlV4HVgOPA+cDPQEC+qMga4MISwMroIRUREREREdl2ZqANIFzPLCyGsNLNngAvx2bozgROATSGEV7Y6tiCaSEVERERERHZNTs3gJZnZS8CPwE0hhB8K3Z6vPXciIiIiIpKtcmp/WaGCK/cB+wJNC9+u5E5ERERERLJZTiV4iYIreSGEEfjf/Zjk7Tt7rJk1N7NqiT/bzo4XERERERFJt5xK8ABCCAVmVglYC0wvzmPM7H+ASXiz9GIlhCIiIiIiIumWcwlewsnAeLyiZnF0BpYB+5tZq5RFJSIiIiIishtytciKFXNZZn4IYZOZ/R5vp7Av8BTwQghhXYrDFBERERERKZGcnMHbXnJnZuUT3/MTxyWLrhwADACGAH2AZmkIU0REREREpERyMsHbmpnVNLMngYdhc2JnZsl/nzlAI+AJoAJwjpndYmadoohXRERERESkKDmqsZl4AAADJ0lEQVSf4JlZR+ANYD9gbzM7NXF74WbnXYHpIYSlwAbgz0BH4LsIQhYRERERESlSzid4QDngWeBC4L/ApWZWLlFts1zimNHATWb2FVAN+BSYCVROf7giIiIiIiJFy7kEz8zamNmhZlYvcdNXwKshhLHAe0AArgYIIaxPLNNsCLQH7g4hHArcAdRKf/QiIiIiIiLblzNVNBOFUx4GzgTG4knbH0IIbxU6pgrQHzgNOD+EMCtxezNgUQhhTdoDFxERERERKaZcmsFrD7QEWuANy58C7jGzQ5IHhBBW4cs05wPXFXrsnBDCmmTRFTOzdAUtIiIiIiJSXLFO8MyseqFKmD2AJiGEJUBBCOEOfG9dPzNrXuhhXwMvAh3M7DYzGwEcCZAsulKcHnoiIiIiIiLpFssEz8xamdl7wPPAa2bWBJgCzDazLoWqY94OdAZ+aXcQQlgPbMITwn7AYyGE99L6FxAREREREdkFsUvwzKw/MBwYD/wBL4byF6AMsAhfnglACGEiXmTl/MRj882sF/Aq8GAIYc8QwlNp/QuIiIiIiIjsotgVWTGzW4BZIYTHEj/vBUwD9sYTuX2AR0IIwxP3nwj8A9gvsc9uT2B1COGnSP4CIiIiIiIiu6hM1AGkwMPAOgAzKw+sAWYAFYGBeJGVX5vZjESVzP2AockKmSGEeZFELSIiIiIisptil+CFEOaCV7oMIawzs3b4UtQ5ib529wK3AG+b2U9Aa6BvdBGLiIiIiIiUjtgleEmFKl0eBkxPFE8hhDDJzE4DugLtQwhPRxSiiIiIiIhIqYptgmdm+SGETcD+wLuJ267EZ+xuDSGMAcZEGKKIiIiIiEipim2CF0LYZGZl8Cqa9czsY6ApcHEIYXGkwYmIiIiIiKRA7KpoFmZmHYEv8fYI/xdC+FfEIYmIiIiIiKRM3BO8csDVeE+7n6OOR0REREREJJVineCJiIiIiIjkkryoAxAREREREZHSoQRPREREREQkJpTgiYiIiIiIxIQSPBERERERkZhQgiciIiIiIhITSvBERERERERiQgmeiIiIiIhITCjBExERERERiYn/B7pkcv6PCK58AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy['2014-07-01':'2014-07-07'].plot(y=['load', 'temp'], subplots=True, figsize=(15, 8), fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create train, validation and test sets\n", + "\n", + "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", + "\n", + "Allocate the period 1st November 2014 to 31st December 2014 to the test set, and the period 1st September 2014 to 31st October to the validation set. All other time periods are designated for the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Insert code START\n", + "valid_start_dt = '2014-08-31 23:30:00'\n", + "test_start_dt = '2014-10-31 23:30:00'\n", + "# Insert code END\n", + "\n", + "train = energy.copy()[:valid_start_dt]\n", + "valid = energy.copy()[valid_start_dt:test_start_dt]\n", + "test = energy.copy()[test_start_dt:]\n", + "\n", + "assert train.index.max() == pd.to_datetime('2014-08-31 23:00:00')\n", + "assert valid.index.min() == pd.to_datetime('2014-09-01 00:00:00')\n", + "assert valid.index.max() == pd.to_datetime('2014-10-31 23:00:00')\n", + "assert test.index.min() == pd.to_datetime('2014-11-01 00:00:00')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preparation\n", + "\n", + "For this example, we will set *T=24*. This means that the input for each sample is a vector of the prevous 24 hours of the energy load.\n", + "\n", + "*HORIZON=3* specifies that we have a forecasting horizon of 3 (*t+1* to *t+3*)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "T = 24\n", + "HORIZON = 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data preparation - training set" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# Fit a scaler for the y values (to use to unscale predictions)\n", + "y_scaler = MinMaxScaler()\n", + "y_scaler.fit(train[['load']])\n", + "\n", + "# Also scale the input features data (load and temp values)\n", + "X_scaler = MinMaxScaler()\n", + "train[['load', 'temp']] = X_scaler.fit_transform(train)\n", + "valid[['load', 'temp']] = X_scaler.transform(valid)\n", + "test[['load', 'temp']] = X_scaler.transform(test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the TimeSeriesTensor convenience class to:\n", + "1. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n", + "2. Discard any samples with missing values\n", + "3. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras\n", + "\n", + "The class takes the following parameters:\n", + "\n", + "- **dataset**: original time series\n", + "- **H**: the forecast horizon\n", + "- **tensor_structure**: a dictionary discribing the tensor structure in the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) }\n", + "- **freq**: time series frequency\n", + "- **drop_incomplete**: (Boolean) whether to drop incomplete samples" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from common.utils import TimeSeriesTensor\n", + "\n", + "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n", + "train_inputs = TimeSeriesTensor(dataset=train,\n", + " target='load',\n", + " H=HORIZON,\n", + " tensor_structure=tensor_structure,\n", + " freq='H',\n", + " drop_incomplete=True)\n", + "\n", + "\n", + "X_train = train_inputs['X']\n", + "y_train = train_inputs['target']\n", + "\n", + "assert y_train.shape == (23350, 3)\n", + "assert X_train.shape == (23350, 24, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data preparation - validation set\n", + "Create a `TimeSeriesTensor` from the validation dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to allow T lags, we need to add the last T-1 train samples to the validation set\n", + "valid = pd.concat([train.iloc[-(T-1):], valid])\n", + "\n", + "# Create TimeSeriesTensor\n", + "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)\n", + "y_valid = valid_inputs['target']\n", + "X_valid = valid_inputs['X']\n", + "\n", + "assert y_valid.shape == (1461, 3)\n", + "assert X_valid.shape == (1461, 24, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implement a multivariate CNN" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras.models import Model, Sequential\n", + "from keras.layers import Conv1D, Dense, Flatten\n", + "from keras.callbacks import EarlyStopping, ModelCheckpoint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fill in your code below and replace the question marks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Implement your CNN model with the data prepared above and the following requirements:\n", + "1. Use 2 features: past load and temperature\n", + "2. Stack 5 convolutional layers of kernel width 2 with dilation rates 1, 2, 4, 8, 16\n", + "3. Use 5 filters in each layer\n", + "4. Train for 10 epochs\n", + "5. Batch size 32" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Insert code START\n", + "LATENT_DIM = 5\n", + "KERNEL_SIZE = 2\n", + "BATCH_SIZE = 32\n", + "EPOCHS = 15\n", + "# Insert code END" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Fill in your code below\n", + "model = Sequential()\n", + "# Insert code START\n", + "model.add(Conv1D(LATENT_DIM, kernel_size=KERNEL_SIZE, padding='causal', strides=1, activation='relu', dilation_rate=1, input_shape=(T, 2)))\n", + "model.add(Conv1D(LATENT_DIM, kernel_size=KERNEL_SIZE, padding='causal', strides=1, activation='relu', dilation_rate=2))\n", + "model.add(Conv1D(LATENT_DIM, kernel_size=KERNEL_SIZE, padding='causal', strides=1, activation='relu', dilation_rate=4))\n", + "model.add(Conv1D(LATENT_DIM, kernel_size=KERNEL_SIZE, padding='causal', strides=1, activation='relu', dilation_rate=8))\n", + "model.add(Conv1D(LATENT_DIM, kernel_size=KERNEL_SIZE, padding='causal', strides=1, activation='relu', dilation_rate=16))\n", + "model.add(Flatten())\n", + "model.add(Dense(HORIZON, activation='linear'))\n", + "# Insert code END" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you done, run the rest of the notebook to check if your model works." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "conv1d_1 (Conv1D) (None, 24, 5) 25 \n", + "_________________________________________________________________\n", + "conv1d_2 (Conv1D) (None, 24, 5) 55 \n", + "_________________________________________________________________\n", + "conv1d_3 (Conv1D) (None, 24, 5) 55 \n", + "_________________________________________________________________\n", + "conv1d_4 (Conv1D) (None, 24, 5) 55 \n", + "_________________________________________________________________\n", + "conv1d_5 (Conv1D) (None, 24, 5) 55 \n", + "_________________________________________________________________\n", + "flatten_1 (Flatten) (None, 120) 0 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 3) 363 \n", + "=================================================================\n", + "Total params: 608\n", + "Trainable params: 608\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.compile(optimizer='RMSprop', loss='mse')\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specify the early stopping criteria. We **monitor** the validation loss (in this case the mean squared error) on the validation set after each training epoch. If the validation loss has not improved by **min_delta** after **patience** epochs, we stop the training." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 23350 samples, validate on 1461 samples\n", + "Epoch 1/15\n", + "23350/23350 [==============================] - 2s 91us/step - loss: 0.0129 - val_loss: 0.0034\n", + "Epoch 2/15\n", + "23350/23350 [==============================] - 2s 76us/step - loss: 0.0047 - val_loss: 0.0028\n", + "Epoch 3/15\n", + "23350/23350 [==============================] - 2s 76us/step - loss: 0.0038 - val_loss: 0.0025\n", + "Epoch 4/15\n", + "23350/23350 [==============================] - 2s 76us/step - loss: 0.0030 - val_loss: 0.0017\n", + "Epoch 5/15\n", + "23350/23350 [==============================] - 2s 76us/step - loss: 0.0024 - val_loss: 0.0019\n", + "Epoch 6/15\n", + "23350/23350 [==============================] - 2s 77us/step - loss: 0.0020 - val_loss: 0.0011\n", + "Epoch 7/15\n", + "23350/23350 [==============================] - 2s 78us/step - loss: 0.0018 - val_loss: 0.0011\n", + "Epoch 8/15\n", + "23350/23350 [==============================] - 2s 79us/step - loss: 0.0016 - val_loss: 0.0014\n", + "Epoch 9/15\n", + "23350/23350 [==============================] - 2s 79us/step - loss: 0.0014 - val_loss: 0.0027\n", + "Epoch 10/15\n", + "23350/23350 [==============================] - 2s 78us/step - loss: 0.0013 - val_loss: 0.0011\n", + "Epoch 11/15\n", + "23350/23350 [==============================] - 2s 79us/step - loss: 0.0012 - val_loss: 8.8262e-04\n", + "Epoch 12/15\n", + "23350/23350 [==============================] - 2s 79us/step - loss: 0.0012 - val_loss: 6.2985e-04\n", + "Epoch 13/15\n", + "23350/23350 [==============================] - 2s 80us/step - loss: 0.0011 - val_loss: 0.0013\n", + "Epoch 14/15\n", + "23350/23350 [==============================] - 2s 80us/step - loss: 0.0011 - val_loss: 6.1369e-04\n", + "Epoch 15/15\n", + "23350/23350 [==============================] - 2s 80us/step - loss: 0.0010 - val_loss: 0.0013\n" + ] + } + ], + "source": [ + "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)\n", + "\n", + "history = model.fit(X_train,\n", + " y_train,\n", + " batch_size=BATCH_SIZE,\n", + " epochs=EPOCHS,\n", + " validation_data=(X_valid, y_valid),\n", + " callbacks=[earlystop],\n", + " verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_df = pd.DataFrame.from_dict({'train_loss':history.history['loss'], 'val_loss':history.history['val_loss']})\n", + "plot_df.plot(logy=True, figsize=(10,10), fontsize=12)\n", + "plt.xlabel('epoch', fontsize=12)\n", + "plt.ylabel('loss', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the test set" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to allow T lags, we need to add the last T-1 validation samples to the test set\n", + "test = pd.concat([valid.iloc[-(T-1):], test])\n", + "\n", + "# Create TimeSeriesTensor\n", + "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)\n", + "y_test = test_inputs['target']\n", + "X_test = test_inputs['X']\n", + "\n", + "assert y_test.shape == (1461, 3)\n", + "assert X_test.shape == (1461, 24, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
timestamphpredictionactual
02014-11-01 00:00:00t+12,295.072,434.00
12014-11-01 01:00:00t+12,291.662,390.00
22014-11-01 02:00:00t+12,196.852,382.00
32014-11-01 03:00:00t+12,234.752,419.00
42014-11-01 04:00:00t+12,445.992,520.00
\n", + "
" + ], + "text/plain": [ + " timestamp h prediction actual\n", + "0 2014-11-01 00:00:00 t+1 2,295.07 2,434.00\n", + "1 2014-11-01 01:00:00 t+1 2,291.66 2,390.00\n", + "2 2014-11-01 02:00:00 t+1 2,196.85 2,382.00\n", + "3 2014-11-01 03:00:00 t+1 2,234.75 2,419.00\n", + "4 2014-11-01 04:00:00 t+1 2,445.99 2,520.00" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from common.utils import create_evaluation_df\n", + "\n", + "# Make predictions\n", + "predictions = model.predict(X_test)\n", + "\n", + "# Package for evaluation\n", + "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n", + "eval_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate MAPE for each horizon" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "h\n", + "t+1 3.26\n", + "t+2 3.51\n", + "t+3 3.68\n", + "Name: APE, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n", + "eval_df.groupby('h')['APE'].mean() * 100" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4QAAAHjCAYAAAB7INHwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydeXRkV33nP7f2KrWW1tL75qW9YYzNFkPCMmGfQBKWQ8IkEwInbAkZkvEwA2QSQggZZkgYCGENCZCBA1kIBAyYODaYJWBjvC/d7m63u1utbrVU2lWLVFVv/rjvvnfvqyotbVVJrfp9ztFR1atXpSddvffu935/i/I8D0EQBEEQBEEQBKHziK33AQiCIAiCIAiCIAjrgwhCQRAEQRAEQRCEDkUEoSAIgiAIgiAIQociglAQBEEQBEEQBKFDEUEoCIIgCIIgCILQoYggFARBEARBEARB6FAS630A7WBwcNA7cODAeh+GIAiCIAiCIAjCuvDTn/503PO8oej2jhCEBw4c4M4771zvwxAEQRAEQRAEQVgXlFInGm2XkFFBEARBEARBEIQORQShIAiCIAiCIAhChyKCUBAEQRAEQRAEoUPpiBxCQRAEQRAEQRCEzcLi4iLDw8OUSqW61zKZDHv27CGZTK7os0QQCoIgCIIgCIIgXEAMDw/T3d3NgQMHUEoF2z3PI5/PMzw8zEUXXbSiz5KQUUEQBEEQBEEQhAuIUqnEwMCAIwYBlFIMDAw0dA6bIYJQEARBEARBEAThAiMqBpfb3gwRhIIgCIIgCIIgCB2KCEJBEARBEARBEIQORQShIAiCIAiCIAjCBYbneava3gwRhIIgCIIgCIIgCBcQmUyGfD5fJ/5MldFMJrPiz5K2E4IgCIIgCIIgCBcQe/bsYXh4mLGxsbrXTB/ClSKCUBAEQRAEQRAE4QIimUyuuM/gckjIqCAIgiAIgiAIQociglAQBEEQBEEQBKFDEUEoCIIgCIIgCFE8D6rV9T4KYTVUq/CjH8HMzHofyQWFCEJBEIROplyGUmm9j0IQBGFj8eij8JSnQE8PvPOd6300wkpYXNRj9sxnwq5dcPjweh/RBYMIQkEQhE7lpz+Fgwchm4UbbtCr4YIgCAK8//1w991QKOjH//qv631EwnJ89atw77368fw8/NEfre/xXECIIBQEQehU/tf/glOn9OMPfhD+8R/X93gEQRA2Cnfc4T7/0IfW5ziElfPgg+7zb39bu4bCsoggFARB6FQOHXKf33CDXlUVBEHoZDxPh4zafOtb8Mgj63M8wso4etR9Pj2t8wmFZRFBKAiC0KlMTLjPh4e1aygIgtBheB6cPu2viU1MwOxs/U4f+Ujbj0tYBVFBCPDNb7b/OC5ARBAKgiB0Ip4H+Xz99g98AI4da//xCIIgrCMnTsCRI74xGHUHDZ/9rHadhI1Jo3vXN77R/uO4ABFBKAiC0IkUCrCwUL99YQF+//fbfzyCIAjrxPy8FoSgCy83FYRzc/C3f9u24xJWwfQ0jI/Xb3/gATh5sv3Hc4EhglAQBKETaeQOGr7+dZ0vIwiCsMnxPN2dwBRZXlwEjh9v/oa/+ivpTbgRWSqyRcJGl0UEoSAIQicSzR+M8ra3NXYQBUEQNhEjI7qHeSqlny8s0NwhxH9NwhDXhWoVRkehVmvwYqP8QYMIwmURQSgIgtCJRAXhrl2gVPj8yBH46Efbe0yCIAhtpFQKtd9ll0E8rp3CyrET7o579rjPP/zh9hyg4HD2LDz8cBPtt5RDeMsterCFpoggFARB6ESiIaNPfzq88Y2USXEbz+YQl8MXv7g+xyYIgtAGjhzRrtPQEAwOQjKpty8+esrdMVp9+dZb4f7723OQQoDRdGfPNghgWcohLBTgtttadlybARGEgiAInUjUIRwYwHvXH/A2Pswf88e8hY/zr/ftaBKbI6wrn/oUPPe58O53h4lPgiCsinPn9LpYIgEHD+ptySRQrbJ48oy788tfDs96lrvtr/6qLccphJjUzVpNh/o6RAXhvn3Bwwm28sgXfiKXyyUQQSgIgtCJRAVhfz9HS3s4nHhCsOkb5edJdbaNxk9+Am96k17t/pM/kdA1QThPTvkm4CWXhPmDySQwNsZizZoeb98OXV06r9rmllvacpxCSKUSPj59OrJeGQ0Z/Z3fAeB2ns6r+Cfe9E/P573vbf0xXqiIIBQEQehEoiGjAwOMjSs98fEZYwgefLDNByZEOX7cqnFx883uix/8oDtLEgRhRZiQw61bw22pFDAywiLJcOPFF+vvL3qR+wEnTsi512bMnzsW09Vgz571XygWtUI0xGLwW7+Fl0zxIX4PDwXFIt/5RsHZTQgRQSgIgtCJNHAIJyeBXCgIp+kVQbjOlMt63nnypD8Zijq2p07pNiGCIKwKIy4SiXBbMgmcOeMKwosu0t+3bIFt29wPGB5u+XEKIWbMdu7U34eH/aj5aFXYvXuhv5/7n/KbnGVHuH0iL4KwCSIIBUEQOpGoQ9jfz9QUjkNYIMfiA4fbe1yCw9RU+HhxkTDOzUZymQRhVXhemI8Wj4fbjSBcIBVuNA5h9DEs3a9QWHNsQZhO61oxExPU5w9eeikA3x76dXd7Pr9kC95ORgShIAhCJ9KgqMz0NNCVczbP3B8pvy60lcnJ8PHiIo1zOm+9FR56qG3HJAgXOrYYtLvtJJM0DxmNPoal+xUKa44Zt2Qy7ARy6hT1+YOXXEK5DN+duc7dPjVF/rS0n2iECEJBEIROpFnIqOUQAkwdbtYFWGgHtiBcWKCxQwjSM1IQVoERFna4KCwTMgoiCNcZ4xDG49oljMd1FMXsQ5Hr4qWX8oMfQEFtgUw23O55jN8rMaONEEEoCILQiTQoKjM1ha6qYM2SpotJqTS6ThQKOofQsDgxi7ZxG/C5zzV/TRAEh0b5g9WqXm+ZG5kWh3ADEg3zTSRg1y79fPihGXfnSy/l29/2H/d0Oy9NHJ9CqEcEoSAIQqfheQ0dQp2vppzCMlP0SWGZdcJ2BwEWTpxpvCPA/LwWhcLGpFaDm26C731vvY9EwHWaDLfcAv/0hTJfm3kOs/giIpmE3bvDnUQQrhu2iDdhvrt368fnHitQtSRNfuAy7rzTf5Jzo17GTxbbcLQXHiIIBUEQOo35eT8hzSeTgWw2LGDSFak0Kvlp64IRhDk/rbOuWXaUj35Uwns3Kr/5m/CSl8BzngNveAPSIXt9aRQyeugQxGenWCDNSfym5vv3u6rRDh8FEYRtpNGYZTKQTVbwRs9RIhNs/7fjl4SnWCQvPn9OWoU0QgShIAhCp9EgXNTzrIqW1g1UWk+sD/Z4bN+uvy+cGnV3euELIZnkOAd4Cx/j1x75I777Fz9t74EKyzMzA1/4Qvj805+GP/zD9Tseoc4hrFT0+aamJgGPUbZRQ9U7grt3+4mGPuPjenyFltPI1QVIjY9ArRpUhvW27+Dbt4XiMOoQTkzFZD2mASIIBUEQOo0G4aKFgtVjuSsSMioOYduZm9Pjkc1Ctx+9tjgy5u70tKfBq17FX/FWDnEFI+ziA38OJSmit7EYbVCY6X3vg09+cn2OR6jLIZyc1EOkpiaJU2OcIZ1HGHUE43E4cMDdJq0n2kKjvE+A9FldCbtMGoCje57rDEmsK0tKhRExlXKV6ZOSbx1FBKEgCEKn0aDlhN3vrq45/UMPSShimzHhon19us4PwMLpiCDct4/Ft/wX7uOaYFPh3Byn7o2Mr7C+NGt89tu/DV//enuPRQDqww/Hx/0XJieJUSVPPwsk6x1CkDzCdaKZIEyNPAYQOITfTvyC8/rTr4+xo6fgbMvfKSI+ighCQRCETqNZU3pDOqw0Ok2vzjmUSqNtxQjCrVvDCLXFs5Fx27uX49t+hkpXr7N54gfi6G4omgnCWg1+5VfgjjvaezxCXfhhMERTU8SpsUiKUbavTBCKQ9gWmrUKSQ/rHoRl0lSIc8vMU53XX/QiGBhy40zz9zRp39PBiCAUBEHoNJpWGDWElUan8cWGhI22jVot7CARFYRO6su+fTxyREFvn/P+iZ/KBHVD0UwQAhSL8NKX1p+TQktZziEEOMU+J2S0VILPfx7eeNebeDv/hxF26hfEIWwLTR3Ck0cB7RDew7VMqa3Ba11d8MxnwuDerPOe/EORfGxBBKEgCELH0awHoY2fRziFLzaksEzbmJ7WonDLFi0GYzFIxGp4Y+NUsGZDe/dy+DDQ0+O8P//AMtVIhfYSPd+iOWhjY9IypM1ExcX4OLqS06TOIQQYZjdcfDGVCnzta/BrvwZ/8zdwZG4Xd/JUPsmb9JtFELaFZkVl0iceAbQgPMF+nXjt87M/q0PuBy52oyjGj0kvwiiJ5XcRBEEQNhXLOoQElUan6cUDlDiEbcOMxVZ/obtWg8TcJJXFBRZJkqSiRWBPD0eOUNd4OX90Uk9uTbMuYX2JCsLXvlbHBP/lX4bb7rmnvcfU4djiwvN8QTg3B9UKcd8hHElfwg8f2sonPgHDw9abs7qC5d1cp6+NIgjbQkOHsFYj9dgjwLWUSeswX0sQ7t2rvw9cuQ2YC7bnT7o5hYI4hIIgCJ3HigShdgirxJmnSxzCNmLnD05NwYc+BH///xaZpiconMC+fVQqcOwYulGhtWyeL2b9F4QNQQNHnpe9zN126FD7jkdwQkYLBb8y7+QkcapByOjtmWfxB38QEYMAGS04ZunWIfXHj0vRrTbQUBCePg0LJcqkWCDFWHYfJMK2INu26e+DT9zpfFZ+UunceCFABKEgCEKnsZKQUak0ui5UKjA7q8293l64917d5qw8WeAwl+tS+KALyhw3kyQF3WHY6AT9cPvt63L8QgMaCcIrr3S3PfywNKtvI7ZDGOQPTk2xnVH60BfD497FLCw0eHMiEaiSk+yDhQUYGWn9QXc4jYrKTNz1GB/i9/kHXs0wuxnpOui8x/Rw7d+RcpzDcQZlESZCWwShUiqjlLpDKXWvUupBpdR7/O3fV0rd43+NKKW+6m9/rlJq2nrtj6zPerFS6rBS6qhS6h3tOH5BEIRNxUocwnQK4vrOO0WfXk09JZXZWs3UlNYFPT16smqKy8TnZ5iiz3EIH3nEeqOVR5hnQCpXbiQatHlh166wwSToVYAzkvvZLmy3yS4oM0Ce3ZwGoBDf4gjCF74QnmoKWPri4iT79HMJG205jXII7/zuHGXSxPB4gKsZTe9z3hM4hIM4i5wT9OtFGCGgXQ5hGfh5z/OeBFwLvFgpdb3nec/yPO9az/OuBX4E/LP1nu+b1zzP+xMApVQc+CjwEuAq4DVKqava9DsIgiBsDpbrQwiACsJGg0qjEjbacmZm9Pe+Pvd5bG6GSbY6DuGRI9YbrTzCPAN4t4sg3DA0cgiVgiuucLfLBLVt2G5TMDyTkwwyzl70wlfRy1Iuh+9585vhCU/wn2REELabRiGjRx7UAxSnwgi7OedtC15TyheC6FPO5MWDf418UPLibdoiCD2NyeZM+l9BbIRSqgf4eeCry3zU04Gjnuc96nneAvAl4JdacMiCIAiblwZ9CE3emoNVWAaQ1hNtwDgSGV23wnIIpymSYwZf+O3bpyuMGqyQ0UWSzN19hMbxbkLbaSQIgfLBq3mM/ZSN6yuCsG00DBm1BGGVOAsqFZxCiYRepNlnDCi/sEwgCKUXYcuJCsKpKRgb1gMUp0qRLPOx0AW0W/ak09C1NR28ViXO9H0n2nLcFwptyyFUSsWVUvcA54CbPc+zExx+GbjF87wZa9sz/BDTbymlzJrMbsCOWRr2tzX6eW9USt2plLpzbGxsDX8TQRCECxjPq3MIva39gfBwEIew7ZhJj5nIGIcwPqsHaBSdFFPZtc+tG5NOQyqc8EwsdMH997f6cIWV0EAQTk7C6x+8gdfxGX6LT2uhLzlNbaFW019K1QvCAfLs4yQl0hCPUy7rffv79f6BIPQdwlP4ZSzFIWw50RzCI0cI7mUJqhTJUPBCQbhtm/v+wb0Z53n+wbOtOtQLkrYJQs/zqn5o6B7g6Uqpq62XXwN80Xp+F7DfDzH9CMs7h41+3qc8z3uq53lPHRoaejyHLgiCsHmYnQ1VB0AuR6GWcTYFiCBsO4uL+nsiAeUyQchafFbH9BpBeCJ2UbBvQCRsVPIINwClki5jaUgkoLubf/1XGKnqGeswe/g3ni8OYZuwhUWl4rd5qVRQszMMkKeLIkkWIRbH8/Q5aUIPTRsDk0N4hp0skBRB2AaiOYRHjhD06IlTpUSGYjUZjG9UEA5cFOlFeNKUlxVgHaqMep43BXwHeDGAUmoQHQr6DWufGRNi6nneN4Gkv99pMMsxgBaXp9t06IIgrIJKBT7+cXjLW+CLX5QCehuGBgVlGoaLQpCEHzSnf+ghGcgWY0ReMhm6g1SrxOb1kzH0Aucjszvr3xwtLCOVRtefJvmDp06h24X4nGKvOIRtwhYWk5N+8eTpaXqZ0j0+gWwuBkrheXpRxgjCdNoXGn5Mt4fiNLtFELYYzwuFfDyux/D4kQqUtaDbygRFstRUPFh/MRVGDQM7kpAOXcK8txW3Mldn064qo0NKqT7/cRZ4AWCufK8CbvQ8r2Ttv0Mp3VFXKfV0/zjzwE+Ag0qpi5RSKeBXga+143cQBGF1fPvb8A//oOc4n/qURK9tGBoUlGkYLgpBpdHAIZyfh5MnW3p4nU5DQTg7S9yfqI4xBDt28MjxpPM+pZBKoxuRJvmDY2Nol0lPdXQZ/JERmp+MwlphO4TRcFFDsidsUWALQvDDRjNpQI/dSfbB2bOuEyysKXb+oFLw2GOwOK3/3oOM83TuoBTvxvNUMAzR4MBGhWUkLz6kXQ7hTuA7Sqn70KLuZs/zbvRf+1XccFHQIvEBpdS9wF8Cv+oXpqkAbwW+DTwM/IPneRLDJAgbkLvucp+LINwgNCgoE60wGhoXCrpyoUMIcgNtIZ7n5hAGgnB6mhg1wCPPALXde+sWtq+6CqeNwQT9ejVGBMb60kQQjo8DKhaEHo7jKw6nUpDQChoWlJmdZZDxYJ94t74Iep6uzVQnCFXMF4VWHqEUlmkZDfMH53StyoMc4Rruo5TQES3Fot6/ziEcIEiDABGEUdpVZfQ+z/Ou8zzvGs/zrjZtJPzXnut53k2R/f/K87wneJ73JM/zrvc879+t177ped5lnudd4nne+9px/IIgrJ5oyzqp7bRBWEHI6IED1pNcLnQIQUKjWkiloiegZhXcFoQKiFOjTIb8jqvcgjLAM56B7hvph/nmGdAf9tOftvNXEKIs5RBCsPoSCELJI2w5ttsUDM/MjCMIa11bAIIcwl7rEhgtLCOtJ1qPPWae50d6zs8DWhBu5xxeIonn6RDgYrFBUZlIL8JxBuV8s2h7DqEgCJsfz6sXhNF5kbBOrCBkdP9+60km6zqEJ6RUd6toVmHUPIihl8nvTF7v9Efr64PLL/ef+IVl8mjhIWGj60wDQVgu69pOQDBBnaCfGkryCNtAw5DRiCCcS/QHbpTnuanTYesJEYTtwnZ1JyZ07ifz86RYYB8nieGhrI718/OuIJyd9cdQQkabklh+F0EQhNUxPl5fvGt8vPG+QptZQQ/CvXu1Q+V5QCZNgRwV4iSo6uQNoSXYFUbBivacnqaLeeLUWATuLj3Bed/ll+uy+IDuR3j2bCgIpbDM+tJAEDrXQt8hrBFjkq0MiGPRcoy4iMVcQWjnEE7TSzIZtvK0K/o26kXoAUoEYcuwHcIjR/yN8/NczKMkqFIgSyylPS5TCCiX0+/73vfgBz/Q19OZahcm0zrPgLYaFxfDVbgORhxCQRDOi6kpHW1hVlsBfcWdm6tzB0FCRjcMDRzCaA5hf78VIuVX0wvCRsUhbBl2QRlwQ0Yv4Rhx3yF8aH6v877LLgsiEYPCMhP4ClEcwvVlOUFoORZjDIlD2AaMuFhcDBcuU3MTdKNtWw+YqnQ5GsGPTgT09TGXIwgZLZLV55vkELaMZoLwIPrJGEOk0rEgpDSXg9tug098QgvCWk0buoVyIujXOkE/XqUCR4+uw2+08RBBKAjCeXHiBIyOWkLvve/VsWt79nDqX+6q239iwi/vLawvDXIIoyGjfX0iCNeDpoJwZoZLORqEjD461e+877LLtA6Mx4EtXaBiFMjp5tojI3BaujOtGw0ceWdxLBcJYTt6NLSlhJZgFjGD8wsYnDvu1wzV17paIk0yqcVFMgnHjsGZM/r1oEF9NmxhcJJ9EjLaQsyY1WpWkMrcXCAIR9mOSibIZPSYdXXBj37kRiYlEv611V+EqRLX9zUJGwVEEAqCcJ4Ui/p7oYCe9PyJXytqepqTn7qpbn/Po3m/O6F9rCBktKfHEoRpvZoaCMLR0XDwhTXFFoQLC6F7EZ+e4ACPEaeKB5yZzDo5TXv3wqc/rRdnSuUYdOuCGJJHuAFYziGMh47FOIN65hutGCSsKcZtChbCKhUG5sKFrvHYdlQ6RTKpL3+5nBYiX/96uKipW0+ErSkCQSh9WluCGbMzZ0JxuL1ymh7f1T3HNmLppEnrtIuJYqUW6tejhWVEEAIiCAVBOA9qNYKiFsUiOszJXLGBU2cTsFCue5+EjW4AlgkZLRbhi1/Uq7Cehy6vnk67hWWkF2FLaNhyYmGBntIo3czSxTzzdLNQSwSTor4+Hal2+rR+3+wsQdioCMINQANBWHcd9F3CMfzGaZJH2FLqHMJIy4n8wGWomPYLe3pgi15fYWQEfvIT/XjvXiCZCNTGSfbpi+foaBt+g87DXBvtW8/B8gPB43NsI5ZKEI/r66ARhJdfDm94Q/ieVAopLNMEEYSCIKyacjlcCC0UqMudOMXehv3PpNLoBiAyCN5WN2R0ejos2x0UBspk3NYTEjbaEuyiMnb+YA8zKGAb55jObMPzVBBVeOmlcPfd+rFxMkw/wiCP8N572/UrCFFWIgi7Iq0nJI+wpRhxESyERSqMjm29DOXHj8Zifo9Pn1tu0eemLiyjpNJomzBjZtcnODgbpqacYxsqpWPt+/rgaU+D17wGfvVX3Wqj6TR42UgvQjnfABGEgiCcB3bEYLEI3qOhICyTYpTt1FUqQSqNbggiDuF8ZsA2dwEtSDIZq2BQJiOtJ9qAHTJq5w/2oJ/s4CzFZG/QGw10aK8R9NmsP2a+4xQ4hDJe60cDR96+DtZqSC/CNmOud0GofKTC6Hj3RcRiYaXl666DId+8XViAb31LehG2m2pVnyt+L3oUNfbm7wleNyGjoE3bF75Qu4NKaVHvp8KTyUAtGzqE4wzC4cNS4AARhIIgnAe2IKzVoHR0OHh+mt14KBGEG4CxMW3eBk6f59VNUKfU1uCxaYoO/o3T3COjDqG0nmgJDQWh7xAC7OQMxYR2/4xDaBfGCBzCtJ79OIJQcpvaT63WsIiTcQjzeR0CN1HV55Y4hO2hWtVf5txRs64gzGd26+2+Szg4CC99afj+hx/WodlKESiNc2zTRZxk8aUlVCr6mmfGJOMVidXClczRxJ6gD2GtVt+U3uQW5nJQiyeDG12eAT2haVQavcMQQSgIwqqJ1hQpPHo2eHwKvyT+/DxUFp39RBC2l6NH9fzk9tvhwQdhZnjG7RPS1cVUMR08rVaDGjKuIEyLQ9gOmgnCXrQFuIsRSrFs4BBWq+66Szbrj1kqCbFYGDI6Py8VndYDE39t6O6mGk8FFZdNs+yZxSweEUEoAr5lVCqWoAN6y+dIEoqLseROwHq9F/bvhyc/OfyMm2+G7dsJrSf8e5/kV7cEIwhjvmrJLM4Gr3nAWHJX8JrnLSMIa6o+ikIWYUQQCoKweoLeTSn9vfhYmEgfCEKocwlFELYXu3r92Bjc9YMCd3Mtc/g5FJGCMpVKOL9Jp625bFZyCNuBLQjtpvShQ3iWEnpms7Cgw6eyYaHDQBB6noJ0JpzsgIzZetAgf3ByUo9RtRpqPi8WpxZLME+Xdpnm5qRVSAupVPSCixEQA8Vh5/VxTwtz87pfo4kXvCAsVjIz4xebyYQLaiIIW0elomsXmDHJlsPE9yn6WEzmnPDQaJ95c51sGlYvglAEoSAIq8c4hP39QKVCYSRUFa4gdAvLiCBsH5WKnnDG43D99TrnJVGYZppeTrBf79Tf7wjCajWSa2E5hBIy2noaVhm1cghzFFhUeqZTq2nRaJdXz+X0mHsekMmEDiGIIGwxExMNurEs0XLCzdtV1HI6FFjyCFtPteqKi+4ZV3zn/RBe4xAaQZjNwpVXhvv19xOEZ4OfRyiCsCVUqxGHsBTeuM6xTUdFoMesr6++lafrEBK0ngjOt8OHW3n4FwQiCAVBWDVm4jMwAJw7R8GLhM0YpsUhXC+M25RKabfv4ovh6q0jACzgW7sRQWg7hG4OYdoVhCMj4Q8Q1gRTNCEW01+Ncgin6SWe1Hkynqf3Syb1GGez4aSnVgMy4hC2i2IR7rsP7rknUptiiQqjduQ2hIUugtYT4li0BOPMLi5a+Wj5UBAukmBqUYsFpfSXaTsBgbEEaOFhO4SBIJRw3zWnLmS0EObmakGo72mxGGzdGrbFMtgOoV3IaYJ+PJDzDRGEgiCsknJZX1CTSb+6/ZkzFNAXV4+IIJydhWq4FD43V3+hFlqDWSG1Q2dSs3qCGgjClYaMxuJMp4YIpjm1Ggy7YVbC48MOF11c9BddPI/4zCRdzAN64pPIhoJwyxY9Yb36av04k9HPtSDUIr6C35VZBGHLMNe0clmvlQQsIQijlX1rmYhjIQ5hSzBCPBDktRrpiTPB63kGgkRqpfQ9zhb5tiPf04M+YX2VcpJ9+iYn+bpriufVO4TZQnhunWMbJPU9bTmHMJHwtaMvCGvEdH68CEIRhIIgrA6TP5jN6vtmbPQMC6SoEGeaXuYIl1PTlBn0XFtQXML2YAsMQ2paz0ZX4hAmwp7L+rX0lkD4AxI2usY0LChTKtFdmcA3MhhN7yeVCQfFTE6vu06fjyZ/plolGMggbFQEYcuwzfITJyyxsUTIaJ1DmNYzVqk02lqMEA8E+fw8mdp88Hq+52KI6XMsFtMFZezxtR1C7Ryq4FwbZo9eNJOw0TXFjJV9zmRmw2aeo2wPQkabOYT2uHV1AdlMYBGPMwhnzjTsnUy+AaAAACAASURBVNxJiCAUBGFVmHBR40bkxvXNr0Au7MXks5dTDM0/5mwTQdgeGgnCxHSeGDWqxKkSW9IhhLDiKCDN6VtMQ0E4NxeEiwKMbr2CVEpPYjxPT2yGhmDPngYhUSII24bt9i0uWub5CnIIe/1Tqk4QikPYEurExewsGUrB62MDVwSPlaoXhLZD6Hl+lIyfR1gmrd0qEYRrSp2IxxWEYww5IaNLOYTgi0MVCzYG18gOzyMUQSgIwqowgjBI0h7VTemLZHW4aF/Y124vpxgccyc2IgjbQyNBePbUIt/jWTzCQRZJLllUJvpeaU7fWhoKwtlZRxCe23JxMCZGED75yXriasYtEISNehEKLcFMVLt1XRhOnfLHcwU5hIO+/qsl06BUKAjPntUh98KaUhcyOjPjCMJ878XBYyMIbXFhO02Fgt+gvlEeobBmmLGyBWF2KgzzHWW7EzK6VA6h89gfTHHlNSIIBUFYFVFBmB05BmiH8BR7YceOYN+9nGJo5F6ohbEeIgjbQyNBeOP9+znJPv6dZ3KOIUcQmlL49o0zKgjFIWwdDSuMRgThaHoviUTQU5neXrjmGv24ropeOgWoUBCOj+t+hMKaY8ZucFBPRisVv8/1CkJGh/waMjUvBul0ODkFaT3RAsxYBa5fRBCOd4VRLsuFjM7P+4LQqjR6mt1ybVxjGjqEU2HvY7vKqAkZXcohDCJfoq0nxCEUhAuEs2flQrsBsHMIAXLDjwCWIOzthYx+cR8nGaids2a4IgjbhV1l1DwfmUgTpwoone/SH4aMmputHSaaStmVRiMOoeQQrilmvBIJK5VldjZoSg9wLr4zCInK5eDVrw5D2OocQqUFhlNpVJyLlmCP3cW+wTQ8DAtjbk6St7WfsbGwSAZYDmENSIkgbDV14mJ2ljShnTSW2hM8bhQyagvCYtFvgG45hOMMynm2xtSJeCAzoas3VYjrkE//RheP6zFbyiEMFjqlF6GDCELhwuCDH9RLcQcOwLvfvd5H09E4DmGhQG5ci3SdQ7hf3xz7tJO0l1MMMu40qBdB2B6iDuHZs1ArlH1BCGfYyVx2yAnHsd0niFQaFYewpawkZHS0NoRSeox6e3W4qKEuhxCkF2GbMBPWREKHjQ4O6jE4cTbt7DeXHaJcDlsfJBJ+6wKMq6sFfFDNVwThmmOud80cwnxsKHhsC0LTSSKRCBfNajU/TNiKsx9jSAThGlMn4hcXyfhVRscYwlOx4MY1MKAf2mMGriAM7nHRXoQiCAVhg/Pxj8MNN4RX8Pe/33GchPZRqehhiMf1gpx3/DGyaIU4xxZG0ge0M+HPcvYwzBBjToN6EYTtIdp24swZoFAIBOFZdjCdDN2ISsW9aYKe5wQ31WgvwlOn6kslCufNcoJwgSST1Z6gd5rn+X1AfepCRgEy4hC2A1sQAlx0kf4+Mp6ihOUeqSFn/1wuLINvBGGVOJP4edjS2mXNWTZktBYuoCjlN5/HDVe0C8vYRWVABGErqBPx8/PBvGOMIT9/UF8Yt/ktCT3PDRuNCkLPA3J641n8NJcjR+r7wXQQIgiFjc2XvgS/8zsc4VLeykd4PX/D7QvXdnys93ph3MF0Gv7+7+H9/yfGv/F8YlQZY5DFjN9yorePfvKMsItDXM7i9Dx4epYqgrA9RB3CkRGgWCSOHocxhphSYQGgSiVSVZRIc/p4gunsTvcHnDmDsDYsV2V0jCFIp4M+XL29rptrh4wGOl2a07eF6Llmqr96M7OOQztW1Y/N+JjwwzDvU5+AwZiJQ7jmGHe2UcioB4yVe5z9TUhvs7DRXA7nwjnGkL4uRpPYhPOmTsTPzQUi3m45AbB9e5gmYQ9BLBYOU9BbMpGEZIrT7Nau/OIiHD/eyl9lQyOCUNi43HQT/Of/DJ7Hh/g9HuQJHOci/jf/g4WHjq730XUkRhAWCroqenlshh9zPTfyUg5xRVA6fSGeYS41yOf5dR7giYx5AzCjK+aNj7uhHEJrqBOEp2u+INSz0XEGGV8MHb9oQRmIhIwC0z173R1EYKwZ9oSzUNDfY7PTTlN6UmmUCkPZnKp7jUJG0yII20HUIQTYkihBuUSJsLHneLHL2d84TW4hIF9UgAjCFmCiXJQCPI/UzHiwSFYgR1mFbl8qFYb02uLCdggzGbTaSIVj53meuLtrSKUScfzm5wNBaDelB+0QGuHXLI8wnXYLy5RJSx4hIgiFjcoPfwiveAVUKhTI8hBXBS9NspWjd0ys48F1LqagTDB5nZwEoEiOW3ge+Wo/4+MwMhLG2iRZpEYMzxeElYpE/LYaUzHU5JstLMDYcBnwAkG4mOhieDRcWbV7EJobp+MQAlO5Xe4PEoGxZhiRYM4xPI/u2RFifkbZWXYEgiEW06eXU2TBHzs3ZDTDJFupmdb2Ml4toZEgTBf0tTEQhP39jOf1OEQdwq4u1yEMcppEEK45lYq+HsZiQLlMZtEqeJbeA8lwEAcHw/OqFEaVOg4h+MNm9SKcY4uEja4hRsSb6IhUaSYQ8XqhzBWEjRxCCO9rSmknEYAuPZjD+MWEOjj6TAShsPEYH4eXvjSwox7l4rpdDt0v4RjrgXEIgwutXywmySJzdDFTyzE7q1fzegbCG2uOIl7ZStyPVGMX1pZGBWW8gv77G0FYTed0aXwfO2TU3CyjgnA6HRZcAKTS6Bpixsy4g5RK9FTDha9zqb06eRc9oYk2XzaTnXjcEiaZDDViYe6nCMKWYLcMMWQKeuzKJoewQQ/CZiGjIghbR7VqCcJohdHBKzG5aKAFoXED7Y4t0V6EQ0NIYZkW4oh4IFO2Ki9bLSdgZYIQwlBgM5iBIBSHUBA2EJ/9rFOV8hiX+LWdQw4/mkJoP0YQ2g7hk7mLyzhMgS68WCgCn3BwgRx6dpujQLUY2hlmYiS0hmjLiZERAqUR8wVhLZ3V2wnL4JsbpjndoiGjUzEr/BBEYKwRtqMbCMJohdHuSzGTVdN82XYI7clOUAwo0yAnzX6T8Lgx4WzxOEHBH4DMvF71ChxCSxDaRWUgDPP1khFBePasjNcaY8SFUtRXGN16qbPv0FBjQWiHjIaCUFpPtApHxAOZ4mTwWiOH0Cxs2q4uuNfIoCCXfxKeZrd+LoJQEDYQ99/vPD36rNfDJZc42w6d65dEtHXACMIgNn9qil2M8Fo+R5oSsWScdFo7TG94zXzQQy1HgVo5nNhIYZnWEq0wagrKAOzjFOBRS2UYHdWvm0ltJqMT7s2EJyoI52NbqBAPN4ggXBNsR9fuQWgLwnNZt2F2tPlyPB6OdyAIY3FIpkJBWKuJ67TGNAoXBUhNa/W3QEoH/TZwCM15Zr7XiEEyGQpCz9OiUFgzHLcpWmG0+yJn34GBULQ3cwjn531BKJVGQyoVeOCBNbvROyIeyPotJ2ooRthVl0O4xa9tZ/kKgDtuJjdUHMIQEYTCxiNyQh7re7KemcbCf9dTi9spnBCbqZ3UaloIKuULwmIRyiV6mGGRJIoYg9vj7NgBPT2w89rtQWnoHAWqC2EFDAkZbS0NK4z6M5onca8OG82EgtBMUNNpXazERD8p5URC1fcilJDRNaFZywm7Kf1ofHfwuJFDCG5hmbDSaFp6EbaQZoIwNpknTRkPpcNGBwaCglq2Q9jVFU5UTdioNKdvHdWqlY8WEYRjGbdo1uCgPpdiMX3PixYDgsYO4RhDnXueLSzAL/4iPPGJum/05z73uD+yUtF//8AhnNNC8yT7tAPvO4S9vfq62N2tr6WlkivkbYew19zGMhmIxUJBmM937Iq1CEJhY+F5TlJvDcWjM0OAgmy4vOOhOPydkXU4wM7FhF+k0zA7S7D81s0sRzlILJ0AFLUa7N0LsX17nJDR2kIlaD0hIaOtxRYY5bIvwGdmiFHjah4gQQVyOUZH9STUnujYghCWEYQnT4pTvwbYOWiNWk54+KFRPkYQRkOimhWWkUqjrSO6+BKQzwdio0SGhb5tzMz4oaFeOIa9vRFBmBJB2Eoch3B21g0ZTW539h0a0uMUdQkbOoSSQ6j5yEfgW9/Sj+fn4Td/Ez74wcf1kXU5hLN6AnGIK/QGP4fwiisIqjCb/pETVv1BWxAaF9HMLUfYRXAn61CXUAShsLEYHbVipmAkeyll5a+85dya+Idvj8QDCC3FhItms74g9CuM9jDDw1xJPK2XyGs1fWEmkyHXoy/UOQpUiQcxbh26ANc27EnqmTO+ZpuZYYgxchTpY4pSqof5eb1vpaJDDnt7lxGEiQTTdqXRYlHU/RpgxiuRaNyUfppeFhLh9a+7W5+Hk5Pu5zRsPSGCsKU0cwjJ54OCJSUyjGf2OPtnszqSIpPRX0qFDuEs3ZTxw+CkfcGaYXKl7RxCu6jMOG7RLJNnFs0jbOwQhhfKIIew0xbLzpyB97ynfvsNN8Af/MF5/z2iOYTZGR1GHQhCP2T08svD95ixs6ORbEGolL6OApDLsUAqbPfSoZVGRRAKG4vIiXh0z3MJqn5l3VrPhx6sIrQPIwjjcd+ZmJoiTpUcBQ5xBbGMvihXq74gBHLb9DJcjoLOjynpm6+EjLYWWxCawjHMzLAL/WSIMWZUD54XCsKtW/UNt6/PFYEpp36TYnLb5TiIwHjcNAsZNYLQ9CA07N4dhkSZ8xKkF+F6sJQgNO5TmTTjyZ2Amz9oBKFxoapVpNJoCzF/+0qlcVGZMasvK4SVKI0ANAWfolVGBwbQ/wAxnV89xpB+YaLD2mO94x3+anED/uzP4C1vsWLZV4Yt4mMxoFYjM30OgMP49yLfIbQFYX+/HuPpaXcRxlAswh4/StQM6Cn8kGFxCAVhAxDNH9z61PBJxCE89FgGoX2Y8LRgojk5STezKNAOoS8IazW48kq9S3anztzWgjAOfusJMZVai11lNBCE09OBINzOKDPVrmDfSiWc/CzpEALntkYEoeQRPm7MeMVioQuh5mbYwhwAo2wPehCCLtq0dat+bM85zVht2eI6hEEFPRBBuMY0ajkB1DmEY2qbs38uFwpC81xaT7QW87cPNIkVMlpDMVHucvaPCkJzbiaT4QJApWLy0VSQRxg4TZ0UNvrv/w5/93fuNrvsLsAnPwmve92qnEIzZuY7hQIZilSIc5RL9UAoLWXMQjTozT09+keZSIqmgjDai1AEoSBsACIO4bF02JA+6hCOTqbqqkgJrcM4EcGFeWqKbmYZZ4A8A8Ry+maoFFzkF2vL7daz1ixF3RzbL086NWV9jrDmLOcQ7mCU6XIGz9Mrr5VKGGLT2xuW7YYGgrDLrcQnAuPxY8bLLhLTNXcuaEo/ynbHIdy+fekcmZ6eekEYTME6aZLaBuxwX4eIQzhW0yeY3YNQBGF7MX/7ahV90SvMB2M0qQbwrGqVPT2hyI8KQqXcsNFUyh9/v9LoHFsoke6ca2O1Cr/7u+62q6+GH/7Q6u/g8//+X5hjuALqRPz8PFmKPMrFVEgEISxDQ+E10RANG13OIez01hMiCIWNRdQhXAxLrZPNBkVKACgWOPxwDaE91DWln5ykh5kgjj+e0zfD3buD/tnk9uorcoyaXi0vhfkanRZN007MGFUq/t95cZF4cZbt6LKiO9Q5ZgrJIGS0Wg1vnkuHjMJoao+7oVMmPS3EiIpgkcTzyM2E7QbOsc1xCLdtCyc/k5Oh+DMTnnTacqwyacpYlUY7MbephawkZLREhnE/HNEOGTW5oGALQj3OIgjXHsdt8kMbzRiNDV4ZOE1gNS5HXw/jfgq8ubY2DBttlEfYCfzN38Bdd7nbPvIReMYz4Pvf15MCmz//8xV/tDlfgsWyeS3ig3DRBvmDBnNPm5jQlzxbEBYK1mFlIw7ho49avbU6BxGEwsbCcghn2cK5SrjkE88meU7m9nDfWo1DPxaLcElmZuB737MSk84PzwtDRhcW/A2+Q2gEocpliMd1hVGTa5HdH1ZGzFIMQkZBwkZbibl5BsV7ZmfZxjkSflP61LZeygsq2FcpLQTT6fDLRPuk065+GPXcSnwSMvr4MRPVYNJTKpGrWk3pk3shHiqO7du1UDehoaYOl90uxISUkkhCPB5OdkolOHeudb9Mh7HikNFSt7P/ih3C4WER8GtEI0FoxuhE35Ocfbdtc57W5RGuqPVEJwjCiQl417vcba9+NTz3ufrxlVfCV7/qvv6d78BPf7qij68LGfUFYaMKo1G6uvTptLAAc3P1DmEgCONxSKfDa2StBseOrej4NhMiCIWNQ6kEx48HT49xiZM3uH+/4uo9085bDv2kSQKzAEeP6l5Az3mOjuG0/rarpVzW18hUyg+bmZuDaoVuZnmYK/UFNZEgHod9+/TLALmLQvGQoeysuklhmdZg9zkLRLeVPwgwPPAk4vFwntnfr/PXenvDst0mbLSry21Of66yFQdxCB83RggG7vvsrBMNcS53wNnfTFaNS9goJKrXro+RyYSTHZAxW0MahozWajAxQYIqCSrUiDE6q12IZUNGEwmIxUJBWCrVl5MVzgvHbfIXSY1D+HD6Wmffyy5z3xsNG5XWEz5/+qfuzTyXq3cAn/rUUCAa/uIvVvTxdYtlc3NkKVqCsLlDCG7YaDweRrx4nj9m1nGfYSdVI4uOHl3R8W0mRBAKG4ejR52V0GOD1wdVuwAuuQSuuMpNUj70UE0WTxtRKMArXxnekCYm4L3vPe+PM+5gJhPtQeiHjGaygKoXhJeGLQqSLOAVrRLf0nqiJVQq+jRKJOCsiTq08gcBHs1eTTIZnm52/qDBzG1MY2bDbDVHAUt5nDmz9r9Eh1GXQxgRhKNJN+Rqu7/OEs0jtEN9g5Lq/gtSWKY1NAwZnZ4OVlEylCCT5Vw+7uzfVBCiV2PG7BYIEja6JjjiIiIIH6q5FtNVVzlPV9Z6It1hgrBUgs98xt32rnfpMKEo/+2/uc//4R9WdB2qE4Tz8yhqPMYB/XyJkFFww0bBXTQzVbUBSKepkAj7vY6OLntsmw0RhMLGYakKo2hBuP9JW52+QVMTVQk9jOJ58OY3w333udu/9CXOtwqPMfYCQeivWM/STYEcZPWNsLdX514YQZg8sDsIU8xQxqssBhMlGbfWsFxBGYBjsUuDCaznufmDhobhhwCJhNMknenpzgtpq9Xg/vvXzLlZyiFcIMlkLCzMoJRbETYe1xPSUqlZ42UgLQ5hq2goCC3HJE2ZWk8vY2P638a47T09erzqBSGQlub0rcApKmPlEJZJcazkLrqYStmGpRzCQsE/J6Mho5v9PLvxRndOMTSkew424iUvcf+o1Sp8+MPL/ohGIaOn2aPbWAGkkuzaFVkAs+jr08JvZkZfZ+1xc8JGfWEZXCc7MKxeBKGwcbDyB6vEuEdd64SqXXIJxK84yEGOhBsLhU4tCNWcT3xCV/KKUiw23r4CjCBMp/2FVf8mcAbdW0s7hPp6r1QoCFUqSbZbz5SCXoRl6UXYSoy4qFZDvZKYm2Qb+gbnAUfK+5ycJ1tgGGy3yaneFovrnDb7BxasYk+bgXK5ucidnNRh2Ndcoy9Kd9zxuH6UHeIbRFRbgnCMIafs69BQWLTJFusTE64gtB+LQ9g6GuYQWhe3DCVmt+ykUgn3Taf1uCnVXBDmGQgrw4ogXBMqFX1drNWAmRnifkjvYS6nZrl7u3drwW6zlEMYhIymI4Lw7NnNXZwk2mbiP/2n+rLUhlisXiz+9V8vu0hdrVpjBsQLsxzlknCHVKph/qAhHg8XOvP5JSqN+rmIIggFYZ0ZG4Pxe4YBPWH9PL/O7fmDDA/r60W1CpdeChw8yBVYCrBY5OGH1+WQNya33w5ve1vz1z/xifNyc8w9LZVyHcKT+FVg/ZvAE5+oV8oXF8P35Pr1azkKVIkHL0jIaGswLtO0lW67vXiCOPqOmmeAiWpvkEthcgehuSDsdfs117ee2Cz9XwoF+OVf1r/8M54Bjzzivl4q6dd/8AP9fHISXvtat1/EKrEd3aDJvCUIdVN6t8KojR02ao+Z3TqETNptPSGCcE2wxXwzhzBDiZnsdmq1+vxBCCeodm87E74WVIYdHm7Z79BJVCpWg/PpadKUUcBDXOW0dYmGi4I+BZNJ/Rnlcr1DODSErlKaivQi3Kxjd+5cffuI3/iNpd/za78WxruDXjn+679e8i3m723SFjKFPIexFGByaUEI7jWyuSCMOIQSMioI7adSgYceggfu9xhjkGH2cDfXUU2kApdjYgLuvhuKe+oFobSe8Bkbg1e9yp2cbtkS2gmg/9BmMrsKTA5hpeKv1E1OkqXIEfzMez9k9IorwlC1II9wSN85Q4dQf5gIwtZght/8/QG2z4UV0x7hMkhnAkfDhNRAc0EYXS0fzex3N2wWQfjOd8K//It+fPvt8Mxn6obLoP/xX/taXbXX5tAhvdByntgOU2C0zs0FglA3pXd7ENrY7SccEYg1ac1kKKNdJ0AE4RpRrWpRGI9HenBbPXXSlJlOb6NWq88fBPc86+ry1+t8UXGWHfoFcQjXhGrVF4TFeRgZCQvKcGVwD4PGghBcl7BhURnonOb0X/qS20z4CU+A665b+j2ZTH2/wg9/2IqVr8cR8UBmfiJsOQGQSjbNHzTYeYT2+eYIQj9kNIikEIdQENpPoQBezYOTJznEFdzGs5lgqxOD09sLt90GH/5cH7191p3X8zh8b6njUpga8qY31a9GfuYz8Eu/5G775CdX/dHG7Quu//k8OQoc5VL93O/j00gQZrfpmU/gEJbEIWwljZqcb8mHAuAwl0M6TSymHQk7b9B+bIuLaH5GtMjJRq2CuLDgzlmW5Lbb4C//0t2Wz8PP/zx8+cvw9rfrQgiNePe7zzsG2q5SacLRmJ2lC/0k2pQ+6hBmMnpyWq3qc87uG7lzp7UT1uq3CMI1YamWE4YMJWaSg3ie6xCacyqRCJ3BQBD6J18Qki+CcE0w4kIdfhi8WlhQpucZjlKI5g8abEEYLSrT3+8vCvifM0UfFeKbVxBGw0V/4zciqyJNePObXZvu9OklU1kcQeh5xGamOEWYsqDSKQ4eXPpHZrP6q1JxD9HJIZSQURGEwvozPw/k86jiPHNs4Xs8m/HYdojH2errQrMKXirBmR1PZguh/VGYKHHq1Poc+4bh9Gn4ylfcbTfcoB3DN7/Z3f6P/7hqNWYEYankP5mbpUiGCglAQTYTNMuucwh3akGYoozCCz6sWFxyYVA4T+oKlCwukpsJK4EeVldCKlUnCGMxtxBJ1LmwORfb6W7YgA5htQo/+Qnce+8Kdp6bg9e9rvFr5bI+jz74webvn5yE97znvI7TDhkNHEIrZFQLwlDlRR1CaB4SFYjHVAqUCic709NuTLFwXjRsOQF1gnA6vtUJGc1mXdfdnGumr6QRhCP4VZpFEK4Jgbh48H5Aj80Yg4wPXgFopZBK6dTgRizlECYS/nlo5SKOM7g5F18efNDtI6iUDgddCQMD8PrXu9ve/Gb41Kca7l6tWiGjCwtMVq2bVCzG/ovjbr50E8wCp71406iozBl2aiEvglAQ2k+hAJw6xT5OMsxuFkgzkrqIdFrR16dP2Oc9L9w/P3AZl3PY+YCOLyzzta+5z6+5Bt7/fv34ec+Diy8OX1tYgM9+dsUfXauFzctLJYJwqEn8ahbZDKhYsKpaJwh36/0UfhNgqzn9TNh7W1gj6hzCmZnAbfKARzJPBKVQSk9izMS0p8dtL2ELQnvyAzDqRWyqDSgIy2X9N5idDUOem/KOd6yuT+eOHfDWt7rbPvYxHZK9SmxRUSigLaJoDmG6eQ4hNBeEYZ8tJYVlWkDD/EGgOjbBd3guf8PreYz9zKje4DoKbsgohOdad7cRhHq8g5DRzZqHtlpqNR2i/eUv69SHVYYGVauw8OgpYud0P54MJR5WT3BWWS67rIHA97EFYSYTXi9NJELHNKePOnrPf76lrFbA7/++m8pSqegIp7e+tS4fO3B1FTA/T96uvptMcfnlK3AlCX9cVBDmcv7103cIa8T0eTc+vorwks2BCEJh3SkUgJMnyTHPJFuJU2Ui1h8siivl9jSd6D1Qn0d4mM7G5D0ZXvOa8K4Wi+mLrc0nP+l2G18Cu8Lo7CzB6neQI2GFi4K+wCqlL7bVKuT2hhfwNOUgZBREELaCOodwZiYQF+MMMpnUk59YTIsHI/bscFFwBWG0cNxYdWvYwBc2ZMiofS9f0gy79Vb46Efdbf/lv8Df/m3jmeGWLfDNb8IHPgAXWcV1qtXmJdeXwIxXEFJYLJKslUiif4HRxB6Ih8fRyCE0eaBzc+4hO9Vh02lXEHZg0YS1ppEgLBTg83dewW08h1Ps5cu8islqt7N/VBAaER84hP7NL3AI8/kVrGpcYIyOwsteBgcO6EXLd7xDC70TJ7Q7c/fd8I1v6KIjb387/If/oP/Rr7xSO/bPehb88R+v6kdWKrDw/duJEfaIfOiKVzjx8c3CRSG8Vhonv3HriU3ei7Bahc9/3t22XDGZKJdcAv/3/9Zv/+hH4cUvdhx2J2R0bo5Ru+VRKrlsQRmDOUejghD8PEIVC3Y6zW59Qe6wUugiCIV1xziEp9hLgS56mKYSTxOP6wtBMqmTvM1q3EzXLvbzmPMBHT23mZnRk1qbaN7g617nXgmPHoXvfGdFH99MEAb5Lf5d0dxIYzF3JTW7P2ywnGbBKcMtUWtrjxEYwZ/ZEoS6oIye/CiljeOtW/V98GlPcz/HFoG1misYa/FUWKAENqRDaC80Nz282dn68KVLL4U/+zN9znzzm24CZSKhJ63XXaf/QB/4gPvem27S71kFRiSYcELbHfSAcyl35b2RQ2jEPbinuSPyE0m3F+EGHLMLjWgO4eiojnw7fja0acukOTahzxXbIbT/reocQr9aZXCNBaup6CagUtHVem+8UQvAW2+F//2/tdA7cECvejz5yfDSl8Ib3wh//ufw3e8GvQMD3ve+VTn71YUqC7ffHQjCyuC9YQAAIABJREFUNGUe2vtCZ59mBWVAj3Mqpc/VUqlZ64lIyOhmE4S33uqGMHd1wctfvvrP+d3fhX/6p/rwk1tv1cW8/Egkp8ro/DxnzCIJrKjCqME4hHaOtRH2YR5hZ/ciFEEorCu1ml6lUSdPcD9PBGCeLfT16Av24qJehE+lrMlNfz8JrOX/YrHTFnJcbrrJnf0ePEjdVXJoSN9sbVZYGbGRIFwgGTZOzmVRSofaGOyw0dzeQT3BARIsQmURanr2Kw7h2mOcQVsQmpDRw1wehDQpBfv26XnZf//vukicjS0IS6WIEIk2p9+A4sJ2CJse3v/8n27opFK6EJOZ6b3gBbrK6ItfDD/zM/D1r8MLrQnkK14Bz362+5n/9b+uKtSoLsTXEoR5BlhIhTkz3d2RhvMWjUKiXEGYYIRdYeuJDejqXmjY4b4PPQSf/rT/v2aX+AXG5zMUi+G/RVdX43zdwCEESKcYY0jnM8HmyiN8z3vgxz9+/J9TrdYvyixB5Y67WJgponxBmMwkeKR2qbPPUoIQmucRBq0noiGjmy3cN1pM5lWvqk8yXymvfKW+vu7b525/5BEdpYFVGTYGpfw804SlsOPphJMNsxTGIbTdfMchhI5vTi+CUFhXzApN8cQ5TnAAgEn66BvUZ221GiZ4m9LBbhwUUCoyMV6lY4mGi/7SLzWu9hUNG/3qV1c0KTSRSkFT+nyePIPECUvm7d/v5i45grA7HiyHJ8x7/LDRzSQIFxbgvvv0/X89q94uLuqfv5xDGIvB3r16f3vV1BAVhE6oYiKhi50YNqC4sNdIGhYwqtW0+LP5vd+Dn/s5d9vVV+t+Wz/+sRaGNkrBhz7knm+HD9efkys4zuB4rZYTp9ntVBhdKk2n0Qq4UygomaBMOlzI2YAi/kLDCLyjR3UB2sVFwPNIz43zGr7IU7mTKjEWvQQzM/q8VEqHFtopVOZci8ct5zCdxkOF59lmEYS33aadvfMlKj7+9m91A/hlqNWgdtO/UiFBzF8Wmbj+P1KuhAphYMDOu136xzdtPZHwC63hC8L5+c3TnH52Fv75n91tqw0XjfKkJ+nqX896lrv9C1+g9uWvUK3q8yo2P8v4Dw4RI5zrXbyj0PDe1YhlQ0ahvtJoh4WeiSAU1pVCASiXOXYuXC6tESfVo12najVM0wl0YCpFbcBNpJk43aGtJxYXdZ6FTTRc1PDsZ7s2XqWi3cVlMPeyTAZmZ3Rc/ZgtCLO5urwLWxBmswQN7oL3lDefIMzndZTL0aNw//3rU0G1WsXpdwaQnhsnQRWPsOUEhA6hEZBRlnQIkxFBuAHFRdSkqzvE0VE3BK2nB/70T1f/g667TvcntPn4x1f89rqcT8shPM1up6DMUoLQTHjsdiHlsvUef4dgsrMBRfyFhvkfOxa2+aQ/W+S3Kp/gch7h+fwb8UQcFVPBOZbNLp2vG7wWbT2xGZymiQn49V93Lzg7d8LnPqcLjfzcz2lF3NurrbrnP1+fW+96l+57d+yY/r/db/VBLZfr89F+/GO4/np4ylN0deCFBapTs/C971ElFIDDV9WHiy7XOcEIwkKhvvXE4CCAgqT+GUGe/WY51771LasUMnpF0S7wcL5s2wY336zFocXUm9+h80q8Gnz5y0yUc0G4L8AVz1t5IRuzABN1CD2vvjl9p/YibIsgVEpllFJ3KKXuVUo9qJR6j7/9s0qp40qpe/yva/3tSin1l0qpo0qp+5RST7Y+67VKqSP+12ub/UzhwqBQgIXjpznuu4MeUMgMoOLx4AQ21/4BK2VpbudBshSD55XZYl16QUdw221uIt7QEDzjGY33Vao+1v/rX1/2RxhBGItBMV+AcolJ+rW4i8UgXR/H7+QQZgkqKAQXc7/S6GbKISyG/45MTMCdd7Z/HmDERdUyzHOzepVzjCGm6AtyXDKZUCw0Eq/LOYQbPWTU/C3MCnLdIT72mPv84ovr81msXR98cAnn9/d+z31+yy2stPTxUiGj5+MQRic8wWTHLpgAG3LMLjTq8nWBX3zKaYbQbX0ylHnK4GMYx8jz9PXQuICFAjz8sCtCek1E3GbrReh58IY31Avbv/s77TJ98IPw/e/rVcKpKX3C3Xyzroj9vvfBr/yKPkeTSR3jbvOxj4UX27vu0qHet9+uH99wAzzhCVT+2ztgocyiEYQ9vZxMuf0lliooY1hRc3r/XAvc+M0iCL//fff5q1/tlqZ+PKTTeqytC1h+vAYf/jCp794MJx5jmt5wDnHgAJf83M6GH9UI+7po7gm1mr73RVtPnGVHR7aeaJdDWAZ+3vO8JwHXAi9WSl3vv/Z2z/Ou9b/u8be9BDjof70R+DiAUqofeDfwM8DTgXcrpba26XcQWkChAMd/OoG5YWYpUszqIY3H9YlrhKAdKToxcJABrMTBYsHkIHcW0dC0l77UjUWK8rKXuc+/9a1l853MZGdhgaCgzDw5PWK5HKDq+jYlk/r6bhyrWJ8WhHGq1FCbMmS0UNCVqnfv1qv8Cwu6B96jj7bvGMwE1R7S3JTuQXiYy/UGf6J56aWh6ItU+rZ3A/T/QDSH8EJxCM31o27xIdp2wXYdLMplvevY2BL/r096Evzsz7rbVpijWycqrKb05+MQNu2zFRWEm2WSuo6Y/zFbEHZNu8VferdlgogJz3MrjD7yiDaqbdPFDhmFTSQIP/3p+nDDt79du4Cr5XWvcy9Ic3O6QuVjj8Ev/EJdDidHj1L5jM59CxzCa67h+GPuFHi5/EEIxcTiYjOHEEjok3CcQX2/2yyTkx/+0H3+nOes7edfey384R8GT8cZhO/cSvIWXahrmh4tCPu2wv4D7N3b7IPqMdfHatVNbykW9ak2OEgQMuqhdIVfEYRrj6cxZ2jS/1oqwO+XgL/z3/djoE8ptRN4EXCz53kTnudNAjcDL17ic4QNzvw8jB+fIYmeFXVRQHXpZbdEQkckGOfFdgjz3Qfox7rIFjqwsIznNc4fXIrrr3f/kFNT9Rf5CGayUy4D+Tweinn8GY7fciKaEw5h2Oj8POQG9X5pytSIbcqQ0e9+VxfN+8pX9MTioov0yv/Jk+2be9c5hIuLdM1rh/AQfvNlf0Zz+eXh5KaRQxiLha97Xn2Bko2eQ2gm6/39+neZn48I3xUKwrNnQ2dwySiE3/5t9/lnP6t/6AqP0xaEa+UQlkq2QxjJj9mAIv5Cw4ydff5kJ11BON59gIEBfS0wDmFPjz5lzBDYa3iByEhtIkE4M6NDQm2e8pTzC9EG/UeMft6HPgQveUnTfMKqX5zHOITlK691UsSU0tfE5TDnV6VSX1QmnfbFvh8yWiOm+/VuwOvjqpmd1SucNs985tr/nHe+E667jjm6KJMmxQI1P+tzml5iiThcdSUo1XDe0QxzjlUq9YIQ/OtkMlx8O8NOySFsFUqpuFLqHuAcWtTd7r/0Pj8s9P8qpcydbzdwynr7sL+t2XbhAsTz9Mk4OzIXCMJzDAVX2Xhcz9GMaOjtDU/quS076MFa8u9Eh/Cee+CUdTpkszpUZinicfiP/9HdtkTYaF1T+nyeOboI1nNyWXp73RLqBiePcJveIRSEOmR0MwnCI0f099lZPSz794dCuV0LjWZiGjiEVkGZh7lSu01+fNpVVy0tCMENG+3ttV5IJDe8Q2iHjBpHxnEJVyEIDUsKwle+0q1IMT0NX/ziksdYqejrYDxutZnzBaFHaxxCySFcO8z4OYJw/JSzz3hmL8lkuKCSy+lUbjti2R6zYLK6mXIIv/99d3Gkq0ufGyutCNKIt7zFvSjl8/Vh2j/zM0FIY8UXghUSsGs3496AE+148cX1/VYbYeYg1Wp9yCj4TlMiHNAxhjaHQ/jjH7u9i6+4wl1cXiuSSfjsZ8kndgAwQJ4yGYpkWSRJ7MrLIZUmm13dj7eFfCNBODCA8/84Q484hK3C87yq53nXAnuApyulrgbeCVwBPA3oB/7HWv08pdQblVJ3KqXuHBsbW6uPFdaQUslvOzE+H8SFD7MnuMrGYjqsbWEh7EOz1QQIDwzoJueGQqHzHMKoO/iCFzTNgXKIho3eeGPTXe2WE3NzQD7PDL1OhdFmYRtOpdGd+sadpqRXav2Q0c2SQ7i46LdPUXrCYLSGiWoaH29P5dG6fDRfENZQ2iG0emRdeeXqBGEmY01cE3EK5JjH/3+bnnYnCxsAu0ecmYw7uvXECRZI8hV+mX/m5ZR3XVT3GVNTep554416/nr48BI/MJ2G3/otd9tHP7rkwJvItmzWDxv0vKDK6BR9FMkGg5TLRUR5hOUdQv1C0HpiA4r4C43FxXDBDPS/QHzUdQjzSb1w0tWl1wte/nL9P2lf+2yHMBSEetwDQTgysuHOsRUTde1e8QrdHunx0NsLv/M7zV9/9at1S4P77oMXvcgVhD/7s4yNuelvKwkXBT3WjQo4mbDfoSEChxA2kSCMRhJFqzGvJddcw/gb3glAP3nKpJmhF9XXR6xfTwL37l2+AJDNUiGj4M9XUqGQn6VbBGGr8TxvCvgO8GLP8874YaFl4DPovECA04A9zdzjb2u2vdHP+ZTneU/1PO+pQ8vVERbWhUIBqhWPhUk9K6oR4yT7AlGjFDxRtyYMVuaDPMKtW0kpq3LGwgITp62qHp3AasNFDS96kTtrPHw4tLciNGo5MU1P2D4iu7wgnJ+H3G59Ed+sDqFpa2AEk+lF3NWl/50XF9sz/66rWOn3IDzBfi0u/BlMT48u7meOt1EOIbiCsFy2CsuoGMTjoUvoeRtuMO0ecc0E4Y94BvfyJO7jGr6fr58Rnjmj+9D/4Adwxx06Km3JKNA3vcmdpdxzjy5u0QQ7+qFQQP8j1arkKGh3MB6HuD5Xd+9eegLUrM9WT4/v4PuT1AV0fzsRhI8Pz9OLDqVSKCyyWfQ/jcW40vOPmD5lGBoK3UEj8O0xSyT85/EExBPM0EOBrP5hF+ridvS4nQpVj4O3vc2d3Rue/WxduTQW0w1Wb7qJ6c98hdqrX4P3C78IV13F+LgrCFdSUMZgxss2OM11QbeeiDiEm8GNjwrCaM70GlIuw+wrf5PYE68OigdOb78ctbUvuAauJn8Qlg8Z3bIFJ2R0ji0iCFuBUmpIKdXnP84CLwAO+XmBKKUU8MvAA/5bvgb8hl9t9Hpg2vO8M8C3gRcqpbb6xWRe6G8TLkDm52HukdMkKvqMLJGmFk8HV9mdO8NwJzNxCkIE4nFiA249oYmjm2AVbqWcOKEnmwaldEGZldDTU58M3iRs1GlKP12FiQnfIfTtl1y26YU5k9EX4XIZkgM9EE+QZkE7hNUqVCvMzq5vz761wsytzUTh9Okwj8+sR7Xj3tLMIdT5gwRNk6/UKRircggbNaffyGGjxiFMJLQgUko7cpUK+p/uxAke4Opg/3vzexwDplKB48fh7rvDcZ2a0u07m7J/f/15+LGPNd3duEQ9Pb4g9Fe+AkG4wvxBCCc8tttkojB278ZRHafZrSepm+HkM5w+rUt2tqnnmzm/q9VQqGezaCfPYryi71NGfHieHvdkMoxStsesXIYdO/wnm6WwTPTi51xIHgfbttW78lddpU9S6+LleTB+8Bksvv5N5K7RjeinptwFlscrCItFfa5tSoewUoEf/cjd1kJBmM8D8Tj9n/kLFj7wEXjZLzJ93XOIxcIBW03+ICwfMtrdDSTiwT/FLN36ohwtULSJaZdDuBP4jlLqPuAn6BzCG4EvKKXuB+4HBgGTYfxN4FHgKPDXwG8DeJ43AbzX/4yfAH/ibxMuQAoFmL3nGCk/f3CWbujpxlQcveqqMPenziEEvIjzm39sYzkULSUq4J75zNXdZFcYNmr3IJx5bBKqFabp0SGjySQkkk0FoVKhS6hiCnp0hTAz3pTKJkLugicqCBcXQ6OgnWGjdQ7h9DQ5CjyE736lQ0EIqxeEF0rrCTs37/+z9+ZxclzV2fBT3dV7z0zPIo12jWTJq7zJ8gIGr2CDWUywiYEEh0BY3oQlCYGX7yPhi3kxAcwWSOLACyEsDmCDMcE2NnYA22CM5d3WYi2WJUsajWbrfe+u749Tp+691VW9zdjqnunz++mnWbp7uuvWvfc893nOc5iZUeoIZ2dxNB0SfcJ0H1KVMPbtE69x9Ch5KFSrNJaTk3QNfvrTBuNoN5f50Y/IXfH22+nf/ffXmCr19ZmJibnQhZBrqX4QEPeeYbj0IvR4idmFCQhZ57wQ4hOfIG3syScTJX/cceQ2+ZGPAHfe6U6BzyEcHX3DqGUIC1Q/LQNCgN4uj5MdEFrzzBz/w1hB3y8UQDifqq3rrqNeoAAZ1fziF1JtCUUiYTY3N42ySiWpZhd0/S1pdRPB41WtquAim12gDOFTT6nSiKVLqZ7HjFxufjEvl/8Mrwgg//Z3AZs3I5H0KPOkVUDI5RyACuRZ6ks+COKUNM3GeYuIJdQbP2TuYRjGUwDOdPj5JS6PNwA4isMNw/gPAP8xr2+wF8ckslkgteOQZSgzgyGgXxTJnHyyMCthJkkuIq6M2JrTH85j0cTWrer3b3xja89//evV3mkPPEAJva1jssIQ7qPeWkkMYBAzQIjs8OpJNyIR2ow1Daa13gwCMMepkAciESQSzqY03RSMhWRziAMHKMlg2Wg2S4+z5SrzGjUtDEzJ6A6YCNDMQLlv5FwBYac6jcrsIMfAAN2L8TgwnHhegGSA7ntNw5NPwmqhMj5OJHw+L9igZJLkfo88Apx9tssfv+wycqjgfiPFIhnOyHHGGcj+4j6USv2KkytSKQSRhxfVthlCPgHne6CmF2GpqBrLNFN33Mlx993AZz4jvq9W6do/9xyBwS99ibL0t76VmqKffXZrxUcuwfeYDAhDQUNhCHMIIlsNAB5xMBGJ0DqxapWYr9UqTc1Cge4DCy+Z8/UITMqwW41lXiyGEKAF9dFHqU5xdNSxL94UbV3WPpNIqA9bsUJdKxqFzDZFIuJcxQKEEkM4hRFg5rEWPlAHhpNcVJpD27bRwe6mTWSqYxgE6iIRZ0VvvahUxFYyPCx885JJdcxaBYQAzb9KpZbZBcThNXx+oFAgggKge3f9+tb/WBfGS15D2ItecGSzQGrPEfhQggFgAsuAgX7r96ecQvthIEALby6nMoTFwWXK601PdmnBfTuxd6/6/emnt/b8445TNTLlMnDXXTUP46TS7wdSB+MoQ0cGYWIIwyFoGm2mbsELr67DKpixzIAWUC9C/gxyUsF1hACwZKAIJBKYfPwg8MwzhCpeBGmbUw2hBxXsg2mYYprK8NA3qiGUmaZayWjnOo3KhjIcfNaRSADYvx/bcIr4pXlvsuIwk6H88tlnBZPg8VDinkwCP/5xnT/u8ZADYr144gkkr/+a9aetPnRTU2rLiRYYQtn90C6JEo2XbU6jHTRmbUU22/haA0Tvfu1r5Dq5aZMqt28zHAGhkVVY1+ngKmtR0DQaa02jQzSvVzW6kA9frIPPhSIZtdcQzicgBFA1NEx4llN9ukMwIOSWHnZAOBf5oWNz+oXmMlqnfrBYFCqfPXto/fzoR0m0dPHFtN25RTZb2wZ5dpbW2f5+yh/yeXpMOi3GTNMar4dO4ebEDEiH0qaxjAIIF0n0AGEvmot8HvjkJ0mvv2PHnF+uWATKySwyR1LwooIsIuRYaOq6/H5xUi+zhAMD0iY6NAK5nWUmWX2pykeOfdgBoSTfqBf5PCWz3/42MP2qa9RfOshG+XpWKkBlctZqDOuBAYTCijGJUygLsDm2Pq4/XEC9CB0B4R/GYbzmtUAwiCUnDgFvuhKTl14D49RTqUlhMEjZw+mnA296E3DLLXPWlDIQtORQySQOY4VIlAIBrFwp5JNyk2X+04UCgaFcrkWGsIPAhWwow8HJeCoFHHl60pKL+lDC8DLdet6OHcQOPv20KivjA/FUivIjGfDXhL1xtkMkvv5DYGIC/f2mGiuZBLZuRQQZGDBBW0hkm81KRu3gwslpdME0p/8//weKzreZ2L4duOSSOSd6joAwM6U8Zmr4BHAJhNdLhxJ+vxhLmdWVD18sFYHJEFuSUZsctWvixZSMglTZN94I/PCHtb9LpWgO+P1ijswnILQ3px8ehrLwTGO4u+eZYZCrlhwSIJTdcvN54MtfJr+7YpH2kY9/XDrwkmLnTlL7Xn+9qka15KLD4jVZIcZjNjqqzpdmw8kdtoYhtEtGF1Evwh4g7EXjMAzg6qtp8/3Wt0hys337nF4ykwGwcyeKIDRxFEvgC+rWydoJJ4jJKwNCTRObpTYyggiklSSXxczUImAJmb7g8Hqb3tG++lVywv/ud4FPvvAeKPDjzjtrjusYEBaLMFtO9ENvwlCGg8GizBBaz18gTqPVqqhxtfKAHTuQ/ddvY/rurUChgCgyCCGHEnyIQ5LlTk1RfcbPfkY26Zdd5ur42ijY9bBcNiWOpRK8uRT2gA8LqD5CJoY1jcbIMAjgPP44eQfs3Ans2tViDWEHJT1ODKHXS5u+YQAPPyLkThuxG5s3Cc3s449TDvDIIyog1HXRXHx2lsoCXWN4mKSM73431bLxP0nikCwFgW9+UzCE99wDlIoII4sU+pDRB4CllDgHAqo6winquegtyNYTTz8NfOEL6s8++EGiEh5/nPqEvOc9zr06ZmeBv/3bOf15xxrCjMqETceOs77WNGLmN28WY+VW12Qp980b2JKMdtAcazoM40UFhIWCaAeza1dtTTqzg0uWiP3MDghbdayUD1/szekjEUDzC0BYQADl6S7ur3TggMpMB4N0E5vBS8jwMJ3NfOc7qvjl0CFKG+3xwAO0zu7bR4fU8biQmgJmP0fQ+sWgk8es1fHi4LlWlyH0Ld4awh4g7EXj+PrXgTvuEN9nMlQTU7dLc/3IZgFj2zaywgcwiaXQB8TKeoqk5mI2o8ZpNBxGSJdaT1SrmNmxCE5z7Ozg2rWAz4dcjpRQblg9HqcclWP77DIcHBAui5idVZzEKhVRiJ/LwbEHYSMcyguvzBBagHCB9CLM5wkw6zqgwQDuuw+4+UdAqUhtVMxYCtpYLCMTp7j3XpK0/eM/qmikieAE1XI9NB1Gn2WHUbMpvd1NjxPRffukek/Qpm4HhEoep+uYwgjKMHfZDgIXTgwhIA6THtkl1SpjO07dErQ+9/btVAYmS538fsJ2nJAkk8B//3eDJfCMM4BvflOYydx+O3DDDQCAMrzIIALPvXcjuvMRZB98HHjmaQCSw+i69dYBWaOWEwC9N4+n1lQmn6dkh1z0bK0nuhFgAHQK8973qmhs5Urg05+mjPyMM6hm8BvfoMOzn/yE2u3IcdNNBMLbDP7TSlP6pLr/TPWNKd8vW1bb/NyJtbAwrCnxHcdyAvDdOF7xuDpO0ei81q3a1Zg2k1dLrToyIpbUudaj1ZOMahoQGVAXnsxssXsdfe1y0XPOUU4vZEB4yy20JsrDzUZccl5SqVCJby5Hj33+ecpdHnuM5lMwKJjXfL4WELZTPwjUl4wKhrAnGe1FL5xj1y5yarPHzp3Au97V9iKXzQK5p/dSAg1gBoPwSvWDcpNYPrlJp+nPWSflmobAgKobmH6qS2ssWgkHuej0NNXVx+O0flUqtU/73e9sfY01D3Ztebv6IKm3oVw/mJ4pAvE4EjIgDIUaOrPxAuz1wspyNFSVP9DtDCH3INSNEvDjHyP0mzut31mA0OvFkoESsHQUk2vPhrF8haP5AQB6seuuA047jar1m4waxsI0lBEOo2r9IMfwMI3PyAj9js1SyuXaPoR+vyRn8+kwoJFpAtBRgNCJIQSo3jWRAA5MBlGCDh1lHI9d6D9pJdavp/UlHiecIBtwnngi1cUwIK5U6HT7zjvRWvzZnwGnnookaK3rQwqej34E2c9+zXpIGFkcWn+BUpzbbL1MvRNwaj0hfnEEyzpqzFqKr38deOgh9Wf/8i/i9FCOYJAaof/857W11u9/f9tOq3yPyfW3obgq6ZwOqgsksx5yOI2Zx2MCDQnAz2CoO8erhfrBapWWvFa8c2ZmaCzGx2nplMmsbJb+cS/SfF60/XixJKMAEOnzKn8gUw3O6QD9mEYduWipRCDY46EylMlJuibVqshBCgX6/gtfEHPmmWdojb3vPvp34ABdU75E8jxxAoTtMoRuvVoNw2R2NVhgN4cQHXb2AGEvegGave94h7MAHCCe/8tfbuulsxkDqR0H4UMJFXiRwIBiKCMDQl0nCVS1SqBQdhrVB6KQY2bnIpi8EiA0AOxbcg6eflo9lXPKce67r/Znu9ddpv7gBz+wVnKl5cTOwwAMsyl9mcCFx9u0ZNTjAbQB034dVToGKOQBGAsDEGbL8N1xG7B9GzZCSD4PeNZRcUuphGj8IEL//SOU/vMmxLcdogt86BBpZy69tPaFd+8mFqTJcAKEVWhC1hkIQNdry03Xrwde+UoiJuXaDDsg5NN1SzZq7qrW63dQsurGEAaDZnKRziCBAWzEbmqDMjaG00+n5KZUUnvJ6zpw5ZV0L//pn9LPDIMee9NNzocvruH1Al/4Aq13APqRBO6/H9k9IosNI4tD1/ytQgk2Cwjr1cgMDEC5IGlEO2rMmo7Dh6kwSY4rr6Q63Hrh8xFjKFOtzz1HpRBtRE3PTwDhWfVAclJfrnzvBAidWItCwTwPkAD8OJZ3J0PYglx0aopAxfPPN3/WvH8/pSJ3301M1M6d4ncyO6hpdF3TacolGFzEYq27XMvybDtDCJggURrQDCLdayxTx1CGl49nnyWAl0yK+3lggMawWiWgvm8fpRe/+hV15pGZ3IceosPINWvoefJ6l8uJQ+O5MoSyVJuHh5VQVpsse3P6Xg1hL3oBsvJ++GH1Z/bF/GMfo75aLUbm2YNIpQ34UMI0hmiih+mobXRUBX2A2o9QrqXRBtQT4em9XZjgtBp79gAg2dlTOA37+zZB0yix5+tmB4SpFMkx7LF0ODQcAAAgAElEQVQ7dJqklQCt0r/6FQBby4ndR2AASHIPwjBJfRstzHKtRWgwBPgDCKBAzenNnaLbAWE+DxR3Pgd9gvyx1+M5As2hMGbe8WGk/uT9VhLK02dyEnRxVqwAXvEK2k1vuqn29PzBB9V60TrhBAhnIU2WYAAbNtQ3AQJo09Q0Gh65rskNEFrGMh2UrDq1neCYGc8DxTzSiGIDdtMFWb4cxx9PyWKxSB+FcUN/P3mQAOSpxXWIhkECCnu+1DAuuwzJl70GADAAOvrOQmSV4ddciEPRE5SntMoQyp+bxy0aVX+RQaSjxqzpuPFGVVYQjZKDaDNxzjnABz6g/uyGG+pbIboE32NyvVRo6gXlMdOaupE1yxDmcsDy5VDaFywYQFiHIeR6P3YVbybuukvc47kctSFkMMmvx9d9vuSHjRhC+1xLI9qdY5dIUK2uHC9/ufVlPE4f6wc/oGtbKND9PDAAvPa1onaZ58i3v03nL7OzYow0jYD0r39NOcyZZ9bWQM+3ZNStOX00CksyCpjj1mMIe7HoY+tW4FOfUn925ZVUYyYX6VcqZIZhF+7XiXIZKD65A2lEoaNM9YMhv5WByfWDHLKxjAwWq1LfQgCYeSGDBR8mQ7gfazGLQfjWrsBpp9EiyYucfTP97W+dmYzdz/tgXHW1+sPvfheACgiTz00hhxBK8BHYCYcRCjU2upAX4GCIHIGCKAjXy1y+62sIczmgcGTGqo2MIIOVy6pkaDE2prhRcmJSkxtoGvD2t9NR66ZN6u9+8Yum3gfXMlmAMJHAUbleMRCokYu6hZO0hpMuK5/T2RCq8xhCN8no1BQQ309SWi+q1AJl9WrA48H0NCUwMzMCDHo8pFYYG6PvYzHg1a+mr6tVuiY/+lFr780wgOT7PgpoHmIIIQFCnx/h/+fDNd0FWmUInfpshcNQAEYW4Y4as6bjqafU7//hH1rTkH360+oFLZeB973PpqdvHI41hJOq9exUWe3raj/oBJwZwnzeBIReGyBMJlukpDsgmgSE1aowFAGary1nQxkGGLt20Txnd0qvV+xTLwYgdGUI9QXAED70kErVnnKK0kg3HicldrEorqvXSwzgunXAxo2kyuC9gw/SWKYJiLXWweQcAN0TfNjJUupGeYdb1GvNA5h5pp0h7AHCXizqKBRIGyVvPEuXktzmuOOA731PffzEBNViNBnZLIBt21CEHxpMh9E+oU2rBwiTSfrakiLG+lGRbuPpiXLtkxdamICQ3SpPfuUwBgdpgd2+nf7ZVb5OclGANsyjb3i3+sNbbwVSKQUQzu5PImnK3LyoAKEwVq1qbHShaTYZ2+AgAgogzHU9Q5jLAcXplNVOI4Qc1rzxDGvjlAFhNErXhIvpayIWI0dfOZosVONkxMprk0nRvwxoCxDyBgqI+hs7Q2g5IHYQuHCTjG7bBiCRQAwJrMRBTGMEhdUbUKmQ9Gz9+lp28NJL1fv8LW+he5kTmq1bW8v1MhmgsnY9Qm94FclVIQHCCy5AeN1o24DQiW3iZIyS1AXAENpbL0iMRVPR31/LKD74IPD977f0MlwrxfeapgHBif3W7w0A0wVVi9gsQ2gBQk2zQKE1lztonjUVTdYQzsyoKUcz+0K1SutrpUJpS6FAe9ptt4k/OzQkwN981aM1xRD6FsBca1A/OD1NBGKlIkDVli2UPi5bJr4vFMR6WSyKcdY0sbY+8kitQyygTnePh8arUd7hFm4MoTJuEkOYQh+dLnTbIUyb0QOEvaiNn/2Mjtnk+OY3xUL+hjcAn/iE+vuf/5xs95oIBoQFUKHLJJZAHxCrqlw/yMGJdDZLmwCfEIVH+1GCmMAzSb1tk4CuiGIR2L8fVWi0yQDoO3UMABlTfuhD1Nfny18WC3A6TYYzbrFr5OXqrpjNArfeagFCnw+YPZxDAtxHsASEQ02frHKiEwgAGBoSklEAyHc3IDQMUyozk4VuJvdB5LHmVMFcy4DQ4xEK3YwbmX3FFer3v/yle+d4Kfg68mZpJJKi3xwABIItA0L5a05+BUNoqyHsoITHjSHcvh1APA4fStiMx2BAw698l+Pf/53Oue64Q3wMj4cOny68UH2NTZso2alK3kimyrqp4IS0/yPvsbLJDCLA0DBw3nmoVlV2xOdr3qW/nqmMI2vRbeACqAWEy5c7P65e/NEfkeJFDlMZ0WyUSrQcM7AIBgHPuEDyaURR9IhizlDI2VyzLkMIKE6jADpqnjUVTdYQTk7Sff+HP1ClSjO35q5ddH/zXDQMyt1//GNRai//uflmCO1MUzYrDErkuZZGtDsZQrse/hWvsL5MJKhnK5v0ALS3XXMNMeGrV9N1WrmS1ktZYj0wQNhSHptslky27SGX8Hk87ctFAXXcZCDPQLSvD4DmsR6YQh99QNYeL/DoAcJe1IY9u7n2WgKBclx3Xe3J7Ic+5G5AI0V2Kgs89xyyCCOHILIIQ49RluzzOfdY93rFqXyxKABhqE9H2S9m9gwG2+7j1hWxfz9QrSKDCKrwIDwSgd4fRiJBls/s7nXvvWJxffBBFzbKjN17PWQeJMd3v2sBwlwOqE7PIokB6KhYTembPVlVAOECYwiLRfOaJ9N0XUAM4eotoxYwO3JErTNiQOhqOnfWWeopejLZsFCtUhGW59UqgEIBicmicljSN+JvWXroZCzDJ7+cqFo1hB0ELpwYwlzOTC4SCXhRwQW4H3fhNfh/n3kbvvlNUsPv30+XPhajz7luHf2To6+P2nBxElosuncvkJNVDr7fBzYsocO3s89Gdt0mkgzres18WLGi+RPxerbqsmsl0KWsRbVaW1Nr3pCZDDEWiQR9ze6GrvHZz6rf/+Y3qmaxQZTL9DcYWIS0vHLKM+VfqVxvJ7koIEC8nY23AKG+wAChA0NYrdLcvPFGOpT5wQ9Iil1v3wKExQEffuo6Xcc9e+ga+nyqvLBQmH/JqM8nJNpsoEKmMra51o2A8Ikn1O9t9YNPPknXntO+sTEyKAMI9C1dStfjla8ETj2VrvV559EhW18fcNFF6oHjT3+q/jnDUAlmr7d9h1F+PkDjJlsn8LS1zIXszekXiWy0Bwh7URt2k5i3va32MV4v8G//pno3799PRjQNIvPwNpQMD8rQEccgNF2HHqQMZmzM2QgCEItusSg211AIKCmAcAjVHc82fA9dG+axJ/fI6VtLu90999CiLPeQ+8pXaB2zy0XDYUpKeRPdvRu1gPDXv0Z+Px3NZSZSQCZtOoyadlzBYNMLs5KkDg1BRwlWfpvLWVKfboxcDjAqVVRTQusSRB7Bk9ZZeY9hqDbqchsVx/B4qCJfjgayUR7Pvj5TIrhvH6aMIXi4xUcohBNPD7YMLJwAoXWqa+6u0zAnI1t0dkA4MYTWIW88jiWYxCDieBAvg+HzwzDoM/t89G9wkA4wXvWqWjAWDIqkxzDoI+/apTLBAEmpbriB1IlyS0mLIewHcOmlKD/4MIrX/gUwPAyPp1Zh1yyIB+qbytglo11ZQ8g9Bjj6+oBIBKUSqSCefpp60m/dSgD/t7+tk4efeKJan1CpuBcy2cIwHABhQb2W08PHA2Klc5SLAu5W+NbBi8k0TWIJ2eB3MSA0ABhLagFhPE7YY3KS7mGvl/at556r/9JPPmm+rjl/PR6av3xwfPbZ6nVNJMR88JhEkHWdWwju+Vmt0j87S0imMjaGsNvGLZ9X37PXq5yOTUyQ4kLeuy+8UOD9vj76ulCgtfR1rwP+8z8px2MF5tVXq47IzzxDaSSHXJvo8dBaPB8MoV3qy3uxBRLNOsLF1ouwBwh7ocbRo6QD4PB43Gs0Tj+dWEE5Pv95UeXtEumtO5BCH/woYRaD0EM+K+lav979eTIg5FM/rxfQw2LhNaAh8fQBh2cvkDAdRrmPWf96yjJuu41+zZr8cpnW8uuvV41is1m6dtPTgqnYvRuUHJ1zjvW4iqGhfNe98BgVpP/+c+bfHCC5aCgEaFrLDKHfD2BwEBqAANgWjv7vVpYwlwNKE9PwVgkI+VGEd3AAGBjA2rXicfY6QqBBWyq7bPSOO+q+D75+/f3mae3u3ZjCiOgZOTzctFwUcAeEhQL9DZqvVCCaRrSjmtMzS65pKutiAcJEAiOYQgp9MKBZC4uc1GkaSZquuab29TWNLNJHRuhvlcuClZfjD3+g38/OChPLQoHyLK9XJCSyqCIcrvXnktoRNgwZXMiHQ9WqC2vRbUmqi1w0kRCuuP39dB11nX5WV+31R3+kfn/rrU29Dcak5bK4zuG8ijynBo5TvndjCN0MnPx+8znmmBnQqF6328bMPOHYjpPwWXwc3996gnJAwg/ZulXsFbpOc+qHP6z/0tym1TDoevHzq1VyH5XNlQxDlR9qGplIubWDbRRurSey2QXCENrbLSxZYl2sUonGq1hUJelvfKN4eH+/YAgB4IUXKO/IZOhnfj/VF552mnhOoaB6qM1nD0JAlYw6MYT25vQ9hrAXizseeED9fvNm52a/HNddp9ZwlErAX/2VaxOhchkoPPUsMohCRwmziEEPi1XbLs+Sw4khBAB/xK88bvoZW9KwkMLOEJ6wAocOqc7QnKBks2TIJx+o+3x0jb1esQhOT5t71bXXWo8rIADcfTcCX7geM/c8gio8SJmusGyW0qgpPYfCEA4MAJoHfpi7RKkIVMpdCwjzeaB4YMIylAkib51qyCeZ8jjI9bCuteqXXaaime3bqTmXS8isUyZtAHv2YBJLBCAcmh9AmM/TxmwZDZsPZIOjTgCEbi0nZIZwBFOYwRB0VBDu1xEOk1nO9deTNfovfkFmlPLptRwDA8Dxx9fKRnnZK5cJ2E1O0jwzz3EU4C7PU45wGG0bygCqi5593CIRKK6VGUQ60rXy6FFhVFETdQAhf7t5M51tMfnnWqsLUMN6OX75yzrUvQgZEHKEsirynOobU753qwN1k4wC5seTbuSuBIRmMv1rXIwCAtg7M4gf/1it+9u3j86heY/na3HXXe5LSjJJc4znnM+n3vNbt6oK4FJJvJamzV89misg7HZ5th0QSlRqIkHsbKUi5unxx6vlPuEwPYUfc/QocQV8YLd8OR3CydVIhQJNQZ5XLP8GBEPYbN7hFDKIlxnCGsmonSFcJL0Ie4CwF2rY5aIXXFD/8f39wJe+pP7sf/4HuPlmx4enUwawbRtK0KEBiGNQcRhtlSEEAE84BBl+Tu/uspO4VmLvXlTgQRZhaDAQPWk17rijVlYB1JZzVqvEang8hOm4/g0wWcJrrrGOWAsIAC8cQODOWzGNYYtN8fk9wNq1GBlRGZV6IZ/6wuMBYjEBCAEgl+9aQJjLAcWDR62WEyHkrJt4wwZxjWZnBVPL1tnc3NwxYjHF0Q2Aq2zUMATQiEaB3IFJVJMpxBEjyajHA8QGcOKJzX+ueoAQkJzHTVnULMwfdBAgdGo5gXIZSKcwginrPQf6AwgEiIl7+ctJ0uQGBDn6+qgmhudasUilbcxY7N1LpO4dd5DBxW9+Q0kQ54Ry5575BITyCbg8brmcmbRKtKnlbNphk+/QIemQyh4NAKF8XTnhq4vvzjhD9BQB6Aa/666G79EREKZUre9UQM1cGzGELEMECLxUKgugOX2lAkxNIYcgJrkFTjiMPXso8QdoyfjDH1RGn69JLkfzxym2b6frxIBw1SoyQefI5ykVkb9/MdgmJ0BILqNd3nbCDoIse2kCd9u2qXLRyy9XDzU0jfK0oSHhMvrQQ4IxHBujx7zpTWK9LZXoEI33yn37xPh6PAQw/er5f0shj5nMECqmMkCvhrAXvQDQOiAECEhceqn6sw9/uNapFEBmxwEgEUcJPhgAZrUh6FGRuTTLEEaj4nv/QFCYlACYeT7pylB2fezdizSiMKAhggwmBk+0nKE1jZKO/n5amLlNAEcuJzbAoSHhkAmYgHBkhIT+gOUAG0ABMxgih1GvDn3TCYA/0NJGWmN0MThI0lOOfK5rexHmckBhfEZxGOWbOBikonmO++8Xyb/cV9M17LJRF0DILSysVgi7dyNutgjRACA2iJGlXsRijk93jEaA0HotO0PYAcmqW8uJqSlY4IcZQvgD5CoHpb1Ww4hEKD9i1of/5j330DX6xCdEPmUYNPaPPirkoDI4eDEYQqfGy9aJuG6TQ3XAmMnB19LRn8w0lLkHr8JX8UHcVbgY8TjNI01TAaHfT/8qFdRIFK3QtLZkowwEZRYzlFKTxmmfWpzmVkNYj9VdtgzKjXwYKzpuvOrGzAxgGHgB5oYRDFkf+KGHaE5MTalyUcMQDJ5hECB0Ojjbto3Ggfe4k04iYQVHoaCaPc2XoQxHY4awy11G6wDCBx+EUvsfDtf6DgK0z42OChCYSon5zeA9HKZzGY5CQZzJyPWEc2V0Abr12HjNqX+kAIQ0dr0awl4s3pidFVXaHJLNsGtoGvCv/6oeyU9M0HMff1x5aObWuwEAeQSp9sgfhu6j2zAadT9FBVRAqGlCyRqKBVDRxLHRTDawMCdwtQrs3SvqB5HE/Yc3WIfm4TBtRMcfT6072PSAw+8XzCpfZwUQApZslAGhD0XMYhDJ4CiwbBl8UcoyWwGECkMIAENDQsoIdLXTaD4PFI/MKD0IZZp7yxa1KTKft/DpZF32QgKEBoDp/3nCsaWKIhfNANizBzMYhseqHxzCxo2tfS43pqmWIaQHdjpDaLFz5vsbxjQBQunDtdLsOBSiObR8uTCwAIBf/xr4+79Xy7ABGuf/+A/6ev16KfGACny8XjVv9HqVPKxhyONmT3gEIKQHWQxhB4yZHDx+joBwfBwJ9ON3OB8zGMJDqVNwww0k8f2v/wL++Z/VpZ8/c0uy0TvuaOhyxUmt3JQ+HFeLP6egbmaNGEKnubZ0KZRatDhi3QUIzcGwAGEkoszL228n0HbgAF3yiQn6Wm79lkySGa89nnlGBYRnnEHYnll7Nnvat4++n6+WExxuc02Yyohx60pTGbubr7kQlctk2CRPkVNOca51lo1lOIpFujRyjb3MJxQKBDjvukuMHSB6EM41+ABGVoFkMnQf9UxletELjt/9TqWUTj21PkKT44QTgI9/XP3Z5CRRJPffT1//8R8j/cV/BwDkEEIcg0AgaG0Q69fXt1eXASEgTsDDYQ3VkFiRZzDU0NimK+PwYaBQsBapfN9S7Hghaq3bsRhdv3PPpcV56VKBH1guytfXFRBecQUwNGQBwgICqEb6kTjzYuhBX1tSGx5fmSH0svsl0LWAsFympKM8MW0BXJkhBGjzefWrxXMefpjkcE0xhJs2AatXI40I/hzfxtX57+FDb52osWJXDGUmUsCBA5jBkFI/6NTKpV40zRD6Oq+G0IkhnJkx5dGJBGKIw4cygdigyApaYQi5lmVkhO7rYpGWzlSKWA85AWIn0t/9juakPQmVgY8d7y9bpsqwGoVbjUw2KyWt5oXhPqadlqjy+DmCuPFxWt85olGkUlShcMcd5GL4vvcJZqEp2ejLXqai7mSyYWNJR8nojErtTpVVSr4RQ2ifaxar2821aHZAGA7jda8TtgO5HHDTTZQezM7SfbpsGbBxI83ZdJquyy23qHOqWqU6U8Ogf14vedytXCnO4/ighlnCdFqsty8JQ7jQTGXMGsKpKSEX5Xzida9zzt3YWEYeu1KJppt8KHb++YLdLxQIZH/uc2pr6/lgCAExbpomQCGrCFxNZXo1hL1YdNGOXFSOT34S+PM/V3+WTJK4/OSTYdxyi9k/T0MOIcwiBoRC1gStJxcFagEhb56hEFANihV5GsMLExDaDGWeHL7E2uTCYbo+uk4ARNOIJWQWNRaDZSoyMEDJr6bRtaxUqDQnlQKtkB//uMUeZAZWAtdei2QloiTY7UhGLTe3oSFoqIq6zy4FhFbyLtkY2hlCgAxc+TS0WiU3Srk5vWuvNE0DrrgCP8FV2A96gacfLdaUOFl97QaAzK/+AFQrmMYQge5QGAiF2mYIWYrK0a0MoWwoMwxymiBA2B5DCFCCH40SMGTQx3+f34OmiTqnqSlnx0sZ+NgPCFqRiwKq/NCepPL6wIlqGTqK8HXEmHFUKuJMkpt8KzE+jgSELtQ7EMULL4iWO8kkAYX3vIfYBXmeuYbXW9uk3t4QzRY8vnKXldDUC9bXVWiYKUSV57TDENqlh90ICCvw4BDMGzkSwXHHAW99K41NOk3uk7LT54YNtGb6/fT7iQmaN7Ji/vnnxVwxDLrXeY2VRU1sUlIoUOsfXms9HhoPeY60Gi2byqTTHdOWh8Mw6uw/LpLRBx5QOwyFQrXThyMYpH0pHBZzplikl5IPrNasIU6Bf8/vS56388UQ1mtObzeV6dUQ9mLxxlwBoa4D3/oW8JGPqD/P54GpKeQRRAVelOCD5vFi9rhz4I0Erc2gFUBoGIIhXDSAcM8elKAjhxAK8GNv5DRLLsqMzcknk3xD1+l6XXABnZpfe61I7I8/njbDgQG1jnDPHto4n7rs7xD//74C7X3vQ/57twBLlyKRUBPsVk7qZCvwYBAOzenzXVlDmM8DKBRgJERCHdSKNRdH09Talh07KDlpaCwDAFdcgW2QeqVNz+CJx0WWXC7T8z0eSrCyv/kDDBBL7kEFGCaU86IxhF1SQ2i5DZotJwDMSTIKUDIxOCjYDj6o4tPwWIwSGH4ffj85mNpDZgjt86BVQFhPxqZptc3pswh3xJhxyIxbteqg3DxyRNxrAM59uddqN8D7SLlMioe3v53ABtCEcahdNnrbbXXdV/kek/P78KQoeIojhqpPlDH09bmbYcgMof3wJRyGwjR12ng1jMlJHMEylECbQGzIg74+2nuuvprWQZYQBoM0l/7hHwQoBOgeOHSIUgu+P7h+EKA1dMUKkQ9ceqm4xYtFmvvf+Y7a+me+6tEAZ0AYDAIej2a9kSL8HddD0jCodvPxx10sF1wko/fdJ9ymAeDMM+urK7iOkJm/apXWNfle7+sjZ+BNm+j7bJYOqeXaX79/fhjCes3pBUMoAKEB9ABhLxZZpNPAI4+oP2sVEAKUddxwA/BP/1TzK5YoVTacCLzvfYj3rVZARiNAyI1kuSmwzBBW/MJBYcFKRvfutdjBOGLA0BCOHKHrwIvr5s20OXGSWirRSSpLQg2DFuVyma5lqSQS0q1b6d/MrAb9VRfhpE+9DVlfzOqdJvfLaqeuqVQyN047ICzkkYy7HVN2buRyqNk0Q0udM7+VK0mBzfHLXzYpZ7v0UuzVJHovn8P2mx632GLZXdSjGcj+7gmkEUUJPmIIh4Yt++9WonmX0c5lCGVAaG85AdQyhK1IRgG65kNDlMz09QlwUCjQ9xddRElOMEi3hKaRZFg2SgBUQHjwoPq7Vk/EG9mqOzJOHTBmHHYCpeawxMYQeoZj2L+fzH2WLhVJqmFQQvnnf051nY8+Sv+2basF3QCAiy9WHWkmJ0nj6xJ8j1mAtVhEKCNcRqf1ZcoJmptcFFDdNe2AcCFIRi25KIDVKwXI5kOLkRFK9JcvpxrAjRtp3N77XnXvePZZcaCybZu4VwyD2EGeyuvXC7aJx+fmm9V+vPPBNslrpN3ASdNYNtq5zel570+lXHp1OjCE2SzNo3xeSETl3oNOIctGi0W6152A3dq1VHP/5jfTc+S1gA/XWl2jnaJec3qv1xxLndxnDGg057LZBie3CyN6gLAXFL//vXoievzxrWeRHJpG9YRf/7oiLM/oMeBd70bps19EdYRYJzlpawQIAZUlVCSjfpHYTWMYxs6FDQinMAJjcBDj4+pmdOaZ9D8zQqUSlcNwIh8KUbK6fDkltJUK5YPJJFl/l8vEHp59Ni3iMzNiD+O9beXK1pr51oALvx+BoBcVbmZuGEgerldM15mRywEYHxefA0BwtUuzMagn14cOEfjmujO3mC1GMBNTJajjzyYQ33AW8JrXIPmTe4BqlXLZbduQmcxYNVYerQrEBrBhQ/3aXKeo15geqMMQdgC4aCQZdWMIW002mCEsleggRpayX345zZHTTiPGnudLPg/86Efq6zAgNAzRr5CDT8ybDVkyKq8L/Dc6HWDY62MVY5l0GkinBSD0eLHz8ADyeRprlu/KnzudBr75TeDGG4G//mvgAx8ArrrKwUjU7wde/3r1Z3Vko7L8DQCQSpFc3IzJ4RNhevwCqA8IATFu8j3rJBntOobw6FEcgMj+16yliVAo0L2+ezfNIb4lWUnh8ZAM8c1vFq6QpRKpXR55hFpOyIYyGzeKa7dsGXDWWQQ2uX1HpUKuphzzwRDWk4wCtcYynVZHKM81u7MxAMcawt/+VrQu1TSaa40AYV8fMbilEv1bvlw9e+FggB6Nkuz3yiupNcXICI3p+9/f+j7mFI2a05NsVLOSzcXUi7AHCHtB0YZcNJsl+YBJVtTGe98L3H03rfLXXov0zXcC73gHChUfEgla5HlyjoyoRcZuIQNC3vj9fsAjmUMUEEDuufGO0+vPOfbsMfsBAhMYRSqwBNmsuA6BgKgT5Jqxclndg1aupEX19NPJ9tnrFeYohw+TTOfUU8VJ9fS0kF3xWB1/fGtvWxPKGet1A7GQ0iokOd59p28MCA0p8QutXer6+FhMrW/ZsQN47LH6DOHevQBGa19zO04G7r4biff+HfCXf4n+7Q8Bd96JLMKYMd0NvX1RwONtuX4QEPbclYrKWnDdpGsfwg5IVu2SUa7fQ7UKJJMYwRS1vDFNrThaBYQ+HzHlhkFA5E1vonYTV1xBiUY+T8zVn/yJmoTcfrt6mTiBlJswA5Rkyn3Vmgk3ton/hl0y2mkMYV1AaOrjLUAYjeKxx2nu8Ue66irgQx8S/VYBcejC552VCvCNbzi0orDLRn/0I5dMWdRe82t6MikEIPSt0zF14BoBwprWPKhjKpNI1JWzdlIYEzaGcCPNt/37ae2T94bRUdqX5Pjrv6Z5ycAvnQauv572JKegIG0AACAASURBVBkQyuoLXaf5eO659D2DdrlW7sUGhIbR+a0n5LkWj9v2IdmSFaBFZXgYTzyhykXPPrtxHSYrKM4/nw7HzjtPZeY47A3nly+nA4AHHiBTIdmcbS7RSEVhdxpdTHWEPUDYC4oWAWGxSK0G//Efgb/4C5q0jvHqVxMo/M53kFlJSCKfFzmI7DDaTDgxhJoGRPq91FPMjJnqAPDcc829aDeEYVgtJ6YxjAq8GC8OwesV12TTJvV6+ny1CRb3TVu2jOQZPh8ljl4vJSByr7pqlRJXBoT82i9/eetvn5/LSWpwOKIwa4mjRYdndXbk8wAOH1YZwnXL6z6nUKBDlFtuoWnxf/8vkelf/KJj205ijEaXAWPrlMRwG06BAfP08tmd6H/jhcCnP20CQpMhHKQTllbrBzl445QVsJygOzGEBtAR4MLOEGYy5lilUggYOUSRRhL9qPoC1ocMhxs3o3eKFSsoOSqV6DqtWyeSVL+fDmsuv1xIuAE6Yb/tNvraMMQ1PXpUZd43bWqNiefg20RmdhXJqL0mrQPGjIPBPL93RaU1Pg4DAhCWIjE8/TT9iu/VSy4hJuF//S+x1nk8tLbJ4LJQoLYFSlx+uXrRxscp45W1hiAslkzSa/DcCBXikMmLqcha5TmNzLrtB2YAvedQCArKzyOIKjQX3WvnReJIzmJYAihg6YZ+5HIkjf7Vr9TLfdlltQzQqlVUC8puotks7Uls4mQYBDbs8s+VKwnwrV5Nt3e5XAsI50syWqmImn1A1L46Oo12wIEZhz03UOTqdjZsyRLA48Hjj9MY8LrUTFWR30/jvGoVHThHo86AcPlyMU+GhiivHBsTqqb5iuab09ucRnuAsBeLIvJ50gvKceGFdZ/ym9+o8qZvfat+L/hqVWjrMxmxLjbrMMrhBAgBc+MMqbJRt9PdroyZGRQSORThxwRG4dM1jMdDCIXEJspyUYAWuuXLVZI0EqFrpmmU0J9wAmn1mQnSNJXtTSToQDOZpHyEazjPOaf1ty8bawCAf7gPVQlIpeNld7ezDo1SCcD4OMoQm35og7sLyLZtwGc/K5KbeJyu8VNPAd//PvA3fyOZn5ixdy9oYMbGCImfeCLQ14ftOBlZhFGGjiDyCKAIpFLIIIJpExB6hwi1tcMQAiprwfdYLkdzORQyx1IyTcgh1BHgws4QWnJR01BGQy072KqhDEd/P80lZiEefVS8h8FBunZ9fcRcceRy1FeNa2qY7JmcVAHgaae1957qAflukYyypExhCI8cQQYRa74d1tegVKJr5vUSC7dpE92r7343lbGfdx4l/iMjtM3Ja8wTT9j+eCRS65I9Pk5Z7/e/b/0okaD56/OJax3KqczPHqinMM1KRuUxy2RkI6DulI2+MC7utVU4CM+ypXj+ecofEgnxuTWN8Lg9/H4q79y4UYBAloHyOjoyIty0OU44QbRgKpdJ/SL3jAwEWquDdwoZWACNm9N3qmSUAdDRo9I1cpCL5nLE6hoGXVu/H3jlK5v7W/w3eG12AoReL/Cud9EBwPvf33zHs1bDzWW0hiG0S0Z7gLAXiyIefli1c1u7tqGegnv7cOzfX5+Q48afwSDljHZA2A5DKNeKcG0axwyGal2yujkc6gePHNEc6wcBuh4rV6qyGj4RHRig675unUiEWQpo9SME7V3szMZsy5lntmfVbe9F6BmKwS9JrIxcvrETYAdFtWqC6PHDKEAAi+BG52Pnchn40pfoa02jA1cG54BoSXDfferzFDm2xwssWw6ctQU7XvYuzLz2TwEA/RA9O6YxTAmj7oM3EqppANxK8NxkAMjBjpWxGBRw0SlNs+0MYTP1g+0CwmiUgB8nUjt30v+FAtXgMrCRXX6LRZpb99+vAp5J4UkCQJXBtRIykGeAWShIEqkukIyGw/Q2y2UpSbUZyhyoUCds/jgXX6wyTJdcQgz8FVeQGmJsTCXWnnzS4Q18/vPUVE2OQgF4xzuA//2/gWrVusWVA8mscOU4glH8fvZE5SUaHco4MYSKzLfDnUbTafLgsZ/BHpgSm8VqvIBMeAn27CFTLfn6veEN7o66Q0Nk0MQAIZtVDWVWrVJfCyBVxKWXEhA54wxaq+XD0VWr5l6P1ggQRqNQxq3TTGVkQDgyQus8u5Y7GcrcfjvNH4+H/o2ONi9plxk+dgV2ikiESlLcHHnnI9wkozUMob05fa+GsBeLIlqUi05Pi5NwOe691/05fPqi67S/xuPiZBeYO0Po90ORjE5jWFrdFkCY9YNF+DGLQcSjq5HPi0Q9HBbOagBd5zVrhKsoQAkqIJLfVauErKpUogVSNpqV6wd5AT///PbePj/fykWHhpSam27rRVguAzAM6IcPIC8DwhOc0dett6oHJryh8j1crdKckFmLUkkA8mqVGA4G94XAALZ/+OvAP38V/aeOAQAMAAe531coDI+H5pVs3NRKNDJNGByEkvDMYpA+RD2pwIsc7EAMODOEbj0I23Wvk41lAMFAcf0gS2uHhgSzbhh0APPrX4trmU6rTel9PmG732rUM5bp9LYTMrvLyZolG5UAYQk+HMgvsR4LEAC0R18f8JrXUPI/MEC3pwzea9paRKNE337sY7Uv9vnPA5/5TE25AwCE0wLN/wxXwpD2ouOOE7XdbtHNrC5AQKFUItWQdbBXLOKFjJhYq7VD2JcYwh13iGbyAF1yOzErR38/rWNnnkmAOZNRW06sXess937lK4F3vpNYQvv8no9+djKwAFwAYRcwhLouwPihQ+YaZgM/s7F1+MlP6GuPh8aE21s1EzKD68QOvpTRnKkMepLRXizSsAPCBnLRe+91zvn+53/cc0HeJHI50QqBpWia1nyBd31AuLAZwiT6MY5l8KOEI77VVu0fQPIyr1d9yugo/SuX6X/eNBkQ+nxqEX8+Dzz4oAAh+/cL1oIX0XbqB+XnWxuI2XpC/PEcEvFjByRajVIJQDIJI5+zzHF8PkBfUWsAc/Robf+5Sy4hmfV119H3hkH3tNwT6vnnhTTqyBHarMfHKYnN5UzweNppGHjgduA730FhxXpMYQng1eEZ7IfH0379INAYEMZioOzAPGqPw9RO1rh1vHTB14tNcQBJhpvJqAxhQKwX7QLCcJiYi1JJlSOWy/Saspvea18rvk6nSaXPedfEhCoXPemk9k/JZUAoJ19OMrY0oh3JEPp84p6zWFQJEB7AalQ99Dl0neTx8oGYHFu20P4SCtFrTk2Jg4Nt2xye4PUCn/sc8L3v1SCN0qf+CaltB5TDTAAIJWkg8wjgdrxeubeuuqoxG2WX1MufuxskozJA27nTrKM7NIUJkC5Tg4HgUAiPP+XFww+re/c736nWrttjYIAAi66TKVexKA5PAgH6nZ0h5Fi7FvjLvwTe+lYxvzRtfgxK5PXRMFwko/Yawg4ChPLhy+Agvd9i0dzzpdzJAPCz5EXW+XowSOPVSimCzBAea0BYjyE0jJ5ktBeLPez9BxsIw3/5S+efT05SPZRT8OlLNltrKGNvUlov3CSjug5lE15wDKEpGT2ElfCjgBnPsPL57e5sAF2fSy8lBdTVVwvjC3kRfMUrhBSHGagf/pC+l8tKfT6Scixx76pQN3isrSQqEoHfK2XQlQqS+zsryakXpRKAw4cVp9TgcMQx8/va11SMFI0CH/wggbW3vIXGpFoVJj779tHjWC7KzeeLReFEmc/TabzPB0T6PMC11yL7xC7MnPtaYNUqeMxEZL4AoVOtBYEoraOcRuu2nCgW1R6EUkbfrmTU4xGn65xgVSp0Iu73qyfQF14oTspzOTpw4c4GExMqwGhXLgrUB/J2tqnTwIWcpPJ7txhCqSn9c1gPj88LTaMxuOgid9ClaQTGPR5a6woFcUBZU0cox5/+KWm4pWKmeCkMfOlL6O8zlJq00OxhAMC9eBWBbJMh7O93Zi7twWOv6+JzcM1jp5uTAKrpaTpN9/ahbXHLgTmKFPZEz8Stt9Jez+BszRpqL1AvIhHay5YuJROnLVvoXtZ1UQfvBgj5+R/7GPCpT1FN6Yc/PD+AUHb05Z6KHE5zrVMlo/wW2eXz4EEoDOEj2ILdxTHMztJn5n6fbgcwTuH1iutzrAGhvD7K8lWWp/dcRnuxeCOZVBepQKBuFvncc0L6ls8Ta8EnroC7bJQ39WRSAEIGd83WD8rPKRZFs2eAJrXhW7gMYe7ZAyhBxwRGoaOCrCeqAEK7ZTMgjD9WrlQbisuJ07p1Ivnkx9xzTy241/X25aL8fPl/aBp8YZUCSe7qnvEqlwGMjyvGOKEl0ZrHPfgg8Nvfqj9773vFiXgwKORLbJrASSoDQnbTk8cuGKRpu3mzSK6yBS9m8hEAmpWovOgMofTATuhFaDeUKZXE29FKRQyBTuhnMKQgsLk0PB4ZobnGACGfp6SJDZs4Vqwg4MJRLpOqAqhlCJ0OeJqNeklqOAzAK1jdDCKEkI4hqyuHnKRy4mhnCAvw4wWsgtfvtQBUI9C1ejUZzvA4M8PkWEcox7nnAv/yL9a3ccSAJ5/A4D03KxLf0OxhGAB+AtM9yNyo3vCG5g475RYpDG5YWuzYKqSDgAUgxo3LEg4cAHY+TheogADK0PGYfg4OH1bB2wc+0Fh2qGnEMK1dS7fqpk3A295Gh5zLlhGD2IhN1zTC9z/8IbWymI9+dkD95vT2xvSdxhDaAeHoKL3dVApIHKT6jTgGcA9ejZyvD8kkrWl8P7fafmrFCrpG7R6+zVfIklFNq2UJ7ZLRXg1hLxZPcJEYx6pVgMeDRx8l1czPfqZKoWR2cHaWkphUSgC+++6rtTMuFumf16sayvAi3mz9ICDWV7s9eTgMVHUbIFxADGH6uaNIYgBF0GcseEJKouHkYscbVC4n9iH7YrxyJYGGvj66pmw+8Z//qda8+XxqD71WwzKTkVYcvS+kPCa5z2ax2cHBDqNKy4lRVfeUzwNf/ar6vJNPru1/zSetLGNzA4SrVglnw1CIpJCyUcLsrDDNeCkAoehFSA+0GMJjCAjtDOHMjDisGsQsdBCVMV8MISCMZXgsCgVi0u3NlzUN+Nu/pd/xnx4fF26zsuPiKae0/37quejR6bdo/paB+YAOARjNSEYPYA0q0OExAeGqVY3NLaJRMpXhMWE1xPbtDnWE9rjmGipEhLjHY//0MWQPm/d5oYBw5igex5l4HmOmBaMPHk/jpt0csoytKbfKDhkvDh634WEaD8MAtj6uo2C6Yg9iBr/MnK+Appe9jLp6NBMDAwTqeaz8frGXLK1V6b9kIc+1RjWEnc4QejwE2gBgx/NhFOHDL/BaFOFHAgOWDJ6f00reBojekO2095nPsNd+2usIhamMrYbQkpos3OgBwsUeXDDGsWYNDh8GPvpR4K67gK98BbjhBlrgq1XBABoGLc5bttAEZyvuVKqmbZPSA+voUbEmttqDEBB2xwAlYLzBhEJARbeZyiwUhjCTQfpoxpSLFgHNA8PnV045naScfG0yGQEU7ImvrtNJH7OE/Lif/UwsmF4vbRStbgD2v8OvxeG1AcLE/s6pZWoU5TKAw4dRlhnCFYPK7z/3OfVQ0eMhQGA/neb2AswQPvkkfc1tXRgQxmJC8saxY4f4etcuAX54c2/HEZajkWS0GxhCeQ8fMYTxx3wCQjaWkRlCJ0AIECg57TQ6iIlGabx4veT3vGHD3MatHriwvjf/WBbmDzqkjlAeP66RLhaBUqYITE0hgQGMYxkq8MAb0OHzETvYiPGJROh+DYfpulcqdH3KZQKFHNUqcMcdwE03Sbm7pgH/9m8oBAeQRRheVNAfP4DcN75HzQxvvBEh5GzsoIYLLmgerNQDFp0u8wWEZFTXaT8PBoGDhz0Yx3JU4UEQeeQ8EWucdJ1q+5oNloXKrQjKZcohnObZSxWtyLM7nSEESMLb3w/kp9L4OV6PHTgJBoCjuT5LBg9QLuDmFNrpYXeHte9tdkBoMYTT08fUMO2liB4gXOzhAAgfeki97++6i1Qzjz0mDBqY8Rsbo8VB9pKwy0Y5gWSN9uwsPbfVHoQcTsYywSBgeMUKlcAAyjOJJo5/uyAOHpTqB4so9w8Bmpi6Ho+z5I0BYTZLiU5fn/MifsEFdMIeCtFYMUvI4fORXHQuMhsnhtDTF4G8vCYPp9r/Ay9xlEoAjhxRehAGV1K2UigAf//31GtLjquucmYyTj+d5gMDwmSSQELKvBzFIl23cJgSTPmWlk0x5L6gXm/7/Qc5mmYIfTaGsINqCBVAWBHofAZD1MbDjLlIRuXWE9WqqNO190bjuPhiGp8lS0jyNjtLj+drPBe5KNAEuAAs5sJiCDsAENodYq0efACy+ydRhA9ZhClB0zwIBDzwepuTruk6rW8jI+LwjPcrWTb6zDPA1q3UfueBB6QXWLcOs393PQAgBmpCn/vdo8BPfgwk4kghit/jZfTYMF3TN7+5+c/udvhigfgOryGUDw+5VrOUKQDQMIwpLMMRaH6x+Zx3nnOZg1vwXBodFblJuUx7Wijk/rwXO+qtkfa2ExYg7BBQ4VRv7fXS4bBvZgK/xsU4iiUoIoBgvx/BoDhsalUu2kkhr49AbXN663sP3cwWICyX0VVW6G1EDxAu9rBLRtesceznfuut1OiXo1AgTT+fCAKCXXrwQbW/luwwms/TPwZ1Pp97/yG34AVMBoQeDxCJakoxwSwGFwZLODWFOGI4glH4UUQuNKRsgkNDKtDi8PnU0z83FmTZMtoEWKYWj6tSRAaEcwl507H6pMWilukAACQnOqOOqZlgUxmlKf3apchkyMBANuQByAXxne90fq0VK2hsGBACsCy+AVHo7vMRICyXxWYmsxv2thZzkYsCC6OGUAaEwyVaCwzUMoRzAYSBAMnZNI2uzZo1NF5211+Oiy8WX4dCNP/YqAFovyE9h5uLXiYjA8LOk4zKLBMfPsmAkB1Gs4jA6xXSM9nBsF5EowQomGXigxXZWObZZ+n2PXLENNeQIv7H7wXWH4dB0LXKQSzCv8bFtJbpPmDdOmzcSLVuzUYrktFOZwgB2uNHjCn0I4mz8CiyiFo1WUDrjDxLiFetEkx8udzYUObFjobjZoIKwJxr7BDWAeHEEAKAr5JHJmsghzDyCCGBGHyRgPK4bgaEbETFLbmcZfVm+PxCMgpIltULM3qAcLGHA0PoBAgBVe1QKIiEc8kSmkS5HP28UFCNNHj9S6dr5aJjY85gpl64OY1Go1AA4UKRjRaPzOAFrIIBD3SU4Q/rCsByqh/ksANHt7jwQqpl8/tpM5P3rP7+uSepsskHb+CB4aji0pmcLjk8szOjnC8DR48qNYTloaX4m7+pddpdsYKa0rvJAJcvF4CwUiGWaetW+h1L2/r66Boyu8HJ7I4dosZXnsrzzRC6u4yKB3ZSDSG/d3n/HinQwpZEP913Xrr3IpG5N0IeHaXa0HPPJaOfejK2lSvrJ1RzcRgF6jOE1vc+GyDsAIbQDuYBqRfhgWkkzBrqPILQvZq1BjYLCCMRAt583zJDuH27YHdvvZXk8nfdBdx8s6qUmE37gI98BDHQySfLbcvw4je4mNDKeecC/f1NtZqQw23MMpnuMpVhgJTJANHiDIYwgxgSSKJfmWTNjpkc/f1Cns1/s6/v2ALChpJRwALzFrDoENmoGyCc3T2Jh3EulmICGqo4vX8fjkxoSs7RzYAQcN/bFFMZAPDpSCMqlEwLvI6wBwgXe9gB4erVOHRIOJzJdtJy+Hx0sg0QIGHZJzPq7J5nGPUdRtupS3PrRdjXhwXZnD59OInDWAEfitAA9PerkpN6rSAYEOq6u4QNoIT2jDNEM2zZdHDLFne2o9mQF2A+2Q8MRxSXzkRWV6nlDo7SwQmgWkHJNPkphwfw9e+GsHu3+rj166ntBM8Vp4hEiFXiOl05CeX6wf5+uoZ9fXT9GBBms2TxXq1COcjxel96hjCBAVShHdNklUGFz0fXTZGMmoBwBubJiHlTz4Ud5OBEdckSuhyN6ppkllCONWvq92RrJhqyFoBSQ2gAHQEwnBJUiyE8OIMEBpBCFCX44PdXLcBVb12TIxql8QkE6OtSif5mqUQHKzffDPz+92of0MceM/9+luac7/STEfnQuwEIhnB67Rbkt7wS2LAR0H2IxdzH1y0ajpkkPexEhtA+dtksrIUijCwBQskFvJ26Px5nViSVy3TYdixNShq6jEoPsgBhB4wd906U+7Vy3HVbAWXoCKCIU/E0Vg3nMDur+gC04vvQiVGvOb3fL51d+HwoQ0cB5k3WYwh7saDDBggrK9dgfFzUBOZytRr9apUOQ5l6v/JKsUBkMrTBPvIIvfRjj9Hj2RWRE7QXAxBGIliQDGH6SBpxDCIA0soE+tQdsBmG0N5uwikuuohcMO3g78ILW3zDDqEJY0PrpDEQ8qIaEDdXEv2q7rGDo7yfwEXJlIweiRxXc6udcgqZMjWSR3m9NA/4ustyXQaEDAQ1jZJauQfa1q3EaMi1hbHY3O296zFNhkG/Jyc9eqABjcawQxjCVEpcp1AICGfIVMZiMs0LPh826PbeWu0Cwrky8YB724lMhuaezwdrzCrwknNxBzCETjVNFiAcT2AaQ0gjihJ0BKUlUJF41YlIhK5NLEYHYICYMz/9KfDFL9Y+51e/ov85hx8cBLQvfRGlL/wzylteBvzZOxF/2WuhRcSF3rKldca5Iavb4QwhHxzzvZfNwjoJDiNLdVjSwLbDEPKcWrmSGs2/6lV00NYpDCEbIQGir6zXCwvMl6GjCF9HMIRu7OCuXcCz2wUL8Me4BZHhICIRoeQaG5u7ouJYR73m9EAdY5keQ9iLBRuVSk2hxBH/GlQqsP6Fw8BnPqMuAIWCAIBLl9Kp9lln0WZpGJRbpFLAf/0X/R8IUMI7NUUshqaJveHkk1t/226S0VAIC7I5ffpoFilEyWEUQDGo7qb1AOGyZTQua9Y0/jtLlpANuGx539dHG+98BI85j18ggFpAyL0WOjxKB+i+4jYg2fCwIn0+8UTgC19oPvFZurS2jhAQX/f1ASedRF8vWUJzkJmMG28kJ2C5PczGjXPvtSUnOz6fGLdKRSTSsRiU+qY4Yh0BCH0+Gzs4VIWWI8bCAoSe+QOEMijh/p/1YnTUubXEXOWigDuzm8vRPePoftgBAMNJMhoKUSKaP5rEAaxBCXSvBYI02fz+5hmicFgYcLH7Jysh7rtP1MDLwbXAfEsPDgLwepH9iw8Br3sdMDaGVEqda+0w83KCKu9pri6jiYQ64Y9hVCp0X3k8Uk9UGyBMYECpIWyW1ZUjHKbrxP4FS5fS98cSnNgNSuzzrVN7EboBwnvvhTVum/EYVuEQDgfXLyi5KKCukXaGEJCb0zs4jS7g6AHCxRwTE2r2OTSEQ3E6LonHqTl5Ok3J5fXXE7jw+4HLLhMyK256fvLJtECXSrTe5fMkw1mzBjjnHJp409P0c10XrolzBYTy6WAoBGV3mMLIgmAIE0fzSEuAMONV6YdGDOHppzcPTC68kGqgzj+fJKRvfnN7m7dT2BlCXQc06ai/CD8Kzz4/P3/sRY7SQXKsZFOZUmhAAYSnntrayfWSJe6AMBSia3XSSZT8LlkiDmw4uNaQYz6ZJpYXuTqNSlnFLAY7QjKq6zZA2F+0EDQ5jIrm7PMlGeVoVg7n1Ex9rg6jgMoQ2lmLfJ4ZJ5tJSQcxhHKSqmnmuj49jX1YjxJ88KICPUQfshWmiRucj46KMeeDDXbABugeyufp+2eeIeko39Is55Wb0tsvXTuAUNPEuMnrhsUQah7rARlE6F52QrDHIOyGMgCQnS0AJbqgEWSQ8gwoD2hnT9E08byjR+l/Vk0cq7C3MLDLRmUFBdA5hy9Oc61UMq9rOg0PqrgUVPezq6re0Nw3t5vDrVcrM4R2QLhYehH2AOFiDgdDGSYMeWIAwNNPkwzme9+jHk18ss0g76GHiAlcvlyYYhSLhDfXrKF97OhR4PBheh5jts2ba0+omgk3QBgIoFYy2uUMYaUCTE9WUYUHPpQQQQZxo3lA2GqMjFBSunEjAcK1a+fvtWUgCJi28lFVn5rYM4lOj2oVqB4+Ag+qKIBuwGKwXwGEdglho1i2TABCew0hJ70rV1LNzJIldO1kiWg6LRjDFSuAt7yljQ9mC1nm6yRlA5ghFJO4kxhC+TB3OCwy+PlsOcHh9YpksFlAeOGFqqHWypVCyjiXqMdaWE6j0ph1SsNsJ8koYL7/aTKVqcBLgDBID2pVesjsoK7TdWAzGQZ4ckeAapXu89tuo/cWDIox5sdXq7WXrt3aXQaEMuPlZE5i9Y7sgDEDag1lACA7KRKIMLJIBpYCkqN0O5JRQMwtBoTHUi4K1ALCRg6xaUQ7liG0yvfTaUSQQQT0g1251cpzFwJDKB92yu00ikV1z60xBOoxhL1YsOHSckJmH8Jh4NFH6Wtdp39sXpHNCrOLoSHg6qvpZMXvp4mWzwvHxYkJ8Tze8M4+u7237SYZ9fuhmMosBIYwnQbS8Qr8KEGDgUHMYqrYvGS0nbjoInFd22Fw3YITPTnhC0ZUQJg8mkOnB7ec0FFGngGhL6Ik983WNXEsX05zyesVLFe1Sl/za61YQf98PupnyIDQMOjeP+UUYu+/8pX5ATlAk06jPhtD2CFtJ2RjpKhHmBXNZ1N6OZYvp7Fqdj4ODwN/9Vc0nn19wAc/OD/vg8lPrmOy97VzlCB2AEPoJBkF6P1Wp2eQQRg6ytAAeIO0CbQKLGIxmivc7sMwaB5xMrx0KakjPB6xTu3aRe9pxQrxOgwI7ZdtyZL2G6Xz55YlsE5GQJ3UKgRwYQgnxXwLI4ukb1h5TrvXiBlCXn+ONSCUgQXQZC/CDhg3p8MXy1k8k0EE9E0KUYznhcuVx9P9hjKAuq9pWp3m9P7FVUPYBj/TiwUTLg6jsmFFJCJsuf1+YgLZSbRUSewekAAAIABJREFUog12wwbaXDdtonqmbdtoouVyxB5u3kzYk0/1eBFqFxByn6pyWT1N1XUsOIYwnQbSiYolFx1AHFPZCKQWWPMOCAcHKVFNp+u7Y7YavAjLiUMg4oUMAZNHC+j0KJcBTE5CR8lyGiz6wnMChCMj4mBldlYcyhiGaHzu94ueneefT3Py4osJWPz85/Rzv5+Y3fmKppxG7QzhMUp4GACx/E7ppVkSCeoMhuatB6Eca9Y0V6srx5vfTKVomja/tVC6Tp/f1aTEnqR2ACB0ZQiDVWRnCjDggRdleFGBp01A2N9PSW0sRuBv717RH7e/n/aqk04ioyZ2Ydy7l+abLE3k+39mRmV55+Lsy7ek10v/KhWa49b18KnOsFoHAAugliE0DCA7LeZbEDmkvKp1bqvrI4ddanqsAeFCYggtQGgyhACwGxuVhWls7Ni6us5XODWn57xWaU6v92oIe7FYwkUyyoCQ6/wyGaoHBATLx4kqF+kDtCHIdTHpNNl4A9T8l2vg/X5yKV2+vL23LSdPskzF4wE0yVQmjhjKR6Y6pvi+nUinCYT7QUDJjyLKHrHBhMPu/e3mEtHo/IJBoFYyCgD+sJr9dUMvwlIJJkWgWX0Uy9DnJBkNhejUnOdSqaS2nODWIgwIuZ7m1a9Wif5Nm+YfWADugNCxhvAYGV7ILSfk7wEVEBJDKAZrvhjCdiMQmH9jjHptDDq1rsmNIewrzSBb9ZtroAe6VrU+YKuA0OOhebZsGTF+y5bR+srmWZEIzSEZeExPo6adDDOE8wkI3eTZhYIJfKQLk0OoI8YMqAUX+TxQTdF8C6CAPEIwpBs8HG6vVIT/hry2dgUgtB++dAAgdJprToBwF45XFqeFIBcFapldV4awZyrTi0UTNkBYXrkWR44IQKhpQtrBjbIZEBYKlEiFw+qJ0VVXqU5j+/fTZsrmkR4PLUJbtsztrfMaJW/GxSIQG/YqKHGm3NcRC3C7kU4ZSGU0iyEENOUIfb7ZwRcz5D5GHP4+NRNOxNUei50YpaIB5PPUc8+MqqZKX1s9AQ8Gaa4NDAhjmWKRrlUwKFwRuTkzQHPw0CFi5DnOOqudT+QebvbcnDzEYiDDC7MmL44YgUG5CPklCntiKgNCPS/ez4slGe2kcDNNcGp03imSUTfnw1B8HGuxH6OYQAF+6PrcatG4jrBYBC6/nEodrrySDmVGR+l/mWUvFknpIsdLCQidmKZOAfFArWTU3oOQWk6IdX6uJmXy8481W9UqQ9gp41aXIcxkEDbrB5/FCQsSENoZQruxTM9UpheLL2yAcCJ6HCoVkUiFw2Ki7NhBCxwDwnyewIhdbjU2RvVNgGhKf8894nk+HwHNduWiHE6AkEFqTXP6Lq0jNAwgM5VDqhqGHzQoFV9QMcToJkDIOFYeMz2s7ujJROcDwnI6DxhVVGCOg8eLckVdSlsFhJpG0k9mA7lh9tgY/Y4BoaYJlhAA7r5bzNfRUbXOaT6iKYZQeqDV0uEYAAy75FA25/EVKdsxYL5HaQ7NtRF8J0bDRud2hjAeP+ZKCjfJKMbHUYIfXlRRRAC6b26AMBaja8ClB9yOIpcj5crQEPCKV4jHF4ui/QQH9+KcmVEPuOZDMmofMwvEd2hzertk1N5yIol+peVEu4YyHDIgPNYMITd2Z7l6oxrCjpeMlstAIS8ko9rxyoRcaICQr4O99YSdIVRMZYzOz1HajR4gXMxhA4QHPVQAwwnmwIBYcHM5YiLYKTSXE1b59pBlo6kUcPvtQp/t99NkPPPMub11BoTlsjglNAwzubO3nujSOsJMBqjGkyggCA8oWStG1AveTYDQiSH0hvyQ3ecShYDqBtKBUUoSPWABQr9fAR9A65JRgECfpgEvfzk5UF50EfUzBFR5tQz6ZLno5s3zb8HeVA2h9MA4zB8cg2TVLoNSJKMmIExggGS+5k0YiXR/k2WncGtObwFCpzYGqdRL/0alcJOM4sgR674qwA9vQKQt7YCLvj766IODtY6+o6O0p116qfrz7dvVLg+5HK3PxaI44AqH2y+DAJphCDtP5gu4MIR2QCgxhO0aytifr2nHniEE6o9bp8qzXV1GzXGLIAMDwIRvFeT9ed26l+wtvqhRTzKqMIReD6B5hGS0UJCo1IUXPUC4WCOXU+lvrxeHCsNKH7RYTEhnCgXgt7816wNM44Zo1Hlxv+oqkZgWi2qvcb+fajRkd9B2wq31RH8/lOb0MxjqWoYwnQZKM0mUTeDhQRWZqOpLz7Vl3RB80CgDQt2nSmDjiHW8Tr+cIkDIPQjh9yvgA2jPNIFbDvh81Otp+XJxGCknmjJDyKHr89N70Ol1gQYuo9IDO4khVABhgSSj1vvzzl9T+k4MNyBvsU3SgzrFtdJNMorxcWpsDqCAAHS/SFva7WcXi9F847MnPkzUdbqn160Tayv3+OSyCYC2TyZ6GBBu2DC3A5l6rK5j78gOABZAY4Ywhb55ZQjDYVoD1649tj0IOeodmnVTY/pMBpbUP4IMsgij7BcfKBB4cfwKjkU4mcpwKAyhWaJjAUKg4/OTuUQPEC7WsLecWLkSh8a9qFZpkrBpxcUXC8czngcsFx0YcNi8QT3sVq0S38v90ny+uctFAXdA2NeHBcMQptNAeiJr1Q/GEMdMQD2C7iaG0Eky6vVC2TATGOh4nX4pQfQYA0LD51OaxAPtMYRs4lMukyNvuSz6JMnAxUkWevLJcz9kcYpGDGFfnzmekgNiEb5jAgjrMoQmIJyBeSHN7HW+HEY7LWSG0LHtBGBdKKuv3TGsIzQMse/UA4RF+KEHxAPadau0A8J8nkAGt03SNLXljl02KgNCvtZzkYsCTTCEHdi+AGjMECYwMK81hADlGGNjc3+d+QgZyNsb00ciALw2yWgyiRpJyUscrm0nJEA4i0Fl3BbSWtmIIVQOLXw+IRkFOj4/mUv0AOFiDReH0XJZMICxGDETXBPIwC6Xc64f5NA0VTYqh98//4BQXoSjUdTWEHYxIEwdzSFgAsJBzGJKV60/uwkQyg3pOTQNMHQbQ9jhC24pRVkkA8KSL6J8Jq5JajViMQJ/vElxk3pZug3Q5mWve9u8ufW/10w0AoS8TnRC64l6pjK+PMkhBUNIA7TQGUJXcCE9qBMYQle5KADj8LgkGQ1AD4kHtQsuBgdVdUsuR4BQvh/kfapUAh5+WJRZZrPODOFcojFD2EU1hGayEEGG2BV97mPWqVFvjQyHoZxyWHPtGJs4uTKEkmQ0jphyuL6Qaq0b1RAqB00+vccQ9mKBhwMgPHRIgD42r/B4RL0f/y6fJzlNvROj172udnPXdTLOmOvGCbgzhOEwahnCLpaMpqcLFkM4iFlMaSoC7EZAWK2KIQoGgao0Xt0ACFkyyjWERX1uPQg5gkFKljg55pP34eFagCnLRoeHST71YoS8cQaD4n0UCmIzdQSEx5AhdJSM5qiIebFIRusZlLgCwmOYpLrKRQHkD8+gCFojyvDBI5UEtCs/5NrRLVvoHjjtNHot+X446yxRo1YsErGzcycd1LhJRucS3eoyah+7bBbW5LNqCKU6gblKRjst5DXS61U9DSwGTreZkxxj2ah9zNgAsIYh9C9MhrCey2gtIOwxhPMamqYFNU17WNO0JzVN26Zp2nXmz2/SNO1ZTdOe0TTtPzRN85k/v0jTtISmaU+Y/z4pvdZrzOfs0TTt4y/F+1+QYW85sWoM4+MiidI00WT5zDNpoeMksFQiA4x6J31nnFGbbPl8tAHPh+7fDRCGQlBqCLuVIczl6FrnEgV4QavWIGYxWVEvajfVEMpyMN40g0GgqquA0Jjs7AW3lCaGkJPUojc0b4BwYEBs1vz/6GjtY5m1B4jJeLFqaeRkhx0ZOdRehCJZncXgMa0h1HVY0nfAbFSfJYbQkox6FrZkVE54AgGRj5dKtUmqJRk9hgDD1WEUQOKwMHGoQoMmOYm0Cy6Y2R4bA974RgJzXq+6py1fLuTZvC8++CDtObkc5c4eD/3zeud+KNOKM2wnMYSOktEaQCgWyLmaynRa1Gs9Ye0LHcTGy2VBfM8Vi+b7T6fhQwk+lBY0Q8jusJUKgeFQSOyhuRx9bGvsfD4UEECJPQN6DOGcowDgEsMwTgdwBoDXaJp2HoCbAJwI4FQAIQB/IT3nAcMwzjD/fQoANE3zAvhXAK8FcDKAt2maJin9e/H/s/emsbJ1+XnXb+2h6ox3vu/b7e52u+N2d9smcnuQcdvOhyS23AIpDiEEh8RAGEykIBHFAsIHhCwwEihSpMSEYCUfggXYJjgmRIYMigmxhG06iTM4Jvj1/E733nPvmWreVbX4sIa91qq9d9W5t8a99yOd9z2nTp179qlVe631rOf5P/+VEbacePBZ5vN84Ts9zaPunzxR5NAshOfnarGs2oR2On50t3lsHXZR82/BomX0+BhvEjvUthOmjVt2mxdgntLjdp6zjSg6vA2t2fC5TepTJyRiSsLgg+uCn9wfTAPLaEgIX6d+ENTb9sGDPMLcbDDMfeji859XPdS+67vgm7/59X7fKljWZwv2xzLqKoReD8IExECRiqbVEBYR+XCTak+/d6gQVllGr5+p+02iVXlnfn/dwxfIN7imnP7hQ1+Jf+utvBZ+MlEbx7/yV9T5onl7m+d/8pNvnlZbpRCGbSd6nO0NIQwto/0+jVQIDTF2x87ukdKAEO5QIVzWg/CUPgI9jzthQHUihODPkVG0WGtd2XqiptgKIZQKpjNwqj+klPKn9Pck8PPAx0v/EYVvBt6RUv6qlHIC/Cjw3Ru78DojCJV5t6skB7Mw37uXTwAmwVBKFb390Y+utpH6nb/TJ2vGorMOxLr//HzuT2xxDMJRCK+5T/b+i/X80i3CEMJxb2Ifk0FT+kePXq9WbZcwY+UqAWdn/snC1fuDLV7R3ZH11ZhYhTA68pJTX3eTKkSu+E6n+QbjIx9ZfG4UwRe+oD42+R4ICWFp0qhzE+5KIZzoWyVNgx6EKfZim2IZvcsmdR9CZUoto70e1wN98KJ7EZrrPjnxE4vvCrOGmdcofC+cnMBXf7Vat0z69mAAf+EvrN8uCv4GtTAIKFQI96B3JBQohLczmKsHTxhwI+57p8d1riGEkiROveD1OUXCXqjxhYSw17M9CEOFsG6HZ8ua04eE0NYRtpbRN4cQIhZC/ALwHPibUsqfc76XAt8L/B/Oj3xBW0z/dyHE1+rHPga4TOZd/VjR7/s+IcSXhBBfevHi8AjBxhEohO/xMU+VePDAPxH6mq9Rm8/PfEZZSFeZHL7wBbXB7XTUJPmN37jeDZiZq9yJLcvg0VuptwC9uk1yOeNA0O/rWpXbPL5ySuIxqUOqHzRwlUGDk3N/Grp6NmZfMZvBfDAi0k2yAbL4aC0KIeRqoEkYjePd2oJXVgjTQCHcYQ1hpxPUDzqEMFcI1YDVbZNjUJWiZzmEY2Pb9SY1rP+0cHoQ5k3p1dy+jvYFrqpX9F74xCfg674uv8bpFH7sx+DXf109Zn5+HYTQvdfcg9TBQH9d1DvSNPjdIRZCZa7zm++EAbfxQ9xedk0ihNOp/r5+0pREHSTuuUIILNQQ1lkhhIpgmZAQtgrhm0NKOZNSfh6lAn6zEOKfc77954D/S0r5d/XXfx/4pLaY/lngJ1/j9/2wlPKbpJTf9PSQCq22ASkXCeH0LWYzteGNIl8hBPX1Zz+r7GkPHqzWj+bJE/j+71c/9w3foD5fJ8xc5Z4Sj0bw6LE4+GCZ4VB9iKHadR8z5Ib73sb7EN/WZsPnbsSOz31Z4OpF0NRvjzCdAsMhKRlD1K5tIrprqSGERUJ4//5m2kmsiiqlya8hDBTCPbKMpilWcm+KQui2nQB/3CYTXcOrx0wiGHG0nwrhq1dOD8IOSZITi3VYDw0JPD4uvs/efhs+9zn1u8Zj1Q4my9T/4zhfI9dJCGcz9Z41c6T52n3SPtR9GrgK4WwGo1t18wkkR4y4iXwmUTfLaEgsipvT5ycdPc72lxD2epygJvamKIRLm9OHltFWIVwfpJRXwE8DXwQQQvxnwFPgTzjPuTEWUynlTwGpEOIJ8B7wCeef+7h+rMVdcHGRN2ACOD/n3YtjSwiFUJN2SAgN7rKJ+u7vhp/8SfiRH1G9g9aJIkJoWmIceh3haAS3t5CM1Eb2IZeK2B64QmgmYXfMju/5xTdXL4OmfnuELANGIxKmahPNegnh2Zm691xC6IYmbRsmeMD0iSuyjO5Dyuh0qq4xjtWBVpFCOEdYtanuNYTLlN19a2NQGiozGPAcdUoyoUuSSPutdRALM4cW1emax+NYuVtubvwU7qdP8+tdt2UUyuo+c+shsHNCaEI5oki9JsMh9uY7RqUx9yJ/oOpGCJfda6Hdt8+pqr3ZEUp7EE6nMB7lllHx0HtSXcOAljanDxNiW4XwzSCEeCqEeKA/Pwa+E/h/hRD/DvBdwB+UUs6d539ECOX5E0J8s77Ol8D/A3yVEOJTQogO8D3AX93G31ArFPUgfE8s9CB0J+6Tk/wGuusmyiQ6rRuG87mb8dFIxfAvEMIDShrNMjVJDQYQD+tFCIsso0cPut5zrvY4U8YohImrENJZm2XUtJ7YF0IIy3sR7kMNYWXLCU0Ib7jH3Cx5cczZWXGqZR1QpRDa1hNhG4M9DJWZ3gzU/I1SCDtJXjO3DmLx9Kmqay9rcm4Sfj/5Sd+R8ehRrii+9dZ6rsVNfQyDZez6uWcK4bKWEz3OkFE+qKenb1b3uY8ICWFhc/o9JITuvTYY4PUgBLjsfATX6lu3w7OVm9O3NYRrx0eBnxZC/CMUqfubUsq/Bvx54G3g/w7aS/x+4J8IIf4h8GeA79HZM1Pg3wf+OvBLwI9LKX9xS39DfVDQcuLDD/2WEx//uE+0TF/Ce/f2x2ZVRAhzhTBoTn9ACqERb8f9KUzUkbTqQfjUm8UPkRAWKYTpPd+rdX0TqWPnPYRRCCHvQ5iJ1DvweBOF8PhYhciMx+ol+MQndr+BWkYI90EhNIEyZk4oIoS2fhAgimu3wXFRZYcqVS32QCEMCeHFs6kl8SkZUZJP9uuqRTs7Kz+wfPJEfU+IPCH73j3/d69DHTQoaz1hEaZV7oFCCMWE8JT+QsJo3eoHYblCqCyj+Wuw68OXUsuo04NwjuA6fez9XN1qCFduTt+gGsKCkOf1Q0r5j4CvL3i88PdLKX8I+KGS7/0U8FNrvcCmISCEHz7+Wubv55uq09Pi+rRPfUp97AvKFMJPfYrFGsIDUggNIRy9yvtvPeCKF91P4J7YHXINoavMdE5SFZigTQKX0zNn17pfMIRw7pylySheGyE8OlIBTp2OIodlysU24S6cq6aMyttbhE1U2DxWUQgv3WqDON6bg61NwFWapFxuGVWqxe4Ozcoso88+yA+Gjhh5k/2b3GerIk2V4+TiQs233/iNi/vBdRLCJFHrcKgQek9g/xRCGygTKIS3nHs9COtMCMvqrMPDlx5nO1UIi9T4kBDecA+Z5ofqZ2dbm8q3hmXN6XPLqLK43Ur9wGCglIddFvdvCAcWWt9iLQhaTrx3/jkvYTQMlNlXFKlNo5FWMA+4htAQwuGroX3sIZdcpH7/gUNWCF0Sn3b8dhpXPNhbW4axjM6cs7SZ8FfKN9momjCJz35W9f7sdpf/zKaxTCE8OoJuV0CcJ+kNONnqpmcpIez18kAZgDiqtULo2g/n8zLLaFBDuGebVFDhLQYJ052oTd/+7eq6vvzL4U/9qcV+g9tQCPNk2P1pXwArNqWP6tuDEFZVCPeHEJYqhI5ltM5N6Q2qLKOeQoiAJM1rCKG2KmFLCJuIMGG08ynm8zxQ5v79w5gAzA0thH/ac/8+0D18hXDwKg/+ecAlLyM/+eAQCaHZsLskPorwCOE19/eWEGYZMBwyJf8DZqyPEIJfM7jr+kFYTghBq4SprxJu0xYVEkK3D2ESzWE49C2jNVcIYZW+doGNrdfzX7gtoswy+uxl/kDEfCcNzj//efiTfxL+yB9R7pPv/d78e48e5VbSdaCsOb1Nhk3zZNgx3Z0TwtWa0ufbzLoFk8DylNHTU+xhGexnDWETCWGoELpr7XgczC9pMwhhzUTgFishIITv8vHKlhP7CveGPjrKm7mfnnLwCuFsBqPrCaeo+G6AaZLLRScnh+lYMBt2VyGMYw5QIcw3plMSXNHgTQnh8XFux9x3QihlHkL1YVBH+PEtblYrawjnqg7XKoRRBIhaK4RQbj/M3dha1Z1N85q062udyrVdlFpGHUIokDtTm9zN8x/+w/DRjyqjzRe/uN57tEwhNDbfcWDzPdpzhfB9vqz2CmEUqQ9zqB6Gyjx5wl6Hykipx01H6J4wUGuwc2BUx7kyVHZdN854HKzjaZrXEMLe7k/eFC0hbCJChXD8xFMID4UQuovn8XFOCI+OIDrqYFw2t5wzef+CTuG/sn8YjdTfkmRKgrnPNZc88nZLh1g/CMU2XyGATv63XfIQ+eI5GwimfWOYGsKM/HozGXvvrTctfdxnhTCO1TWNRmojMRyqjequk0YrLaMzpbRb0qPffHXcnLooUwiHQ2eDlyhCaGvSdkAIZzO1mTYba4PBAG776oGEKVMST23a1fgJAd/xHZv5t91DziJV9zJIhn28JwphpWW05jWEkB++TKeKWLhzZKfDohq/B4TQzJXDobYkZxlHjEiYqfnbWaQPYT94V4TK7p0IYU0VwtYy2jSMx759UgjevTmv7EG4rwgVQoPxGB697dO/l8+m+XHmnsP0IExr1oMQ/BpCEyQ6m0H3KKd/UxKGH+wuha0KxjJqQmUkMJn5MaB1JoSwWtLoXhHCqarFDQnhHmYWrRVusEwc58qFlIuNzq0dagfph2V20efPsdLvWzynx3nt1aYyNb7f1187tuxd946EVUNl6p0yCtVzZJKgXwO1xvU4U4u8sTVsGeH9ZpvSTyZ5D0IeeOPWBIUwjvPH5vNg7W2IQtgSwqbhvfe8L0dvf5IPnsW2B2EULfYg3Fe4JzzuzTsaweO3Ym+H8VI+PIib2O1BGI3VRtYSwuTwCaEJuzANxEGN372zufe8q/f6BT+9e0ynwGik1Ap06wlH1uh0FkMn7op9I4TLiu9B1wY599st51vdrFYSQq20W0KoSUVhimONsHST6nziKYRbRqld9Bl2IN/i+QK5OIQ16q6osozuW6sQKLGMaqLTlFAZWOVeE1Yl9OzZO8AqhLAJCmFYQwj+eusFuqWpei8btAphi1ogSBj91af/PLNZvoG6d085hqIDeGe4J+ALhPAxXi/CQwmWsT0Ix1j55SGXvOBpLRRCUOMmhE+czs79N9zVB0P2EcYyagjhhA7C6Y22DtXJKDlJsh9R36sohCpJL39/DjjZqtpUWUOoFUJLevTEsY22BbtEVXN6O78nQV+7HSiEZQmjLiF8m2cLLQzqSC7KQmXyViHBPbbnCmETQmVgkVy4dYSl99oOCKGU1YTwBDWhhwphnQmhm6PlkkD30Jo0YcQRmamyOwBx4XVwANv+FmtFUD/4y+ff4AXKPH58ODe/G63ukou8Of3hBcvYHoQj1B+CaxnNd0yHWkMI+Zi5k+/ZA992efV8N3aaZZiOp5BNbKjMhC6Rs+FZB8k4OVH34cc+9ub/1jqwCiEs7Gu3L5bRUCGMW4Uw36QGqsUOLaOFCqFm+o+5YMSRHbsoquf4hXXxBqOR/voQFMKGtZ2A6nvN9qgN7dk7IISmL6k5lAWHEGZZqWX0UPaEd4EZM3etCOsIrcVZT05WJWwVwha1QKAQvpN8juk0rx989Oiwbn5zU7ubCasQdgNCeEAK4WCApxDWpYYQikn88X2/4d7Vy/2r95zNYN4fETEn0zEyk+SUKMrrH9dBCIWA3/7bVcT9PiDc7BRZRgv72m2JXLihJOa95RHCibrInBCqZa9JNYRQ8vdqxWmXltEihVBKePEi/+Yx2jEQ5equ2MfUqTeEqzRFUUHdZ1hDeHXlNCncPlyFMMv0cGUZCVM6TBoTKlPVeiJ/0u7V+KJ6Xds+KLSMOkS+jjWE7vxo8gzCLIpSQtgqhC1qgUAleyf7ck8hPDRCWEQuRqPDVghtDHSDCOHpo4AQXsotXtFqMIEyKZnqAQZMkhPPXl1HkrGyQpjuRr0I1UEI+hBO1EWGltE6jpWL0MZWuEndA8to0Sb18lKLg1q1mJs2L5pcNJFYpCkgIrtR73OqyODt7XYv0oE7dpZYZBknDBCwoBDWddyqWhiE9bq7VAhLexBCZajMIe0JV4Xbv7osadQq2npxscEyrULYohZ49sx+OiPiV27fsgrhIRLCIoVwODQ1hIfXnH40ykkhwyFdxhwzVDWENQiVgXydcces+9Bvqnh1s3/H/yZQJmFaSgjrWJe2CiHcpUJoCKF7wOAphONbJM2zjFaRCysq7all1C5Tkwlv8yzfRNe8/rOKxLv1TOAccOzQNupaRl3r4QkDZkTqfaVJvBD1HzfzXnbnotwymt9rEvaWEE6J1f2m7zXTiqyOqCKEo5FT86onp2v0A61C2KIWcAjhu3ycIUdIqTYIJyfq45AIodn0uBNcHipzmAphvw9JrJq8PeCKCR11MqUnpSg6bAtHISF84O/Or3pJ7uPYExQphFl85FnX6rjhWcUyussaQhMo476ffELYZ0zXtgohiul29yOwZ5MIyYU7bjkh3H3QRZFl9Pnz/Jtv8yy3amm1qa4b1CoSbwWbPVB1DVzLaKgQFpH4Qwirex1UEYv53PQiVE+aE6n1Y08SfcMaQkt6NJG/f78547ayZbRVCFvUAnalhXf4NLNIzVxSKnUQDosQmhu62DIapIweCCEcjyGZjUHOOWHAKx55kVeHkgJbhiLLaOes41mLrub34OZmy1dWDaMQepbR+Lj2CqFpGC5lcfohlCiEO7SM+oSwl2+eAeK49uogVJPfOjsJAAAgAElEQVQLG7Wux2zIsVIt9sQyas8tsyxvOQF2k1r3cJJVkmF3Wfdp4CqEISEMSXxdxwyqFcLxuKRlyJ4qhJfo02Y9gRzSfvCuWNkyqp9o56Hb2531kdwkDnhb2eK14CiE7/BpplGKlDkhjKLDmriLFMLhUJ1qxUf5DrHPKePnu1s4V4HpQTibQTRSq+sxw4UehI8f7+oK14NChbDrP3DFg72zZZiWEwlTlXgITKKj2hNCKG+YvQ8po0sJ4eh2gRDWvX4Qqk+/JxP9tX6SRDDkeG9UC5cQ2pYTYCePQ1qj7oK7hJPsshYN/BYGRQqhJYQ1r/uERSLvEovJRBPCOJ8fe5ztFyGczWA+45S+WnvBnkAcshtpGVa2jCIgTXP1FGqpEraEsEnIMnj1yn75Dl/FTCRe/eCh2QPMDR1aRoXQzekdvHy+f8mVLkzCqKkfBEUIX/LY2y0dOiEsqvuMIv+Ba+7vJyEcDklchTDqNo4QhpZRKc0J+G76EK5CCK2aAhBHjSSEhZvUHbYKMQgto3aZkhKRTXjKi5wQ1lxtqrL5hk/atUJobMemhcEyhbAJhLBMIVR9WndPCMN7bTbTW40sQyA5Zug0pVe1EE1WCL33bAOa0x/Q1r/FG+PFC/upBP6/9GuYzSPm88NsOQHlNYQAjz7a8Z57cRk7Xqn9g7luwK6uR4w0Icz/wEMOlIHiMUsSvB39JQ+RL/aLEE6nwHCIRCD1YjmLUq+GsK5Ew10409T/OsvUBihKI5ugMKHDdJQFb+rNIGxKD4uE0Kop0BjLaJVqMR4vqrrbJPEuQtXixQt9KDad8oiXpEwVIRT5+6uuhND01i2yZ5fWfe6ohrCwKT1YQhjafOtMCENlt/jwJT+g3hfLqNty4oQBEXIhYbRJCmFYQ+jNM0mav6dh7w6s14GWEDYJjl30FY+4SD4CqD1bmqoJ+9AIobmh3Uark4laTJ+8FXs2jZc82nkj3yqYvfN8TqVCaGo9DxVFltHZDDpHObPKSBm+v19jZRRCQwZBEUIXdVcITb/S0DYqBJyeip0QjKUK4eA6UAibYRkNN6nL6pp2bWMz4xfaRUHX7jj97OpKCMEfN/c+sxlbmljsWiEsJRcLltFWIVQKoV/Csg+EcJWWE/cdl2TdsEwh9P721jLaolYI6weTI25u1Bv/4UO1qfvc53Z4fa8BM2/N5/7pTp40mk/CFzzxVNJ9g0cIB3kNYd0so0WEcDyGB2e+env9Xm+LV7UcJlRm7kybM9EsQriPSaMhIXRrmwCSwY1fQxg1gxC6YyblompRqBBeX2813VfKRRtb2HICDCHMN6l1JoTu4UtVENAuk2Hd6ylSm07pO5bRegcBwar27P1TCF1CeIIawNwyqtAkhTCsIay0jLYKYYuDhkMIf5mv4kreZzDI6we/9Vvhs5/d4fW9BtzT1GOnlZ1NGnVYxyse7fVNbAjhbMaiQliTHoRQHgT04L6/Eb16f8A+wSiEM/LFchoQwroSjaqwi7Kk0W01pw8JoUcGExCDfiNDZYz9EPI+sy5pPjpCNzpX24A+p+qJdpe4eRhS4To83JYTb6G+uOXcSyGuM7koUwgtIYzzGsJd9bODEsvofA7zWVBD2BzL6Gym7q1DUePDlhOgFULnXjs019hdcCfLaBpYRluFsMVBw2k58TN8G9czJWdEEXz918N3fueuLuz14Z6mujezbU6f1k8hrItl1A1jGY3gwSO/Gf3ls/2KdTYpoy4hzGimQrhPzenDGkLPLpoC/f6CZbQJNYRQbWWz8+UOa9KK7L5Nt4yWKYTjsam1Vk+YETOhszcKYb+PHVBLCKM8nKTOhFAI/15L0/yAwxL7MGV0D+p1Q1UXWoXQoMwyao+t91hceF20hLBJ0Cvtr/EV/AzfDlGMEEpZ+0N/CC8c41DgnqYWWkbToDn9ARDC6ZTKUJm6WEZdhXA8hvuP/U7h1xcZ+wRjGXUJ4UT611x3QliUfrhLy+h8ntc1uimVBoYQ+gphM1JGoXrDY0lYmFq5xY1quEHt9fL3U0eOeYhSmG+4521S60wu3DWt280PzqzNNw4OXfZAIZQyT6sEdZAZkvg6jxks1lm7hy/dLotz455ZRstqCJukEIaW0W7XGcc0YUpiW061CmGLw8azZ0jgL/Mvc8V9iCO6XXj7bfjMZ3Z9ca8HdxIOLaP37+MdPfc429tTHROEkyRa8dCW0dgk7Om/Q4jDn6DNWuPWNUkJZ4+PvOddvdqvRNjQMjon8shhFPmHEnVCaBktIoS7aD2xNFAmBXqLjelbQuhsdHZYkxZa2Nw91pPjPgKViN3jzLOx1fXgBaoDnPaFWICvEI5G2tmSZXQZkzBTARxRM0g8VN9r3S6aHKtT9x5n6hR0PN7qNbaE0EeRg8III1mm3tPWjaAnKWsbbQlhi4PGs2f0OOPX+G3MiRBxxFtvwac+5U9eh4QyhXA41BvUTkAI91QhNOrg0ZHmglohHKJZrp6MHj06rD6RRXDrLdwxO3587D3v6mp/JGtTGxKP+mSonfSEDpFzAn56epgq+yoIF053Q97T2T+FCuGGawhXIoShQhg1zzJa1HqilBDuUCF098enQm1QRxwxJbFqU6dzuOvVKqhqYXB0hBdOsqtWIVDdlB6aZfOF6lRf9bmwY7eLwxfXTWGuNawhHNFVCph+QpLUty4eFtc1ISpso3qRsUmjeyouvAkOfGvZ4k54/pwLnvCSR0giklRwdASf/vSuL+z1UVZDOBrpTeuBKISGECZJrkRFzPPTKK28HLpdFMoJ4ckTf+W5uo3ZF9gkxFEvb0pPSpTkDLApqgVUKYTbrSFc2oOwqIYwiWs9Vi6qNqlWBNgDhbDQ7jtVk2Lez67eTekNwnttgVjsiULojl0RIWySzRfupsbbvqg7vNdgUSG8QsuBetwePKjvIScsjhlUNKfXe0kbltQqhC0OGs+eccETLniMRJB0I6LosAlhVcqoIoT5atrjDPl8vxVCO/kOBhwz5BWaAXbqQwjLbL7dp/5O72rQcaL1dgurRI1dQugrhHUmGSu3nUiDzequLaPxHEajQCGMGqcQ7nsNoZnHJ06OVGfqKE1g7Yd1J4RVJH5fLaMhIZwSq/eTHjMh6j0/QnWA0z4evkAVIVTrWp0DZSBPNzYOIFisIyy1jO6puPAmaAnhgePlyxVTwudzePGCC57wgqcAdI8jhDhsQhjp1PQw6nk4VBNf9zQ/oZQIhs9vd3CVy2EIIaBm7mmWB8o4Ho86EMIoUn/SfB7YoU4TLzDhivs7s0OFsIvpqGeLyid0EA1RCFevIQwUwh1YRt3T3nSuPIh2A6ZTD+tsg3JRRQjtUO2RZbSSEDZMISwl8XFgGR0M/FOQLaHKMmoVsDjvQVhnpQlWvNfiPVUItWX0Es0AHYWwznDts2WtJ6xlVL9w1jJ6deUvNjVASwgPGKMR/ON/DL/0Sys8+eVLmM34kLe54iEg6B6pO+GQCSHkN7RLCA3BOnvgp0D2Xgy3dFV3g7leKVlsOZGkmGL0OhBCKB6zNMXb2V/xYG9O4SzxGN5YhTCjQ5T4NYR1xTLLqJS7SRldphAm2nZoyY5+49V5rFyspFrs0Sa1mhDWv8E5rGAZRSyS+JubrV2fQZVCaDfNWiGsu10Uqu+18PBll/eamSuzLL/f4mxEl3GhZbTuWNZ6wr53RQRJ4jenf/VqK9e4LbSE8IBhSISnLpVB9yB8h08zJYYopttVBOPQb3pzQ7sTsFmgzh756QO9i1HuDdgjeD0Iw6b0zm63boTQ3cgXEUL5Yj8IoV1MHUI4oUPkBDzUWSEMF800zRfO2Uy9f3dRQ2jIX2kN4SwkhGrJay2jizY2u0ndokIY9rLzxm6i5IvWMpp/L1SarM13hy0MQoXwlH7jSDxUBzjZIDjHnj1H7PTwxXWWnc5vEdASQioso1D75vQtITxgmES26XSFUqtnzxjT4Tf5ciQRxIJu9/DVQShWmywhfJB4XpXeJHVWr/1BdVP6+vQgNCgaMykh7eZjNaHD6P39OIEz91o6uA4IYTNqCMNNKiyqhEohzAn9NlJGzQl3aQ3hdIjESettqEJYtEkNVQu70dkXy2gWEMKGWUaLxiys+9xFLZqBS+Zd66FtSg+tQkj+WOig2HYPySpCeDJTpTShZbTuNYSwOG6lllGwzekt9sTBtC60hPCA4S6e7ueF0IEy19xHIkgTdc9/5Vdu9BK3gkqF8FwsBMvsW+uJyUQRwSTRk1KDFEJ3zMZjeHjm18Jc/dZ+1HyORsB8zvHwVZAymiuEdSYZbq3ufK4eCwnhLhXCKkI45BipLdfEMUdHh9+6ZVVUqU2hanHLORJ2skktDJWZ9Ox1qSc1Q21aLRlWfbKLICCDQoVwMvEJYdwcQljVLmQ81geGznrR42xvCOHpVF2HVQijViGEwDIKkKS+ZbRVCFvsC16HEJrFNU0kQsBbb23u+raFMoVQysNoPVHWg9CGytSYELob+dEI7p/7dt7r91dJTNo8hkNgPOaIYU4IxTEiakaoDCxPGg1PwHucqY3qBi3aqxDCsAdhnYl7iKrNjm37op80J1JK6r4ohGN1GNR0hbCqFm2XCmFV24mQxDeBEFYphJPJ4oGZnR+3BNs6SV+Ca5Q6zdR1NC1UBu5oGe0EhHDP9pJvipYQHjDuRAh1D0KzgJjJqg4Ew9zQQuR/13zunMrteXN6LQhyfOwTwpSJsidoG54Q9bFwFBHC4RAeBH/f5Ydj9gGjkfrPMcM8ZTQ+9pSmuhPC8AQ8bE5/ekouJaLVi9ks71y/ASytIcwGfg/CuCWEBpOJ3qg7m9Qb7u1PH0JdQ9g0+2GV0hTWfe6qhlDKnLAWpYy2oTIlCqGTor3tliGVCuFEWfvtuLWWUaDYMtoSwhZ7ibGzV15VIbSEUL/p60AI3QXU3ewNBiUK4Z4RQrOYnpxoQqgZYobe5errf/DASxw/aJQphA8e+X/g1fNlb+zNYzZT91c0GZEyYaLHZRIdeYSw7kRjJYXQeeKm2xhIuXjqDYukwoalAMTN6UEIyzep5+d4L94t5/ujEI5UcmbYwqDuBy9VCqGdb+LdKoQuGRTC2Ytkql1S02y+sFwhVJbRQCHc4riFh2deDeFYEcImKoRmD7KSZTQkhDqssS5oCeEB466W0Rc8tSeK3eP69LVzF1B3s9fvFzen37dTnTKF0ChRJlSmDmNlYMZsgRC+1fGed/1y931+rKV31iejY+vRZnHq9dZq2kY1JITHxzq/ST9xTJcZ0cYIhmsXdcfB60O4oBAmtSfuLqo2qUWE8IZ7O00ZLSKETatHEyKv153Nqus+d6UQlqbDZhkpWeNUXVh++BJaRretEIYBXO691h1dI2GhMX0TCOFKh2YGnU5eaw17Jy68KVpCeMC4CyGcPbvggseWZBxpQvjo0aaubntwFUKXEB6iQjgaYRniwCQj6uuvEyE0Y+YqO6MRPPjIkfe8fehLbwj70Xxg6wcBpsInr3UnhMua0wuxmDS6yWCZovpB93GAZNz3awjjqFGE0Cg487n6KNzsxBGmz+kt5+pGHG/eqj2bKdJjrhGKCWHT2k6Af/jijpk9+NhxDaEbKOMq9QuEsIE1hEXKbq4Q7i5Uxtxb5rrcebIzuqHPKVP0H6HDt4785biWWFZDmCTOvjJNkYjctVAzhTCp+qYQ4keApYkAUsp/fW1X1GIlzOf+SfgyQnj5wYhbzpAIYqbExyn37vkb8kNFmUJYSggvfmu7F7gECwqhfsAu9g0hhMMhPPgy3893eb37MyujEB5Pbz1COIv8m6fuhLDKMmrKBE9PoR8kjZ5vqPXEKoQwnYSEsFk1hKDGLcsWyYWtITSNzqfORv76euOJY2HCKARjN1Cb5abaDycT9Rq5xCInhOpF27VlNElyYg8QT8dEyIUgoCYQQlM+XXb4UhgqswPL6IJCKCXp6CZXBwHiuBHqICyvIQT1/h0MsG6zG+5xTm/vxIU3xbLd1jvAr+iPa+D3AjHwrv7Z7wb24Ay/eQgPcCsPdKXkxXPJrV7sO2SIbqc2BGOpQrjHoTLjsVpQ01R9FBLCpL6EMIr88Tv7Mn+3d93f/YmFJeyznkcIs4YphOEJuPv3mnqUMGm0z+nGFMLwxNvAIxWjW98y2rCUUfDvr9Ayascw7EW4hY1qWD8IiwqhxJkH9R9S9/sMysfMYk8UQnPYYJBO1WRpFZSoWX0/y8bNKoTxbgjhbKY+oqjY5tth4rScUI6BphLCkMiDcwil95L24GyP9pLrQOVuS0r5A+ZzIcRfB/5FKeXfdR77duA/3dzltSiDWTi7XfWmrVQIb264yO5xoxf7DhnEcW0IhpmEixTCx4/Za8uoqw5K6RNCO+nUsIbQHbOjo5xQHD/1CeHV+EitXKEMtEVYy2h2y5W2XEtgin9NdQ8rWWYZtY+l2+lFuJJCOLoNQmXi2o9TCHfDc3yc9zuV0hlD/SQ752zBq72UEPYv6WlXCwCRGru6BGtVocwyOpupv3+mnzAlISMh3bK33lV3iwihrX/Xqu7x8TavbncwBHk6LUkZTXdDCIvmSnuvTSakZHzIR9TXDUoYheWWUXCSRtOAED5/riZSt4j9gHEXP9a3AD8bPPZzwBfWdzktVoW5mc1paZZVtPvSLSd6hhAmc0DUon4QVrGM7m+ojFs/OB6DnCtW2GHCJXqAknoTQnez0D2JvRq0Kx7svPmrtYxmN1YhzEgRcb4IHB/Xf6NaZK0xIRfjsaPQhwrhLi2j416j205A9YbH2qNChXALBCMMJnHLIASSZHCTXw9AHDXCLgrVStPJCTttXwD+2IVtXgDVzxKsQtgkQgjF42bb8mjy0OdUTZxbqNctclPYcZtMfIWwQQmjsFwhlNKxPMcxRFFOCCcTuL3d2rVuGnchhP8A+C+FEMcA+v8/CPzCJi6sRTVchTBNg8LuEEHLiW5nDtSHYBxy2wlXIRyNUAMr5xwx4hWPdGFCfRJhDcoIYaeDN15XPIBXr7Z7cQ6kdFJGJzkhnNAhipvTlB4WLaNCLKqEYZ3M7hXCm0aHysCqhFDdkLu0jHrjFk0Rcp5fjxAgmkMI3Xst3MQXHrrsMFQmJISSRYWwCeEk4N9rSZKvc/O5WeeEJfPWubCFsSvq1xoqhGEPwqYSwiRZPKTK5x0BacdvPbFH+8k3xV0I4b8JfBtwLYR4hqop/HagDZTZAcyhUreb3+RltlH5oSGEJ/ZnoD4EY3nbCd+mIS8v/USeHaKsB+ExQy544u126zJeUE4IpYSkkxOtMV1G7++OEE4malHodCAe3HqEUDSMEIaWUVhsTr93NYTD2zZUpqL1hCWHO7SMmveVZxeV6gur7sbNrUWLonwZkNLcY7kdYdcKYdjmxa2zJorpdvH6tdYZ4aGZe/hil/Id1H+GLSdgsYbQXk8D6z6F8MORQttoZXP6GiWNrnybSil/XUr5rcCngd8DfFpK+a1Syl/f1MW1KIe7GVpGCG9/4xV9ThhxTMSctFsvxakqVCZJoHuUV1JLhLKz7NiGaFCWMNplrNQxxz5ZF4svlBPC0QjuH/tS9817u7Nk2PrBI6DfdyyjHaIoJ4RNWDxDYgErKoS7tIwObxYso02tISzapIaEcJcKoUcIUV80NZykqoXB8THePXbL+d4ohMlkkNtFAeKoMXZRqD58sZ9rMm/f21s4fFlFIbTjphfnJs2Ty5JGvZTcTtoqhAZSyt8Efh54VwgRCSEacvazX7gLIbz4zQEZHcZ06TBB6B+oC8GoqiGE/a0jtCEyLBLCOUKFKWh18/79erQIMXDHzN0wDIdweuIXww6f3WzxynzY+sFjoNcLLKP51NcEhTDcpMIiIQz7EG5SISwjhJ5i0b/yFcIGpoxWWUZD1WKXoTK+QqgsMLlCqO61poxdqMa7Y3Z8bJ6gDqR6nKmJs7RmZP0oTRmd9HO7KNh+dk1B1b1m1+99Uwh1DWFICJtM5Cub04eW0SYqhEKILxNC/BUhxEtgCmTOR4stwyWE5s1bVpv84rdGTEgtITQMsm4KoUlkM4FPo5F6bF/rCEcjRQq7XfU3uIQwQxPYGvYghHKFcDiEk1M/sav/YrDFK/MRKoRmszOhQ5S0ltF9qyEMa6mTQVhD2DxCGI6bqxKEm9RthspU1hDO1WIWtpxoythVKYSKYImd1H0aFIbKSEma9X2FMGqWQniXe00l6LKTGkLThgJAZBNiZq1CSDkh9CyjrUIIwH8HTIDfDfSAbwD+KvBHN3BdLZbgLjWEFx9OmdBhTEcTwvqRDHNDS7lIMPa1F6FbPwhajRqa2G49I2m1pS5qrkElITz34zoHL/pbvDIfnkLoWEbDGsImbFSLLKMr1RBuwDLqEj+XEHpNsmOIBr0FQtikjQ5Ub3bs9/WTzDqxjU1qmDLqKYRzdeM1ua4JVqv73Kb10KDQMpplvvVQ97NrEiFc6V6L85Yhky3da6FC6B6+dOYjBIv1uu24KYxGgWW0DZUB4FuBf0tK+QuAlFL+Q+DfBr5/I1fWohRmMySEusGXEsIXkj6nSCK6jKDT4eSkXslfS5vThwrhHlhGXbuo/Vo/ODGEUFtGnzzZ8sVtGGW9I4dDOLnne2OHr4ZbvDIfnkIYWkadYIcmKISrWEbDPoSbsoyaPnpJ4gdWeCpTqi7KryGMGjFWLqo2O1mm7VBhTdquLaMzdePlCmGzLKNVY2Y/30FapUGhQrhACNX8WKd9xjJU1RDaXoTOvbatXoRhAFfRvdYqhOU1hJ5lNFQIm2gZBWYoqyjAlRDiKdAHPrb2q2pRCfe0R4jlhPDFq9guGl3GkHZqpzi5m9Xi1hNBDeEenOqECuFwmD84MJNzUj81F9T7NorUpt5dNIdDOHngx0b2X22+T1MZyhXCtJGW0TCNrbCG0OmRNuAEbm58FrkGrBQok8K8F4ZcxI06+Ybldqh79/A2qTfc233KaKgQNmyTWmUZzcNJdkcIV1IIG9aUHqrvtcnEEML8IHFbhDCcL917LZ0qQhjW67bjprBgGW0VQkA1of8X9Od/Hfgx4CeAL637olpUIzztqSKEoxFc3iaMOEIwp0MGnU7tCEaZQpi3njgshdBOzlptqRuBh3zMFgjhQ58QDq53U6Y8m6l7Kor0NTZcIYRFK1uhQiiEfaLd0N+sNxjI/P5KQhjNGEx8+/HJWWRrjJuCZapFoUK445RR0+A8tIw29T5zxywPAtqzGsIFQthc62ERkS9SCLfRMsS115vr8SyjJQphE8etzDJ6dpZnU5CmDDhhir5JG6oQfi/wd/Tnfxz4aeCfAP/aui+qRTXc+kGoJoQXvzUky6QOlMkQQiVX1o0QViWNHmINod0IaYWwbpZRqCCEj/yVaHhdIn1vGLYh/ZFeDIIawihpVsooLG54Cgkh2PetPdhYcx2h+f1hfzOPEMrJQsuJ09OGsUGq205MJosK4a4so0Wb1FYhVP93x2zfFUKbMtpaRksUwu1aRo293jjKzLUYmMOXtoaw3DIqhLPG672kPYjZg73kunCXPoRXUspX+vOhlPI/l1L+x1LKD5b9rBDiSAjx80KIfyiE+EUhxA/oxz8lhPg5IcQ7QogfE0J09ONd/fU7+vtf4fxb/4l+/J8JIb7rrn9wHRAqhKaOxk2OMrj45UsyN2E0TQFRO0J45xrCHd/Es1k+0ZjJx1UIrSVBK4R1Gy+oIIRP/F1f/3a+xavyrwWcxaHXs5udjA6iVQgXCKF9rcJo9TUTjLl+S1QSwvk43yxDIwNloLqFwS4VwspQmUwFSTW1hvC1kmG3RAilVGNnjACtZTTHMjU+TGHehkJY2XKC/F5rFcLyORIc26guP7J7tBcv8hqKA8dd2k6kQogfEEL8mhBiJIT4Vf11Z/lPMwZ+l5Ty64DPA18UQnwL8F8Bf1pK+WngEhVSg/7/pX78T+vnIYT4GuB7gK8Fvgj8OSGE7wlqAEJCCOWtJy5+9YYJKSO6dBjbH6qbBXGpQrhnllHXLmpO7QoJYU1TRiHf9CRJ/hqMx9B94rOrQX83hNCrH0TVo010O5AJHaI0n3qaslENF844zl8fKR2Cpp9oT503RAjjYPb3ehDORr5CGMWNIe4uVrKMOi/kDfeUxXe+uftOyiU1hCEhbFjKaJX1sKh9AbA1Qugqu0IsD5VpErGoOnyxCmG8XYWwsik9qnfklJgp+rriOC+TaAhWIYQ2aTSOIYryPdpksvaSiF3hLpbR/xr4DuDfA74O1W7id6HJWhWkQk9/meoPqX/+L+vH/xLwe/Xn362/Rn//dwshhH78R6WUYynlrwHvAN98h7+hFigihGW20at3e2R0mNC1gTJQP8VpuUK4X6EyYf2gfWw4RCIchbCeoTKQT8Lzuf86pGdHuIVewyzJX7AtIhyjST8/Vp0SI+LmKYTLkkYth0hzQihhJ5bRZDZufMsJUK+RCXAyvVoNLCEUkZ1EbzlXT97gJseMnwkqgtVsbE0hhG7oVjhmdtqJt987EiqsvqFltOEKYRiYVqoQbnjcVlEI/d6RsXdQ3QRUWUbNwXBlc/qa2EbvQgj/FeD3SCn/hpTyn0kp/wbwLwF/YJUfFkLEQohfAJ4DfxP4FeBKSmnOdN8lTyz9GPBbAPr718Bj9/GCn2kMwhpCKCeEl+8PfctozZrSG7yWQrhDmT+sH5zNYDKWMBzaOjUA0oTz83qe1pX1IoxiYZVR0Ivmq1dbvjq/hpDJhPE0ny5nJBDlK2ZTiMay5vTDoR5LZ9Mz5Hg3ltHpMCCEUWMIRQh3w1NYQ+g8yW50NqhchKQCgk3q+BZw1K+GWUbBX9Pc+d8Swh00OIfFsbNzQasQunlaC0R+VymjSxXCcW+h1rop65nBnSyjUNvm9HchhGXnBSudI0gpZ1LKzwMfR6l6n7vD774zhBDfJ4T4khDiSy9qMlgGd1EIn30wRSKYkPLS+78AACAASURBVJIyrWVTevA3qoVtJ7TMD5pgTCZwe7v9C9UI1afRCDVzz2fMidRNFUUQxbW0i0IFIYzwetkNONkJIfTGyAmUAZhGHdypr2kK4Z2b02/JMhoSwsVQmbVexsHAHbdCy6jzpG0oTkWEMLSMSlggF03aqLprmjtms5l+3TSx2GaDc3M9UKAQTiYkTBdq0ZoUKgPV91qoEG6DEC5VCMe3bWue4L1caRmFRYWwJkmjdyGE/zPwvwkhvksI8dVCiC8CP6kfXxlSyitUQukXgAdCCHN3fBx4T3/+HvAJAP39+8BL9/GCnwl/zw9LKb9JSvlNT58+vcsl7j1WJYRZBq8uJBLISImZNa6G0LadADsj2lPnHR4UFPYg1AzExhnXtAehgbvhWSSE+eo15BhevtzqtUkZKIROy4k5EXOndNntBVp3rNycPgkI/S5SRqfDNlRGwx23wj6EzpN2pRB6hHB0w5BjpDl0iWOOjhYPAOqMsjHLssUgoG31s4PWMroMZeO2q5TRpQrh6LZVCJe0nYAlzelrIjrdhRD+R8DfAv4b4O8BfxZF7P7DZT8ohHgqhHigPz8GvhP4Jf3zv18/7d8A/lf9+V/VX6O//7ellFI//j06hfRTwFcBP3+Hv+HgIeXqhPD6Gia9jBkxMXO1tHY6pGn9FI2qGkK7YdV1hHaTuMNgmaoehBmaDGmVrI4tJ6BcIQR2bhmdTJQK1eno63QUwoyEKLCLNqXeYpll1Dan35JCWEkIs0GrEGpUWUbtWrDFmrQwYdRci0FnfNt4u2+Z0lRELLaVDGuux72+NlTGR5VCeHaGmrT0gtHjTA2oYR0bwN0VwqjRYwaLbScgbE6f1lIhTKq+KYT4XcFD/6f+EKhQGIBvB/72kt/zUeAv6UTQCPhxKeVfE0L8U+BHhRD/BfAPgL+on/8XgR8RQrwDvEIliyKl/EUhxI8D/xSYAn9MShk0Wqg3sizvKeNuhswi7y6qV1cw7mWM6JKiZ4BUNaWv2wbWPZVLU/X1dBpMyo5CKAGxo1OdLFMfcZyPm0sITZJlnRNGoZwQSsmiZXTLCuGylhNRlNefNmmjWmQZXUkh3IVlNBv4pCJqLiF0iXwc5/PjfO4coKWBQrhly6g3dqOQEDZv7Moso0WtQrapEJpxWkoItULYZMto8eGLUIcv0yx/j19fb+yFKhIQPIVweLNwcNY0QmjCrebz/CBYiFyAmc+XWEZrohBWEkJyghbC7IYMMfxtVf+IlPIfAV9f8PivUpASKqUcoUJsiv6tHwR+sOr31RlFN7f7tdt24uICskHGxATKAHTSWloQ3cVTCLXJMSF5WaYm5rGun5wTMeKI4x0phFUJo+AQwhr3IIQlCmFoGX31K9u7MBZbTvgKYaqCbwie0wAUWUZdt0GuEAYhTruwjE76jScVBkWtJxbaUIQK4RYsoy6hDy2jLwMy31QbWzg/FrUvUArhr23lulqFsBpleQZWIQR9IpP5LUPefnsj12PGp1AhlJLO8JprnE1GAwkhqCHJsvwAptPJ99NerTXU1jJaSQillJ/a1oW0WA3LCKG7qL7/PjAaMeNMBcroJ9ZRcXLJBfiE0ATLjIOk0eMd3cRh/SBoAqIJ4cgsqA2pIQw3PPM5HqEYcIK8eLlaetWasKAQhoTQsYw2iWQss4z2ettVCENC6PUhnPSDk++ocaTCoKhGxsxD9jTc6R05JSbZZajM8HrBMlq3ModlKFMIJ5MShXBP2k40uTE9VFt97fyjn+QphBtCpUI4m5HKcT5mQoBo5jwZEsKjI58Q+pbRNlSmxR6gjBCmad4k1nRT+PC9GUzGzIlJyI+J6kgwQuVin5vTL1MIx4FC6E1ENUIZIRyPoXvsT03DFz22iQWF0AmVyUgRcfNaTsBqltF9SRlNJoNWIdSoCk0oDbvYMSFsgy7U/8O2EzatMo4wSce3nKtvuBahDWH1UJk2ZTSKfGXOjqNOiPUUwg1gPlfvnygqsWdPJnSYLCTDNo3Ew/LWE55ltKYKYUsIDwxFPQhBkUEz8ZiF9flvmJCShISpeseLqJaEMFQuCltP7Elz+iKF0A+V8WsIPatCjVBmiRoO4fTc1wO3TQiXKoQNJ4RVKaMhIdyEQriSZXTcawmhRpFl1KCoJm3TISVhqIyUwdj1r/yE2AbWfxbVfYJ6rdR8KbZGLFy4hNAbtyzz205ErUII/l4tSXR+g37SkGNmRBsbt6JAGfdxQwjt4UvcvPYuBssIod+Yvp6hMi0hPDCUKYTuY+Y5L95T7DGjowhhWs+m9FCtENrWE6FCuCNCuLpCqK63rlapMoVwOISTc9/N3n8x2OKVLVcIw5TRpqDIMtrp+GpBt4sXCtTndO01hCuljLaE0CKcHwtPvx1CeMO9rSqE3rilIIahutvclFEzZu6abw+qnOb0wNYJ4WyWO5Li6ZiIeasQVoybtY2GB2YbGreilhPmOswnvs23VQiLkkZHI/Wa2GFLO0zo5Hu1Fy/yG+GA0RLCA8OqhPDmBvqXGTEzMjqqB6HepNWxhlAItTmUcrEXoVUIO/tlGS1TCO2CqserrgphFSE8vucfaQ4uN2+FMpjP1T3k9Rfs952U0ZQoaSYhLLKMCuEfWkQRenDVazTgRA3qGqPVV0oZHd60KaMayyyj21YIw1AZzy6aSuiH9Z/NG7vw8GUfCWHY99McmgEQxXS7i4c2dceye03Zfbdjzy5TCF1V11MIo1YhLFMIhXBso3EMUZSrhFmWh1YcMBp2qx4+qgih23ri3XeB8ZguY6YkamuW1jukpKw5fWkN4Q4UwslEXV+S+J5+EyozJ8r7ECYpQtQ3tKSsMf1wCKcP/Tf48HJzfZpCuKlstj3LgmU0nzqbtHgWWUbBf4+qDWDuYbekbI3320qW0bbhskVILvZNIfQIYTyD2SwnOTrooq7zYBmqVN28Fm17ybAG7th5IU7TQeP72cFye/Y2m9O3CuHqWEYIIbSN1i9YpiWEB4ayGkLwW0+YhNEOY6eNgdqg1b3ReVFz+n0JlSmyi9rHh0MmpERo+SNNOD2tX89IA1chXLBnPPR9Rv3r6dYsGUUx3QuhMkk+dTZpoyqEGjejxBu4r4F9v2pF3pKyNRLCVSyjYjjwFAuRRI2zrxm8Vg3hriyjUu1WQ9WiSfcZVCuEdv3foUKYpkGIUzb0CWEUN/J+qzp8yQOccmvDJgnhUoWwpIawJYT+uBlzixfwV8NgmZYQHhhWtYy+/z4wHhMzZYaefNKUOK5vauVShTDsjXZ97R9NbwGVhHAwYEI3J4RJWlu7KPiEMIpyUiglpMcqAMlgME1VMegW4G54LByFcNLgPoSwvPWEha5ZtgrhGk9QV7GMTvu+zfj0VNT2cGUZltnYlEKYv5g33NuKZbRQIZRq92XfN1qNbxohrFII7dwU9o7ccOsJ07g7itSH3/dzkJc7QKsQrqgQ9jndOCFsFcLlqKohNEJM3ZvTt4TwgDCdqsk4jhc3QpDf9KORfm+Ox8yJVKAMQJry8GH9FadShdCJYrUnqltWCYsIoZSuQtjxFMK6BspAPl7zuZucp5B2BIvN6V9t5bqWKYRTUqKGKoSwvDm9FXI3qBCWWUZdkjoZZN73Ts6au9ytlKBX1HZiQ6p8mDLqE0K1+8oJYTMVwipiEVpGt6UQViq7WX9BIWyJxYotXjZsGXXXMtOKAkBkEz8ZVh++NNFaf2fLaKgQtpbRFttElTroPn51Bbe3aoIec1z7HoQG7ka1sO0E7CUhnExgPs5gNmVGRIS03rw6K4RQHiyTJHhJlQNO4OXLrVxTISEMFcKkmTWEsLwXoSWKG6whXMUymg2m3vfO7jV3uXPvMykXVYt791DWTK3K33BPvZhrDAIyMHZjYz+GgBDOA4WwtYwC/pjZA2Gt6m6rhrCSEE4WawibaBldphCGoTLbVgjdey2djxHQKoSsZhl98MD5gbTDFc4DrULYYpuoqh+E/Kafz6HXg+7klgHHnkJYZ0JYFlIyGDibiZAQbvkmLiKEJlDGQ5ICotYKIZQTwjjGs/j2Od2pQji/7dta3CkpwrHXNW3xXGYZzQmher02WUNYZRkdD/zkm5N7BbaKhkAI/8CsULWAxZCSDVgQw4RRCIjFLFQIm6lauHMj+GOWE0I1XgNOmCN2RwilJJ30WoUQNTZC5PbafVMI3XutM1PtnNo+hKtZRn1CmPqEsFUIW2wTqyqEkBPCISceIaxjywkDd8MTx35Nmts/BvaLELotJ6SO6q97ywmDsub0ccyiZXSHCuG4l6+icyKPEDZNuVjWnN5uOByFUMLa7jUp1YcQi/b3KkJ4+iBIVmgYXCJfmDIKdnCtFWqDhNBNWfYUwpmaC0PLaN0Px0KYOj2jqLrruy2L2FItmvt7oYAQTqd+LZqIQIhGKoTgz5GFCuEOawg9hXCqpK9WIVzNMuoRwk7KNU4gR6sQttgmlhHCOM6joEcj6I5uGNFVPQgBkuYohOCfctkT1U6gEG7xVCfL1LXFsT+GLiGcm1tSq2N1J4RlCmEUsWgZ3aFC6BLCGbG1sUHzTlOXWUZHI72Y6ntNIlTYxJrutbL6wdnMsZLKGeO5wzgQnJ43VyEEf9wKawhhKxbEpYRwGqgWbW+0QmJRWPe5YUJo5sYFQphlQTiJujmbSCyg/F7bZsqolCsohNMBkpYQwmqE0AtkTDtc8jD/uiWELbaJZYTQfK/X0/kpg2ulZphv1twyGioX7gbCKgmhQrhFQris5QQ4hFDP4HU/FS8jhIC3ivU53bpC6G5YXUI4xU91atpGtcgy6r5Pez39mqT5RDXgZG0L5kpN6eUkv8cB4oiT05qmaa0Id8NTWEPoPGmTCmEYKAOLCuGUOG8ZEkcIQSPVpjJV1xILpxbtlvPdKYSaENqU0ai5xAL8cdtVH8LpNHdHuYdnnkKYDRZsvt3u4mFbE3DnGsJOaxltsUMsqyGEnBB20zliPMpbTgCkSa0JYZVCaOEohOu0sa2CVQjhFD0raXWsKYQwrPsUAq+GcNcpo+N+zn6mJI0mhEWW0bAm9uQEe6+BJvRrJoSVTenn46ApfdI4a2+IKtUiVwjzGkIJW7OMhmmV1i4KEMe17sdahTKF0I7ZlhXCZYSwVZoUqu6101PU5KXf0D3O1DfCHIE3RFlTek8hDJNhk2bWfcJi7efyGsJOaxltsTsUph8GsISQMSDzpvSx6uvWlBpC8DfqWaYnRj07zonWamNbBWWE0A2VsYSw4ZZRKdmrlFFDCOdESsXVFy5E9QFNHVFkGQ37SHa7eC/gOhXCMstoJalIWkJYZRlNEn3/pbnNt8/pRlKYi0JlPIUwG/hkPoobO3bLlab8RbzlfON9CFdWCFvLKFAxbgi7xm8q8bysKb2nEE7aViEuquZIKYtrCG1jnhcvNtamZ1toCeEBochqE+LpU30KJVQTb2u70bNCUxVC23rCsbH1ONsLQugqhBl69m5IqEw1IdyTlNHZjJE+IcxIVJ9IJ/mwacpFkWUU/PHrdFgcv5ub/Kj1DVBmGXWvJ50MWkIYoKoeTUo91zhjdsv5Rk69l9YQZv3A7ttcQliVDLsPCqG950KFsOGW0aU1hGBvQjtPffDBWq9hJYUwTIaN48Y5Xly445Yk/v7EjKVVDqOYadTNxy/LNn7/bRotITwgFJ2shnj6FD76UXXKOidibE7s0hQh4OHD8p89dIQKobuJ6Pf1ROzY2PaREE4MgW8VwkXL6BYUQtO0143pZzCwBysZKZEAdGVuEzeqRZZR8MdP8QqxkdYTK1lGxz1fZUpbQli22THv+Xv38A7Mbri3G0I4CcYujho7dmUKYVH7gn2oIQxDZZpY9wnVhy+WEHaDPIM1E8JVFUL/XmsVQrhD64mwjvDAbaMtITwgmA1YFSEE7RoZDJjQIXZaTpydVauLh47VFMLdEUJTmFxFCMfG4tuwGsKQEM5m7CRldFlT+owOUZRLgk1cPIsso+Dfb/b120Bz+lUso8mo56tMSdLok29YnB8Lk0Y7u1EIQzLvqbsNtowutR4W1aKZhWYDqCKECdM2VEZjaQ0hQEd9w7bl2RAhrFQIRzeBQhg1ep68c+uJmvUibAnhAWEVyyhoQjgcMqbr9CBM8iS5mqKqhrCUEK7JxrYM06maoKNocYL2CWGu6ELTCaFvOZQXm1cIlxPCxO040ciN6iqWUTtHaYKxCYWwMmU0VAiTtJFj5SLc7BS2MdiCZXRpyuj4tjBUpomosozmtWjqSfYAZIMq4TQ/Xwace24yadtOOKgi8mmqH9MK4cwk6m7IMlqlEHbGt61C6CB8X6/SeqJVCFvsBKsohFmm7JEMBmSkeQ/CtNMo+yGUEELHxrZO1WIZXHUwrDnr94HhEAmMAsto3QlhWWP68RjS43wlkwgmr3obL9oONzwA9HqeQiicWbOJp6mrWEbtHLUBhXAly+jwxicVrWV06en3PllGfULYWkaLLKN2PY+3TwhXTRltqmXUHbc0zdf86VTNX6eneAPa53RrCqFnGR3dtjWEDkJC6L5/zR7OK7uqWeuJlhAeCGYztRc20bhlsCFjgwEJmdODMGkMIay0jMJOmtOX1Q+CSoVlOFR2RPRuN004Pq63xRd8Eh/H+eIlJRydRF4D+MG8qxTdDWK5QpgSxa1lFKoVQkvWNlBDuFLKaEgIW4WwkhBagrHFUJnSlNHRzYJC2NRNalUtmn0/O61CgP0ghK1lFFCvlxAldt9OfgP2ONuaQuhZRofXrULoYBWFsM6tJ1pCeCC4U/0gwHBIirNjS9PaW0bDPjKlhHAHzenLCOFspq6N4ZCJq+gmae0JPCyquotJlTkj3kbSaOEi2uvZ2piQEDaRZKxiGbWHVp1AIVzDvbaSZXRwvRAq01RSYbCSQujUEN5wTwU5hVLwG2JpDWFI5qO49k6JMrj3WhgEZN/PuvWEXc822HqiDZVZDcsOX87OsJZR0GP3/vtrvYalCuFsRjoNG9NHLSEkf1+7r0VfBff7ltE2VKbFLrBKwij4CmGEs5CnzSMYR0e5ijAeOzf3HimE/T5qcLMJMXOt6ApImrEJCsnFQlKlw8y2kTR6V4WwiSQjPHgxcF+LTSqEK1lGB9dt24kAodW3sIZQRL7iNJ+v/RBmqWV0dLOQMtrE+wyqx8ySjC0phEUupVYhLMayw5fQMrpJhbA0VCbL6DAJbL6isWMG+bpvxs0VUcxtFYbKXOJ4SFvLaItt4E6BMgCDAZ6ztAEKIfgLqBAlyYed/VEI+32sOT2v90wA0ShCWKoQOq0ntpE0uhIhdJhIu1HNHyvcSIQK4QYto65iKW5v8p6eQNxJFjZGTcMy1cKuD/rNf4N+YM2n3uFaJmVACAdXbR9CjfDAzH0P28+3FCpTqey2jek9VBF5qxCGhPDZs7Wp8fN5blcN94z2XlsIAlJvtqauabCoELrkr5AQdlrLaIsd4M6W0cEAL35Dt52oO6rqCO3EmO6PQtjroT2jIEz9YEN6EEI1IUwSFi2ju1AIvVCZFJG0hLDINrrQRxI2GipTZRmd9obe907O48ra6yZgpbYTYFVdqziteZNT1NzcvF+SBKJhv207oRESC3fM7BzlKIQSNkYIzf1VRggTlxBGzQ6VWaYQqpYhsX1in1M1sa3pXitTB93vMZnQYZKr8XGzVV1YJISuPbRMIWwtoy22jjtbRodDZjhPTpqhEIY39IlfL62ftF2FcDZTi4AQ/sIAeaCMB02CWkLIomV0Swqhd6rqEMJJaxkFliuEOSFcf6LvKpbR6a3fi+3sQc3TmVZAaPUNLaN2fehsTiE0v1uIfPw8q28K9MNm2W3KaJFCCPo+1CmjUxImdHamEHr7jTii2128R5uC8LCjUCEEGyyz7ub0heuY8/vNk4psvk1d0+B1CGGnTRltsX2sYhmVUhNCKWEwYIbz5IbUEIYe8MLkw7CGcMOnOlUtJ1xCKI3Jt1UI8++FltEdKITy8sqefE9JidJ849PUxbMoaXShjyTYF9Ju8NewYC5NGZVyUSF8EETtNRRVDbPz0K3NzY9L6wc7QL/fWkY1VrIeOi9mj7M9IYTNTqsUQq1fUi72kPQSYjvBgdmaCGGZiwJahbAKYViSSwhvbtTr6ltGlUJo3XjPn689hGubaAnhgaDMMiqlCqf6W38LfuiH8pq0iFne0y6OIYoaoRCGN3Rh0MWWawiNAFhkn3EJ4dzcjlohbJLFt4gQRhF7kTI6eXnLVB+uzIiJnGtq6ka1yDJ6dJQfeMznWiUMLaPX1z4DeA0stYxmGdO5s7RFEaf3WoUQygmhpxBqVfeGe2qjs21COBj4CmGDLaNVNl/bKiTJb4RbzrdGCKV07v8s82p2ieLG2kUNyprTe4cv3WAvsiZCWFVi1NYQlkNvlZnP1WuYJPlYzedwe6vWOXsfRjHT5Difr2aztafFbhMtITwQFFlGnz1TJPCHfxh+5mcc8WQw4CN8SM/UgKTNUZyqYoOtOrdly+gqPQjBJYTNaEoPavIVQm0u5vMiQrj7lNH+Ra42SfBIalNPU4sso1GUH3qkqSZuQkCaMuAkP0W9uHij373UMqpbuLgX21RCEcIdt5AQdru+TTsjVRbEbRLCeApZ5tQQCohEY8cvitSHUZpWUgg31HYiHDs3ZTjKxmQ4Fxc3u30B+G6lwhpCsJbRdSuEVYSwXCFsdhCQwSq2Ua/1xNGRbxv9jd/Y6PVtEi0hPBAUWUZ/9mcX98edDnztR17y+/iJPBSgQRbEKkJoI/Idy6gERQilF8GzVqxKCO0Ja4PGC/xN6gKB32LKqDkVDJPZBi9dQhh532zqRnVZc/o4dl6mNGVGvDYL4lLL6GjkqxVJ2uhTbxeugyKsIRSipBfhGglh0Trm1RDOJ0icDXIcAc0lhFBN4pVCmI/XLecbOzQr7UEIpNNh0M+u2ZZRqL7XQsvophTCohrO0hrC1jIKrEYIHzqdJjjq1oYQtj6aA0GRQnh7m3/+mc/AN3wDfOVXQvo3fhnJyzwUoMEKobsRNNaNySQGETGTMKbL0WikmNmGXqAqQtjv50+YmBPWBoXKgHpPZ9kiIQQ8qW7TKaOFCaNA/yrf+cyISJzdbFOJxirN6R88gMtLtCI/4AVPOaf3xgRjqWV0OAwIYdIItX0VVNUQgppzXqX57vWWc55uUyGUY8Z0c7dEHNPpLG+3VGe499pK7Qs+3MyGtJIQZkGD8zhqLaMV99rbb+svtmwZNe0ozIW0hHARVYTQiO9eHeHRkd964oAJYasQHgiKbvDxOP/8274NPvc5/Wa+uNAWLe2RTFOOjhY3unVEVajMYGCsGmKrzelXVQhzQtgcyyj4G56FpErHnrlpy+hqhDD2dqdNXTyXKYTgLKT6XnvBU/X1mghhaR/C0cimwgKQJo0l7iHczU6oNkGJQrjGubHoYNMjhPPRQsuJpo+dqxCGhPD8HO/BW87hww83ch3VCuGgVQgDuHuRcoVwMymjZYTQG7PZCAFtDWGAVXoRtpbRFjtFkdVm5CSre6dxL1/mdlFoTMIoVIfKDIdukl5wMrehpNH5PLdjhSemWabHcDgkYp5PzA2zjLrBMgubiC1aRs2GZ4EQ3uSFcjMS75qaamUrqiEEf/zCvnaWEL4hwVhqGR0O88MVfbFNHacQVZtU0GOWbs4yulQhDAlhHDXmYKwM7oFZYS1amtgC+R5nyjqke9uuE+H86JGLSasQhnD3IqWJvhuyjK7Sq7UzG5KR2MA04pgoaoZwUIU7t544OuISx0PaEsIWm0bRyaqrEHr97S4uGksIq2oIPUK4JYVwNFJKV1FPpn4/v7AzevTNtTQoZRTKCeFsBtLZOfY5VR7EDcU6lymEg1t1882JlJVNX1NRX8mmoMwy6h7AWBKWbkchdGsIPYUwSVtCqFG1SQW9+XEso9fcVyFAtgD7zVDZugBVi+b3IGwVwiqFMHe8BMTi2bO1X8fdLKOtQliWMuqFynQL2k6sIc+gTCH0Dl9CVVffa2FrrKbhdQhhaxltsVXcVSG09YMAaTOa0kN1DWFuGcXO0JY4b4gQLrWL6ied0mtkCBD4hDBN8/d4HOsQF/2EIcdqsdxQrHohIRyN6GfqvZKREDG3aWxNXjxXsYzmtqjNEMKqGkJvo9NaRi2qkg8BHj1CMW394r7ksbox15RcWbSOeZvU2dDvQdjglhMGS2sIYdHxsgHbaCkhlJJ00re9WgGI2pTRsnttMnF6EscJRDEjjpgSq29eXr7x717JMhoevrQ2X2B5DaGUi4RwwTK6wZDCTaIlhAeC8Aafzwv6NxksKIRJY8iFu1GV0t+gjkauarEdQmjGqEhJcglhh0ne2DdN6HSCMa0xynoR2tYFYXPzDdURFhLCy0t7epuREieArs1tMslYxTJqD6nWrBCukjLqK4StZdTA3cybnlugXtPpVBNCsPPjK/QDa7KNLrWMToMehHHU+LFbmjIKiwec2ySEsxmpHOeHMEKAaAnhskTfTfYiLJsjvXst6xcqhE1HkcvMjN9kovaRHiFMEq7St/Kvh8ONlSBtGi0hPBCEltHQLurd+As1hJ3GEELd9gxQr5nbG80jiFq12DQhLNoAGfR6qJl7MibG2Vk3LBWxihDOZljF1NpqNlRHWEgIr64cQtghTvIbrcmL5yopo3bzGtYQbtoyOhz6akXaWkYN3LkxtDxPJi4hVE98yWP19ZoJYWmozHQQ1BC2CuFKCmFIKrZpGQ3bF0RtWiUsT/TNQ7fU2FmVaY2EsMoymmbh4UurEMIiIRRi0TbqEUIEV2cf8/+RA7WNtoTwQBBabUrrBwEuLgLLaNIYyygsLlhVqoV9nXZFCLXvV5i23UkCiMYQeFhdIbQbji0rhGbRzEiIWkIIFNcRgX+v2ddRH75c8ER9/Qb3mpTFhND0kAQQo8AK1SqEFuG4hYrTY83/zJhtQyH0GT7xDwAAIABJREFUgi6mg9YyGqCshnCbCqGUi2Q+JIT2EKZtXwBU1xCC08tOJ43aYJINEkLvXpv02rrPAhSFJlUTQrjqfsR/oCWELTaJ8AYvrR+ERqeMQnUdYb5JDRbQDUn8VYTQ7UFoCWHDWk7Aov3QLEpRpOssdMjOlISMZOMKoTtW8jJUCPOiwSZvVLtd9TpNJnmdLPibQCH0a5nmwQkDjt/oXnPJoFu/6dXGjHsMPULYNqY3COfGkBCGCuG2LaPppL+gWjT5PoNqpWlbNYTuuJn7zroDFhqcq21l01NGq2oIvTo0re7aYJI1EMKyOuvwXmsJ4SKWEcKrq6DtBHCVPMGrGmwJYYtNYTZTE4i7CSpVCKVsCWFF0qg9qdtSDWFRiIKB24PQomGBMrBoPzx2SlHSlK01py9SCMfPr21tpwSiTtuDENTYmBNuN2/EJV6jETx9itdL8oInKjQhlBZXxNIehEA6uvFVplYhtAjDgELlwiqETg2hhLURwqWhMll/oe1E08euTGmyfQjBkopNKYSVym5rGS1EWK9r1hXTisoSQj2oW1cIx7cLrULag7NiQhj2Ijw+9vcJWefEfy1bQthiUyhaRF1C6J3E3d5CluULQxRBFDeKYFQphHaC3HINYTgxgyaEuvfE3NyKafMIYRWBT1O8vn9DjrdaQ9h/3refx8y9m7DpG1VDCN1QvLDNy9OngMhbddg6wtck9av010oGt77KlLaE0MAEiBoLYKgQHh3p+VLPj1MSNUeuaX5cGiozvl1oTN/0sasKJ8mTfNU3BpwwR6y9hnAZIUyYOpZRtZa1hFD93wTcua6ffn/RMrr1GsJxr60hLEA4R8KiZdQ9EAVq03qiJYQHgCJS4VpGPYVQb7RsbZze3TaxhjBUnNzvLdQQvnixtl5bLpbWEGpCaBNGO82zjC4lhI7CNOBkqwrh4CJv8CyQ3kA2/TTVJYQmZfvoKHcxjEaLitObBsuUpefZxF4pSYa3+f0EpMdJ45stu3CtbGFbHtC2UacX4UsebzRUJlQtWoXQh7ueJYmfDCulHkNnvPqc7oVC2HTLqLXMo8bKfR/3eg7J6G4uVKY0eIsihbBNGTVYpRehZxstaj1xgGgJ4QGg6LSnVCG8uAAc5atVnDyCYSfIKIY4zl+n2Wwt/X9ClBFCKX1CmKEvWp/0Nmm8qgh8HLNoGdXv8XXChJK4izhA/yK39CpCmF9L0xfP42N1GJVl9m2MEP58lNuigtYTr6k4lVlG7T+XZZzNnT6VUcTpeYE832C4ipN78GRI9aNH2PECXUe4BkI4n+elD+74hapFmDLa9PssTIYtTBrt5g/2OFOEcI290CoJ4WRSWEPYqk3l95qvEG7PMurda8ObViEsgXvPwWINISz2IrTjBy0hbLE5LGtKX6QQtoSw2DLqNRJPO34a6wZso2WEcDLR19fv+3YbfdLbZIXQJRRxjLcDGnIM77+/9msoTBgF+q/G/gOtQuhhmW3UOhPWpBCWWUatQ2404pyb/BtJ25Q+hLvZCVULMAphfiOsSyEss897ltHRzQIhbNJcWIQwGbYwadRpKnnLudog3NywLtxJIYxbhdDAPewMD1/yw7LtWUbtmElJZ3zjK4RRe/hiEO5J7t3L9469nhpPjxB2OlwnT/Kvr65yKfGA0BLCA0DRQloaKqPVkyZbRqsUQg+dlFvO83SoDRDCslAZa3Hr9TijR9+EYHRaAu++nxUhzB8YcALvvbf2ayglhFe5x2ZO5NlX28WzmBC6r4vdBIUK4Zoto5YQDoecktd9krQ9CEMsUwgfP8azIK5LISybC8MaQk+1iNqgi7AWrThpNJcON9GL8G6W0VYhNCg7fOn3HUKRquhWSwh7PWeDcHcYJV6Iisb0WtW195pOLGzHTCHck0SRv4e+uSnoRfj4K/1/5ABVwpYQHgCKTntK2068fElGwhi9aqQpUdSsyTmU+90NxXjsNqfvIBH5ifQGWk+UnYrb+b7f54xeruh2mqcQxrFavExNjPt+jiLgKN8B9TlVhHCNdijIxykkhIOrfLcqoVUIAxhCeH2dq3fuXGM3Qel6CGGRZVRK5yxnOOSEvO6zTRhdhLvZCW1sUGEZfcN7biWFcHjtJ8S2bScWatFKk0Y32IuwkhCORn4fQv2kJu05ylB1+JKHkgjodP0atDdQCctcFOY6ABgO6TDJSbwuhWjHTOG1ehE++Ar/gZYQttgElqWMhgqh13IiUS0nPKtkzVHVmH44dBbQDbeecNuFhCd1ZvO1QAjT5tUQuhueLPPfz1EExIld3YYcqzf/mpNGSxXC25n9fErSEsIAnY4ifbNZ7lBz7zf7+QYto71eHojSnfXznp4AaWsZDVFlYwOtEEa5BfEVj9QN8oYWxKJ1bDbzSX58c9n2ISxAVdJorsJvrhdh0YGZSwiTAkLYWkb9ey20Z7s2RDodbjlnasKw3oAQltlFwTl8GQx8VTdtCaGLVXoRLhDCs4/7D7SEsMUmsCxlNFQIm9yDEKprCAcDR/rvBEmjayaESxNGoUAhbJ5lFHxV130/5wumYol2s/juu2v9/eWEMCcXM+KWEBYgtI2GKbFRhHevjem89r1WZBl1nXFvJZdBU/qkUWr7KihTCD1CCJbEv0Q/8IYOiqJ1zFMHOzB/8dKraxKdtL3PqG5yXqoQbssyOhrlbZP0k7rdxUPQJsIdt1CN92yIa2xOX2arB2fMBgNfIdQX2t5rCqGoAIu9CBcI4dFH/AdaQthiE7irQugFpaRpo+oHobqGcJsK4VJCOJ/DcMgZvfxUN21e2wnwJ+BCQqjf5Nbeu+Y6wlJC6JSjKYWwTRkNUUUIbbNzpybtgidrtYy6+9634xd+KEnSEooQ7ib1+Dh/LYdD9dijR/qJmsS/Qj/whoRwaVP6jp/qC3B8njbK3VKGMoUwryFkKwphGSF027yQJK3SpOGOW1GAU2mwzBsEp62kEGrLqD1gTVubr4tVLKNe2wngKn3iP9ASwmIIIT4hhPhpIcQ/FUL8ohDiP9CP/5gQ4hf0x68LIX5BP/4VQoih870/7/xb3yiE+MdCiHeEEH9GiPovF3etIWy6Qhg2Fu108tfOm5g3rBCWhSiAXhAGA0DmhDBJVRNvmqsQZpmjKqH+LyWWENoTzW0QwvmcwTCfXiZ0IMlvwtbKpvDggSLut7eLve1sc3rnhV0HIXTnQo8Qymc+IWyb0i/A3aRGkT9e/b5DCPWYbU0hjGcMbjLvZ04fFEyeDYRrPdylZdRdy8xjDIcBIUxbu6hGmT2731fr2iZaT1QRQlch9C2j6ve3h2cKr1VDKB76D7SEsBRT4PullF8DfAvwx4QQXyOl/FellJ+XUn4e+F+An3B+5lfM96SUf9R5/L8F/l3gq/THF7f0N+wMd0oZbQkhsNi7yT35sovVrhVCLT91GKsQIE1Qo6h59RfuwilE/p5OU00CuoFldAuEUF7f0Hfsh5P4xBJ2aE9TDeJYWZ+kVLUVoSL/9CleSMkLnq41ZdS9bd/K3gsUwpYQhghDt0Lb6P37pv+nmh/7nL6Rzdeg6IDM3XAt9CBMU07PWhMTVFtG90EhnOJ8o1UILUIi7xKNycQhFWu0jFaFyrg1hK1CWI5VaggfBvzvehZstFtCWAwp5QdSyr+vP78Ffgn4mPm+Vvn+APA/Vf07QoiPAveklD8rpZTAfw/83o1d+J5gWR/CsDG9bxlNGmcZhUUPuHvyZRfUUCFcc8poWaoe+ITQalBOoEz9dW8fZa0nckKYb06BrRDC0YdXtjamy5hR6k/4LdHIYRbHUkKYBoTw1StHYlgdoWV0NvNv27cnv9VaRpcgnBtD5UIIPZ5rbk6/VCEc3y4SwvYeA6oto2ENoSUVa6whNO+Vcsuo8420JYQG7romREXrCW0Z3bRCuCxUpp0rFVZRCI+OgjIu0WUknDf+s2dqATwgbP34TQjxFcDXAz/nPPw7gGdSyl92HvuUEOIfCCH+jhDid+jHPga4aRLv4hDL4Pd8nxDiS0KIL73YQDuBbSK8waX0F1JLcKRsFUKNqjpCu6DuWiEMew01NFAGypvThwqhXcA2FCrjjlX/gzxVsctIKYQarorZwq8jdO+1wUATQhHZF/cFT+1cdVeEp98vX+bz4/37cHTz3E+pbC2jC1imEIJpTr/eXoRLawjDpvQtIbRwlSZ33hmP3ZpPNV625nOLCuEE5yQtSRrncCmDO25Q0XpCj906mtOvZBkdDkmZ5MmwelJox02hiBB2u/naNpuptc2zjYqIq49+tf8P/eZvbvQ6142tEkIhxBnKGvrHpZRuhvUfxFcHPwC+XEr59cCfAP5HIcSddC4p5Q9LKb9JSvlNT58+fdNL3ynCk9XxOG8J1ek49qnBAEajnOAIAXHcSIVwNUKoZmurEL565c8Ab4gyQiilFge1Qjgzt2EDexAahAvnPlhGB89u7ecdsoWE0aapuFU4P1fzk0nPM7AKIayl9URoGfXqB98GLi8XFMKWVPioUgi9pNENKYTlhPB6gRC2ioWCS+JDhfCJybLQ37hAP/DsWX6C8gaYz9WH3k4Aag1zyUUWEMJWIVQI17UwWMaqTt31E8IwZdQbs8HAt/mmaZsM6yDMoTBY2nriI5/zHzgw2+jWhl8IkaLI4P8gpfwJ5/EE+H3Aj5nHpJRjKeVL/fnfA34F+AzwHuA2+/i4fqzWCE9Wq+oHgaCnnWgkwahqPWFPzkQEaeorqhcXa7uGMkI4Gukx7ffpMmZsT+maSwhXVQi3aRntv8gbnCdM7QECtNaaEFGUL47u/OQTwjdvTh9aRl1Rv5gQtgphiHCzU64Q5jfDSx5vxDLq1RD2FwlhE+fCIpT1aR2N8lAnohjimBvukZGoF3wN/VqL1jFDEplOiWYTJuiL0qyxVZoUqtT4ft9RCNMgVOby0q8LugPKFELvXhve8P+z9+Zhll1lvfBvnXmqoau6eqhO0lPSmUggZmJImBQVkIuoELgkAnpBRAMo8PHxPVe/T5+Hq58DCF7QoOAjw6cicAUVohdEhqgYhpAE0unudHqoHmoeTp1z6oz7++Pda69h7332mWro1Pt7nnr61K5Tp07vddZa72/93vf3rmntXZBMMom30JOxzMSV5gUmhH64NYIfBfCY4zjvs378YwCOOo4zpT1/QggRdx8fApnHnHQc5wKAFSHEM93X/HkAn9+I/8Nmwt5I29UPApri5Qaw21khlPdOX+wMgpZMmTWXA0wbDXMZ5R6EfkTWELqHG17K6MLCwPLzWy0aKyGslFGNEAq0uAdhBOTnVt5LwE5pGxwhlGuh0YNwF4DFRU4Z7QAdNaff6JTRsj12rBBKhDU4L5VoLthpo54z7ADqCKPSRY1atEQCgGBy4UKuU40GHcDYCqFHKIQAUimlEAI9p/yGEUJ9riUrK0a/T6619iOIENq9CH2tJ0YPmheYEAbiOQDuAfBCrZXES9yfvRp+M5nnAnjYbUPxGQBvdhxHHnW9BcCfAzgBUg6/tO7vfpNhT/DOFcLtSzDamcoYqX4pSyEcICEMM5UJJ4TKVGa7wU6t8SmEQgDplBkw9tGrSUdYD8LyvCKccTSZEEZABoHVqnkAk8+7c047BW8gPviU0V0OsLioXBYBIJHgsQqAHuwE9UfbKIXQIISr1tglU0zmXegHnDYhBFwCD/gJ4QDqCNsSwkoFSdRVLVqC3Sp16IeMQa0nDIUplTYJYY9po2Euozqx8R2+pFghtNGJQmg7jS4NXW5euMQI4YY0+XEc55vQzBStn70+4NpnQemlQc//NoCnDfL9bXXYJ6tRCiETwvY1hEaefDJpKoQDNCAKSxm1CaFqSr99CWFYyqhMbQMApNKoVatoII4EmmQsc/hw3387tCn9gjp5EQATwgjIOVap0OOyK7DW6xT4LLo32IHAPMaxu4fDFz1ldG2NNmWAPifj2TJQr6tAJxYDYnEmFQHQCYYdpAIuIQyqIXScnotnIxXC4rxPIeSxI4Q1OJfjtXMncOwY/HWE600IAxVCJoQ6kkm6h0FqvEEobIWwR0IYqRDW60jWy2rM3DRf3tNMdNJ6wk4ZXc7uMS9cYoSQS0i3OJpN2oNjMTMNSyJIIVQpo/SJ3s4po0EKoUcwACCVQhFD8C6tg0LYjhDmUfKljG7HupmwlFE3k8a4OOjm9HKcfIRwSTcYcoCEegJvnn5IEr+2FuI0qrlhzGKip/HTT7/1qToxAcRXFuFAqzN1Jx6PlR86wQhNGY3Hvb6b8xingZUMpAdE1hAWF9hlNAS6ypTNqkPNtTW6phRCWiM3L2WU1kgmhArtyHyhoM0HNwPGc2wdMCHUDWXMHoRJcJqvH50ohHbK6GJyl3mBCSFjkAia3DohtBVCB1pj2iQTjKAaQsOpLZlCAwmV8rJRhNC1GjUUwm3sMhqPE/mTZgX659ojauvkNBqqEC4rezEHghXCCKRSNI52rzTPWCarBnUWE8ATT3T9N/SU0aD6wTVkaKwAIEHOeUFtX7Y79PUxkzHLEep1WZMmvEMqr5VBH+tjpEK4PGOljDIhlEgkaH2U99AmFspplMZrPVJG9fXRJoScMhqOdvW6QmikwiXz/TqNhrmMenPNTfO1STzvaSY6qSH0mcoI68LU1ECd69cbTAi3OKKa0tsK4SoKKiByi/K3Y0DUroawXNab+dKsX49ehG1NZWo1oFFXNYQi5kVl2zFlFDAXYP1z7d2/DSaE5WLTe+xAGC6jHKgGI2PGhQB0QqiixFlMACdOdP36espoUMsJI+WQ6wdDoa+PQvgDVc+kRKv7bEH0nFKvty7QA1WDEC7NcspoG7RTmuwawg1LGbXJhbtGssuoQlj9p1TjVXN6q/VEjzXyvSmETOJtBBHCfF6tX5WKPw5YLiXd00kXrdbAHdHXE0wItziC0mxCFcKLF30nrNtRbQLa1xBWKhrpWsfm9GEKod6D0FMIU+SiCTAhrNfNz7WeUgNsoEK4qnp4tRAzBpI3z2DI+xLYizBjEcKpqa6dYvWU0SBCaK5/7DAaBnt9tAlhKuVec5/YQoxKEXokhGGHYyYhtBTC1Pbdv4LQzmlUKYRWyugACaEeg3i92biGMBJ2yxD9+1pNI4Tupue1njh1qqe/F1lDWC5bJJ4JYRDsLDOADrT0+Mxez5aWAOzfb168hNJGmRBucQRN7lCF8Px50yAlnd6W9YOAmWLTarUhhK5C6N23ARFCvaFpoMuoRgiLGDIMHLYrIdQDHv1zrQihpRBOTWEQkEGxvrg7DlDWyqUaSBhPYKIRDN0MSMIjhLEYkKYneArGk0929fpyPRQiuAehrRDyOAUjqmE24G9O34/TaNha6J2+1+tIrq0oQugaXTAhVGjnNKoUQnqS0Zy+T3RSQ8gpo+HQ51qQGu8Zy7iL5zLcHNKTJ3v6e5Euo65CyISwPYIUQsCMz+x7vLgIOFcwIWSsE4JOVkMVwgsXzBYKqfS2JReAOaHjcUUyHEdb/GyFcECtDHQib5vyBRJCrefXdg2CwhRCT21ap5TRoICnUgFaFZpoaVSpiS/XEEYiqxnXSXimMtoTvF6EXaaNymCnWFTrYDbrzpkFy5SEe2uFop1CaDiNautSP4QwUiF0g1Sz/l1s27UwCO2MgJRCuMFtJ9bWkEDDV4/GKaMKttJkk3lPIcxaCuHZsz3Vn0UqhJVKICHktdJEGCHURZZGw8wsqlaByr5Ltzk9E8ItjqCT1UCF0HGA8+dNQphObVuFEGhvLOM9ztAN9BTCqSnzBveIsHTRVstMGc2j5KaMqlVluwZBYTWEoYTwwgXFEPpAULBaLsP7HORRos2TXUYjIQNBnRB6CiEwMELodtgBQOqgEAAWFy2XysS2nUtRsNfGIKdRuxdhP83pwxRC2ZoE5TIyqKjxc/8uK7wK7RTCkRH33rqEcBUFVJGi8dJz3npAVzWErBD6YPsZ2HPNI4SxOJDUWk+0WkQKu0QnNYRJaO15kjxmQehEITQOY1zMjR0xLzAhZAwKHSuEi4tAtaqITSwOxBPbWiFsZyzjuSDG4kA6o4i04/ScqqEj7ES8XHbbXpRKyIGioTJy3huyU0q2E/TUGv2E2SMXsTiQSKiNrNEYSIpv0AZaml8DmhQJ5VFCWRSAuFoumRAGIyioqFS0TdN9whx2kklJF4Sw1VJt8HzpogCbynSBqCAVGGzKaNB66DhaC55yGQKOzxDNDmq3M9rVEArhjpdQtc7zGKeb3Gdv3a7aTrCpjA9R6dmGU2U2Y/Yi7DKlHujAZTQkZZTXShP2GimhiywrKwGEcPgQPUgmgUOH/N3rtzCYEG5xdFxD6FoUe8QmvX2bnEu0M5Yxai+zWbP2sgf3Qxud9iBUJ+I0Xvl8z32fL3no45VMqg3NaE6fTptB/wDSRoPUi9LFovc4hzJKmTFI0x+AN88w6IGgHLNKhebb8DCAHE3CFmKUGtUlIQTocxFGCO2UUVaYgrFZCqHdcsIzdKqtogatV8k2NkQLQ1RzersX4aDqCIPIPDem7xw2IbTTsw2+kMmqlFGgL0IYqhC6qi67jLZHLKZiD11k12PqlRVt3rmYO3w7KbuVCrVW+v3f35g3PAAwIdzi6Nhl1K1984iNuylwymiwQmgQtVzWTLUdICEMNJQBvB6EdlN6JvDKCl+S9mRSywxNWYRwAMYyQQFPaXrVe5xHCZWk2YGWCWEwYjEat1RK3VdpJBrYeqKLXoS6WcKqGh51wh6QMsqEMBgdK4RaDeGgU0b1MSw0llEyHGJTTAgttEsZBdavjpBTRvtDuxrCSIWwy2wlXR20D5ZZIeweQWmjekxdLPoVwvlSBrjssksyvYEJ4RZHx30IXULoFeWzQthWITRaDNgK4fHjff/tMIVwcdF9sLqKESwrQpjcvk3pJeyTVHnYYRDCdVAIg05Uy3Nl73EeJZSYEHaMTIbWJTmOsk5s5074W0+cOmX2HmgDPdjR10A9bd5UCDllNAz66XezGewyOjYGI2V0AWM9p2gH7WMGIazbLUNYIbTRzlQGWL9ehEFk3jSVqaOKtPEmOWVUod3hi2EqAwCZ/lJGwxxGAX/bCVYIoxFFCANTRudwyYIJ4RaHvRg7jqkQ2imjtkLIhFDdQz04NPLrs+unENqEcGHBfVAqYQwLymLa3cRZ0VWLb6BCmE6bQf8AU0YNhXDWJITluDmRmGiEI5ulMZMkoFajxxMToIXM/azPYoIGtsOiez1lNKyO2qwhZFLRDmEEo1SifYZqCC2X0fPn1cB2gUiFcG3OXIOZEPqgH5jp64+sSw9VCPtIGZUHBm4XEA/1OrxgpKWHkYkE0ml//dp2RruUUaPtBABksljCKGSFRK9teYIIIbed6B5BhFCPqYvFgJRRJoSM9YI9wWs1VZuTTGoT31UIuYZQwT6Z0xc8kxDmNkwh1AnhOObVpu2exNuLy3aCTeBloJ/JmITQu2fAuimEpQUlQeVQRjlmRqdMCMORyfgDyEE4jeqn3x0phElWCNtBD3ZSKfP7Ws1VCOMJ7wcLGKMf9uF8GKoQVma5KX0E9PUxkVCf+1aL5td6KIQ6kddTEOt1uKcyDhxoxd4ixsTCgt4T2XH86b5ZvaNRNoMq0qqv4wAJYa0GGtB6jdN8O0QQIUwm1b1qtfyxwPz8xry39QATwi0OeyMNVAcBPyHkGsK2NYRGfn02Y55Onzlj3ugeEOYyOj8Pbwcfw4LatN03a6cfbCfYBF5+vvN5kxB6RALomxDKTdquuSgtqDTGNNZQi6kcKCE4Jaod5Ga5XoRQiIB10HECU0a5hjAcUQ2zCwV3WXLHq4IsKsj0lEERqRCWpjllNAL2+mgTi/WoIQw72KzX4RUHN2UYyemioWiX7iuEphKm0wCEShudmVFFoh0gzGFU/m05Zp5C6Pb7BPiQMwidtJ4w4nD0beq7qWBCuMVhb6SBJ+OA31SGFcK2NYQGYnGspHep7x2nJ3cvHUEbaavl1hCWywAcjGGBNu1EguzCwQohoMZLfr5zOZ0QpjCLCZVS06epTGh7kGW1A8TRNJ6QzW5fJ9hOIMctnBBS5OERwg6NZeRYtVrq8+BlSZTLQL2uCGEsBsTiTAjboF1zehmojo3BWDgXMNaVEZBEpEJYPM+EMAJR7QvWQyEMWx8bDXjBSBPuDxOcehgGfezSaTPjq1bT6ghFDMike64jjFQI3YLuhKwh1MwUeNz86KQ5fVAWmOeKfomBCeEWhz3BQxVCu+0E1xC2VQibTfMUrZodQQ2a00yfaaNBJ+IrK+54ug6jadRo09bqdLYzIbSNLvQaQm/fSqdRQ0p9zgegEAL+DbS0rHymBRxj1WeS0R4ysBh0c3pJAnULcG8NdN2aPELojhefeoej49YTWqQ4j/GeCGGkQrjMhDAK7carrULYRw1hW4XQI4SmQsjEwg997Gw13m8s03vricgaQlchjKGFJuLeG4vHDW7IcNGJQlivm/tMowEsL6//e1sPMCHc4rBP6AIVQscBzp9HFSnVyymdMuoMtiPsDVTfqCoVK502mxuosUzQRurllruGMoC7aWvW7ts5ZRQwU2vkZ1cIbaySSUDEFJkoFumrRwQFqq0WUC4q44wWYt7pN8AkIwqpFJH7REKRuHI5mBA6QNeEUN+c9fpBAMpUxh0vJu/haJeCaDhX2gphD2tjW4XQcZBfnGJCGIFYjL5aLb8zbKmkpfi6hLCCLMrIrl/KqBuMNKRCyE3pQxE11wxjmWxGmc0BXRHCSJdRVyH0XGG1lhOc9eJHJwrhyoq2t7m4VI1lmBBucdgBa6BCuLAA1GpqQ43HgXgCQ0Pbe5Lri7Dj+AmhoZ4O2Gk0aCO1HUYBN61Hs3bfzgohYC7AemChxkp4aaMe+lAJgwLVSgVwKhTsZLCGGtLGE5gQRiObNVtPVCp033I5eBOxgQQFPidPduRcKZ+iK4Q2IbQVQibF1AjjAAAgAElEQVSE4YhSnIDBpYwGHbx4pVG1GgoNre2Em+67nbNbwqCnHtqEUAj3QFGTeuYxTnOjx5r4sH66utpUl5k1rBCGIqo5/YYphDYhTHD/43boRCEMchq9VI1lmBBucbRLGfWCIW45EQgh/Ln7Mk20VrN6/uUG24swiGh4hHB1FWNYQAuCAixOGfVgj5eEQcIGaCwTFPCUy/BOv/MoEclIMiHsBpkMfcn7K1NoJiZAg+zutLOY6Ni5Mkgh1FNGHWgKYZJTRqPQcXP6TEDKaJdFMvZ66DiaQlguo4BVRQi5J2sooprTj4+DNr6kVUfYY//IThRCmxCyQuhHVHp22+b0AyCEjmMSQs/FNMmEsB3kuK2tmUveU7UXIRPCLYxWSzkgSiLTrim9qh9kQxkJ/YRHCPP00qjB3ACFUE8ZHcc8FrEDDtTmPTzMefxhCqGh9NiEsA9jmSDiXirBIIRl5Fgh7BLZLKVBSbIhs9b6qSOUhFAXE3WF0LNRB4BEApkM90NrhyhTGUBrTu9GmAsYownSZV2affBSqajxzDRWkUBTI4RJ3/thEKKa0w/aabQXQsgKoR9RDrFtm9OfPNnx3wlzGW00XEJTqSCBBsoyk4IJYVvkcrTHVKtmmGErhEwIGeuOIPUiUCEMcRjdzi0nJNo1pzc2rqylEJ4+7Sbd94agsbNTRu0ehNu9fhAwg1SdsBunzgNUCAN7EGqEMIcyE8IekMkQmZDz4OJFCkhCCWEHaYh6o3v97wAAFhfNGrREktNFIxDVMBtwCSHUSZq3ZnWRNipdYfXelIahTGuFrjEhjES7lFFAyzBJWwrhmTM9/b1O2k7YdbtMLvzotjm9L2W0Q0U+TCH01sxyGUnU1eE3z7W2EAK46ip6fOqUir9thfCp0pyeCeEWRpB6EagQ2g6jaU4ZlWjXekLL1ARicRR37Ffft1o9t56QLpmAWpi9lhOARwi9zdp9I9s9XRQwN06dBBqEMLUBKaNusOOljDIh7ArZLN0nvX/q0tJgFMKwlFEvMAWAJPcgjEInCqG3JrnjtYAx+r4LQhjZcqKxBAAcpHaAqJRR71Ax0zuB1xHWdkJXCO0G5xx3+GEfTNumMoZCmEpiKb7TfEKHRWlhhNDrjVcuYxgr2lzjMYvC+DjtW82mqiTK5cxWcMb4gWsIGeuAbhVCThn1w07V0IN5gxACWJk4bF7oMW1UP1WVpj5eywkAhcos0qipzdpNGWWFMFwhNMZqHRRCI1hdaXkTLYcype9oT2DlPRqZjOUOC1IJB0EIw0xluCl9d2gXpJZKdLA15vI/STC8Q6wu1sbIlhO1BTQRU8QimYQQbAgUBH0/a6sQZnsfLx1BCmGz6c7FtTXE0NLSD5lchKFdva4vZRQCS/lJ8wU6PJwOcxl1Q0SgXMY+nNMIIceKneDKK+mezs0R2RPCvGd23SwrhIyBI+i0p10NoUoZpR9w4NpeIbTr9VbHLjcv9GgsE0Qy9BOjsQoloyuFkN4IK4ThNYTGCXU6jRnsUs3pB5wyujJdBtxXH8YK5lOT1DDYBY9TNOTYDQ+rbCeTEFrN6TsIWNumjM7NWYSQU0ajYAepyaTaU5pN2mvGx9254S6cyxhBBZnBKoRr1tilaOy2s0N2GHQSH9Tg3DtUzA1GIQwi855Cv7aGJOoq1dcdYFZ2/YhKGbUVpqWUtr8BHRPCMIXQI4SVCiah9fxkEt8R0mng4EF6fPw43Wf9ntniAhNCxsARtJG2cxm1m9Lzwty+Ob2dBrNS2GdeGOCpqlc/6DgYK1I9h6ohZIVQIsxlFNAW3XQKa8ioILIPU5mggGfpQsV7PIolzKf3Gr/DhDAa8TiN1+ioWsdMQkiLl1FDGFEnI0+/dULofUaOHjVTRjNpTu2NQCxG46SnuNuBaiwG7N4N4yTtAvZ2RTAiFcLKLPcg7BD6+hjU4Nxbm/pQdHUE7WUeIaxUzHo0ThkNhU0IbXU3kzH3u0a6YB6SdGgsE0YIvTPTchmTOM/p2T1g3z66T2trZDGhCy62ic/SkpnJcqmACeEWRtBG2k4h5Jo0P9o1p7cXzWJut3mhz0000FCmXsd4TRsvEfOeyONlEvhkUi20zaZumEAffI9MTE/7GwV1iKBDl+VpdeoygmUsJHYZv+Ol0THaIpv1G8t4hDCZBOIJ1Zy+UvEOtsIQmjLaaACPPWaSinyeFcIO0EnriclJ+AlhF2tjpEK4epEJYYeIcqv0DhXdA5d5jNP8Onu2p16EbQmhqxAqQsjkIgx2HJLJmK3E6nXbWKa31hNBLqOViutf0GohXi1hN6Z9Y8YkPhpCAEeO0OOzZ80Ms0rFVHkdR/OMuITAhHALI+i0x6cQOo4XSM3ADVxdh7EJrcxqu6JdDaF9qrOStBhZjymjUS0nZFP6eYy76aKUG8UKob9NiH5q6hExEQOSWnN6x+nbVl3OMccBlueUBDWCZczHzInExL0zZDLAyIgS/paXac7RmJJzZRVpRQYiSEZQymg67f5etaoUwlQKSKaYEHaAqP5oALB3L+hGuzmc5zFJC5psLhmBSIWweMFs+cOEMBRRdZ/ZrBsXxOJAOo0aUjS/HKcnk7Sgvcw7kPERQlYIw2DHIXaNrK+OsMdehEExo54uugcXEUeLxkwzOeAx6wzDw5Qx4TjqgBJ46vQiZEK4hdGRy+jCghchzWKCVoI4/QITQpVmKIPIdj2SirER88KpUz21nmibMuo2pQdchZCb0huwU2v0OkJjw7SNZXp0hLXn2Ooq0CzRqUsWFcTRxLK2MQthneQyQpHNEgHUx216WluXct0ZywSljGYyAB59FABUipUbaXHKaDQ6VgiF8NIQL2IP/aDDtNFIhXBpihXCDhGVeiiErhJadYQ9ZLwEjV297r6BZkPVEAoBxCmcZHLhRzxOt0i2YAEi6giDWk90gLaE0E0XBdwWL5rExWPWOWSqqK4QPlV6ETIh3MIIUi98CqE726tIYRkjXv1gLMYEA1AKkyTSOiG0TQtWygk3+nHRalGyeJewCWFQy4kG4nQC6Lp8MdEg6Aqh45iEcETn67mcSQgfeaSnv2dvoMvL8D4sI1gmm/2kioZGRvy1p4xgyLHTxy3IabTTOqegthPtCCGTimh00npiryyhdcfrPNw1skOCEakQLp71EUIOUIPRCYEPdRrt0lhGkpdYzMym0XsQksuo7C9DGyor88GIaj1h7P/ZDMVzEqdPq82qDYJcRu36QcD1m9AYDa+VnUPua3ocEKQQXoqtJ5gQbmHYwWq9riZ8IuFet+sH06p+0E6J3I6Q9vfVKt07fRG2i35LJaB5+Ih5sYe0UftUdXlZM21oLiGNmjr9cx1GR0eZaAA0VmHGMkaQWMibhPDhh3v6e3awurwML9jxCKE2MFw/2Dnk4Ys+bv20npBzSD8US6fhEUIvZZQVwo4RpTgBfkJ4Ae6FHhXCZtPt9QlAOC3kFlgh7BRRKaPA4BTCICIPmD0IWzKEdGvR8nmOO8IQ1XrCOPCMJ7BY0FzP63VN6gtHpwqhTQj5AKZzyH3NcCZ/ijSn56m7hWFvpO0cRr36QVch3GX6YGxbCKHu1dqaufCVSv7TzNUDTzMv9JBmYyuEXroogLEmrRJ2D0JWcxXCmtMbbVQKhYEQQnuOLS3BC3ZGseQjhDxOnUOOnR749EMIWy1SjX2N6X0KIf1BViqi0YtCeAF7yaikQ0JoEwtJBgEgJyqIOU1FCOMJQMSYEIZAXxsdp7NehL22nggqfQBMQthAwngSE4twRLWesDOElnYcMi904DRqE8JikcgKACRrJUxgFg3EsYaMN/m552d3kPuadGgGaPzsw2ImhIyBwt5I2zmMekEVG8r4IE90KhW6bzIIqtX8C2Fx3zXmhR4UQnvcDEJYJQLPjrDhCGtOb4xVvuBPGdWrvDtEVMroPMaZEPaIVIrUgkJBbZxzc1rgYxPCiNYTrZYyGwLosxGrrXlzVBFCkgY5yIlGO1OZYpH+HRpyr7vjVUOKDko6PCwLqtOVKIC+UX3R2KmyHYQwW4VshEIYSAjdLIq6RQh53MIRNddsQjFdOGxeiKgj1I1OpEqri4p74zOIwQmca9zzs3NIY7R4XI2pnX0GcMooY8Cwg9VAhdAmhK5CyIRQQd6rSoUWPv0UU1egAGBl15XmhQEohPrCML5GCf2qByGtKOwwqhDWnN5IAUynMJvU6j1LpY57NUk0m7SJxmJqQwwmhCq1hglh55DqfDKp5lyrpQWYLmP01q6VlbZpUc0mHeLohBBHj3pRUAl518+d/gCnjEbDTmPTjS3m59VatncvvFYGgFtH2KNCaBDCJjmVcl+0zqETi04UQu/w8dSprpqjdaUQcoPzSNhzTU8RXVoCLrvMfP5UfL95IYIQtk0XBcx0UYDbhPQBKTLonhR23+TZ2Y17P4MCE8ItDPtklRXC3qArhIC5aWkmnwCA4oSVpjHolNFVakrvbdKcMupDWA1hIqEHJwKl/C6Uoa3IXaaNBvYg1GoIR7HkUwi5hrA7yLmnp0MpIVf4ld4vfzn0tVotkxDqhjKAW0OoRcgc6ETDVi1yOfUZbzRUN5fJSXguo4BbRzg1pRbVNrDXQ4MQNpboGiuEHUMnFm1NZeIJIJlUh4/1OjVQ6xCdEMI6ksaTeNzCYaeM6mtiECE819iNph6i90sIGxR72IcvTOK7hzyo1uMTO5ZkhZAxUNgnqz4zBYBrCDuADEolodY3LXsSrwxbq/KTT3bd9NwmGgYhvPhDALpCSG+AFUKFMIWwWrUOOgoFRayBrglhkGmCXkMY5DLKxL07yPHTlSejk8vYmEkIv/jFwNeR6VDtCGEJea9+EGCFsBPYqgUAXK55WUj+sHcv3FwpGlDPWKYDO/y2KaNVipo8QphiQhgFnVjkcmo+VCo0R4w1Kps118guDjiDDswAN5XYXSMd11mUG5xHQy9VAcw1cWmJzrL0a41UDtPYrS5EzDXbYdRxTEK4b40UfSaE/UPGlHb8aDsp6zH7pQAmhFsY7RTC0JRRVgh90FNGAXMBtB3Uio0ssGePutBsdt16Qj9ZNVpO1OsYO/MQAE0hdHuxMdFQ0FUL/QTORwhtdalPhbBadefY2hoSaCCPEtcQ9gm5ceqGQMWidkvHx1BCXim9//zPgWltMthpNKyUUR8hVAoh1xBGw27LA7QhhICXNuq1nuggbbRtyugaOS+wQtg59PUxFlNzzHHIsMcmhPMYh1eZ24WxTJhCqGdReGBTmUjIe7NEojjSaTV2jQbNC33uIZvBWWgXIkoigurhZRpxOg2MrZwC4J9rPGbdQ8aUdi/CS91plAnhFkakQug4AQohEUJWCBV0hdBx2hPClRUAV11lXuzDrttoOVGZQRo0iPMYp1UlRm+AFUIFXbXQFcK1NVshzKvPPdAzITQMZdw/NIJlCIAJYZ+Q46cTwpkZ7T4ODQOJpDogWVoCvvUt3+vohNB4bbf/ZAuCnPNcFiiEWd/BCEYmQ5//alWphHpQeuYMrZlee9Ysya7dtJ5oqxCWZ+gaE8KOYau6dh1hNqup49ksGkhgBe4E7GIvC2s7oddZq7YTnDIahdFRWpeKxeC00cVFixBmMjiLK9T358+bJzcW7P3MSBedBMQ8sROu1+0fcm/RD0uCmtNfammjTAi3MCJNZebngXodVaRowY/HgXgCsRjXOumIx4kny5QznRDaPZOKRQBXWsYyjz/e1d/TT1bN+kFVvzGHnUBO7eRMNBQ6ThnN5zErtJSaJ54wo80I2AGPPLlFpYIRLMMBqF8k1xD2DLlxCqECD2MOCuFPG/3Sl3yvIwmh3ps53SwTY4F0GBVeJJzNsnNeJ9At56WaMDGhlMPVVSIAoQphBwSjrUK4SkWKTAg7R1T7AqCN0+igFEKbELKpTCTicToYcxy119hpowYhFDFMjVptsNqMn1wbgxxGJyfhsRNOGe0fQYTwqdCLkAnhFoXsuRWLqQnuM5UJcRjduZObw9rQjWX0BdB2uV9ZAXCN1Xrihz/s+O+0WvQl7cF1Qji+RIt5XZ7Yuvb4Qvh7EG1nhKWM+hTCWByzO681f9lVjDqBrVx4qVDNBkawjGWMoCkSXiSbz/udxBjtofcA3b3bfx0AMB5NCOVY6bVumYunvMdl5Cj92l34OF20c8h7JclELGYaXJw9S2MnBAyCUUUqkmDI2k+5Hup/BwAKK+dRRwJVuBMrmTTSIBl+9NKc3qtF60Eh1IPeRkPVEAo4qCFlPImJfHvIfV6WkdjGMgYhBHBm5AbzQkD2hEQ7hXDfnqYXjChCyCS+VySTdJ/TaXXfgxRCJoSMgSAoXcOnEHL9YMfQ6wjbEcJiEcD115sXuyCENsnQUwbGph+ja/K01t3Jx8aYwOsISxn1KYQAZseuNi90kTZqz7HlZXhe0Z7DaC4HuMYJrOJ2D12d1++fkYY2NoZZPfX3u99V9pYuAlNGp1Rwy/WDvUMG8ZJMAP46wkTCnXsaU5vG7khCGLSPGYRwaUr1jwSAZIr7okUgKmUU0FN8abym4DL8iF6fOoIIoexNiUoFQyiiLMeOawg7gk0IdYVwcTGg9UTB2t8eeCD0tXVCaBvKTOaXvXFfRYGeJCjo4DHrDTI1W86TlRUmhIx1QpCFsE8hdOsHFSHkHoRh0BVC/RSz0TD3x5UVANddZ/7yD37Q8yZqpIxOEVnxCKGb3sb1gybCGtP7FEIAs/kD5oUuCKFN3peW4BFCrwdhntN6+4Uk9fppuDGdkinMXX6T+Uv33298G0QI02eOe49th1EmhJ3DThkFgo1lJidhEMIL2EvOh21629lzrF5XB5vxOJCZP6fSRQEgmWSVKQKdKIQesXClDI8QVipe3BCF0LY8gFdnbfe0Y3LRHsNuZU+5TPPAThmdnDQPQ2YTe7EGbRNsQwh1l9GFBRUv5nLASF0xkyKGvHZXAKu6vSKTofhE7mXVqtlbEuAaQsaAELQYRyqEKVYIw6Aby6TTalONx/W+aC6B27/f9KxfXASmpzv6OzYh9BxGm02Mnf4eAN1hlHZyJhom9IAnUiFM7DEv9EAIgxRCjxBqnwOuH+wNQU6jNoeY3X+LecFKGw1MGX3yMe+x3YOQW050Dp0QyuBm3z4VmE5PU93n3r3wetsBbh1ho9G2t127+sF8nowumBB2B7uGsC0hhACyOUUIgY7rCIMUwnBCyAphJxBCkcDFRX/KaDKp1esCwPAwpoRmLPP446Gyk76fhRnKAJIQqkHlMesNsk5dP7Q2SiHACiFjQAhKtQmrIeQehNHQU0aFUItgoWCqFdPTQLEUC1YJO0BoALSwgOEmyYXzGKexcjdRVghNxOM0Ro0GPZbptI0GjZueXluMjVItk8TDD3et5oYRwgWMsUI4AMi5l82aNt26QYwv9ddqPyEPbQxCeEK1nFhFgVNGe0QyqWph5B6Tyah9pNUCzp3zpyF24jTa1mE02wCWNVLhPpEJYXvYKaNBpjJG6mE2YxLCDusIg2KQ5WXQB6JKhNAj84m4770wgqGnjeqK0vIy3VqjjjCewNTh55kv8O//Hvi6YYRw3z4YUhURQrUQMyHsDfKgUyeEtscAE0LGQCAXez2A8imEvpRRVgjDoKeMAmoRTCTMtoMAcOwY+iaEsgehR+JnZ5EF/fE57PQMZQAmGjaEME/B9VO3et0i0Jk0ZguH1PcrK57zZBT0YLXZdOtjZmch4GgKoWIWrBD2Bjn3qlU1drmcSe5mMWFOhKUl4D/+w/vWRwhLJaQXVNRTTowY6YxMCLuDbSwD+NNGldMo3WfPafS4St21YZMKPS214NAfMxxGhWBSEYFOUkb37tVSD7M5zGCXOjjrkhD6FEI3EBmWhFCrR+Oxi4ZOCJNJdc9aLdq+fMYy++80L/zbvwW+ru4yqpeq7NoFg5msosCEcAAI6kVoO/LOz3d8Pr0lwIRwi0KSP7uGSoIVwu6QStG+1WgopUnCLuQ+ehQ9G8vom2ilohaD7NIFxNz2wDbRYELoR+d1hAKzVz7L/OUO00b1E9WVFcBZqwLFFRSwijhamBcT5FzpgsepN+hOo/IeZrNm2ujyikDtx15i/qKWNirHyvudmRlkoBbE0mVHjOIbThntDkHGMrbTqEcIc1Yvwg6cDwMVwiblH3Kj7O7QScpoKqW5+tqtQjpIGXWcNoTQDUSSqMOB8OoHMxl/QMzwI5+n8anVaLzstFGfscz4080LIXWE+n5WLqvrhQLaKoRM4nuDPH9MaQlKq6tmvFKtdtUJa9PBhHCLwiaEjsMuo/0izGnUVggffxw9K4R6AKQvyrkFqwehphByyqgfcpGNbE4PYPYyy5Dk+9/v6G/o6oWdLgoA8yOHvJNvgAlhr9DVeXkPg1oLzD3n5eYFjRBKhbBWcy/MzCANtSCW9pi9QznI6Q5RxjJTUxohdPPcLmAvHXF97Wuhr9u2B2GdCqy5B2F30FPqHcdPCOUhpEcssrTXeGmjHSiEOrnQTU5sQgiA6wd7gK4S2k6jV1xhPvds6pB54cEHtYVQIYwQ5nLwFMIG4qhA5e7r/WEZ3SGdVjWhcs6dOOGPTy4lYxkmhFsUNiFsNMwJn4i1gIsXVVN6AEiluSl9G4T1IrQD/ccfh18h7NBpVD9VNRblmdPeY1YIoyEPMGu1DoxlJizy3qVCmEiYDqOjoK7BCwVzZ+Z51RtSKSKAtZoZ/NgF+LNPe4EZfX7ve15avC9l1FIIy7sOGK/FCmF3kEGhTtjGxtR9rFS09O3hYUAIVJDFMkaAU6dC07RtlckghPO0JnqE0D0F4gC1PeyU+lTKTCOVXGHfPvcX7NYTJ05E7mVB6qDXUN2tuxBuxgsTwu4RRgiDFMKziwU4k/vUhbU1Whst6C6j+sFOLgdyA4b/8CWf5xYvvSIWo/h8clLd+4sX/evXpVRHyIRwi8ImhD51cH4eqNeVOhiPA/E4N6Vvg7DWE/m8ufHNzABLI705jeon4h4hbLWQu3jSew4rhNHoqvXEsHWC2iEhbKcQOgDmU6Z0zMS9NwihyJ8+7+z0stnmGHDrrebFL3wBAJF3+QUAYmYaKahT8qnkQePXuIawO0jHvEpFu8fCrxJOTgKIxT3LWC9tNEQlbJsy+gQp+Z6pjNs2hAlhNDoxlvHGLk0nMh4hXF42i8wCEORyXqm4f29tDWlUVVP6JDel7xaSEC4t+RXCnTvNPa9UEli+7UXmCwSkjeomXbK8SAggm3GAb3wDgEYICzTnmMT3B2mUtk/j6/ZZCxNCRt+QE1ouDL76QTftg3sQdg699YS+EFYqwCGLUzx+PAZce615sYM6wkCFcHkZuTqpTlWkUEqMen2A4nF/7xqGShntSCFMTpoXjh835dkQ6EHP8jK8lXsEyyghj1pG9UlIpZhk9AM5hvoZSyxmbp6zswBe/GLzF3/914GvfhWtFgWjQgBwHKRnzkIebH8Lt+E/z+0zfm2f+S0jArGYGpt2/Qi99Ho3ivXq0kIIYduU0WPfpWsySHUXZSYW0bDHSr9nS7TVmK0nMtmuWk900nJi1Wo5wePWOdJpGsNm07zHS0v+gxgAOHPkx8wLAcYycj/Ts0mzWSD25BNepkURrk33EB++DAJyX9PHSxdvAE4Z9UEIcbkQ4qtCiB8KIX4ghHibe/3/EUKcE0I85H69RPud9wghTgghHhdC/IR2/SfdayeEEP/nRrz/jYbjqEkdqhB+lzZTjxC6p6tMCMMRVkNYLALXXGM+t9c6wkBCODuLHOgbanZuOoxyyoYfXSmEy2ng8GF1odXqiLzrKdi6QjiKpcC0Xh6n3iEPYxxHBSGZjGksMzsL4FWvMlMcymXgJS9B6+vfRK0GiLUK8NnPIlOj6HQNafxR4l1ARn1IrroKeNrT1vk/9BREkLGMTQi91hMuIexZIWy1UHjsQbrmqRYcpHYK2xXWM5CB6jtvHIpkszgH7UJEHWFoywmAm9IPCFIl1A/F/GSeMDV5m3nhgQd8UpSca7p4kMvBUwcBlxAOj3i18Txm/UHua/pcK5fN3tasEPrRAPAOx3GuA/BMAL8ihJDR9vsdx3mG+/VFAHB/9moA1wP4SQAfFkLEhRBxAB8C8GIA1wF4jfY6TxnUajTXZe0NYAoe2Sy8HHLPYdQ98WGH0XCE1RCurgJHjpjPDawj7IJkhBHCOezk+sEO0JVCOAvgxhvNix2kjepBz9J01duNqQfhuOEwyvWD/SHIaTSft1pPzIIOYX73d81fXltD841vRv1Tn4b4+F8CP3jUqx/8S7wOF/OHAVcvFAJ45zs5bb4XBBnLTE6qezk7q82D4RFACEUIT5wwm5+50OeY42iEcG4OhTU6gCEb/JRnisZBajRs8q4bo128qK55hC6bxTzGUYa7pnVICAMVwkrF6kHINYS9QBLCRkMdNhaL9L3PWCZ5yEyvuHiRanc1yNhDFw/yeQBf/7r3fRFDwKhKSeIx6w9yX0sklOlWNqvamwlhEvStjg3ZNh3HueA4znfdx0UAjwFol9TzcgB/7ThO1XGcJwGcAHCb+3XCcZyTjuPUAPy1+9ynFIJaTvhco1xC6CmEbk44K4ThyGRoglarpEDpTpYHzRIkHD0KONeuv0LI9YPBaKcQjo2Zat3SElC73nIajSCEujoIAMsn5wHXJGEEy1jYez3VSrlg4t4fgpxGc7kAhRAA3vUu4A/+wPj9Vr2B2qc/B1EhRpFGFSdxEJ8WdwH7VfT0sz/rP9xhdIYgY5lkUnMXhUYw4nFgaEgRQiBQJdQPyKpVNd6p2XNIuS6Vqyi4B5rCeB+McNgKoT5GkhDqQaqcgJ5KOICU0aKVMsrkojuMjtI+Viqpz7zj0H32GcucjwO3BaiEGoIIYS4HgxCuomAULfKY9Qd9X7v6anq8Zw9w993A3/4t8L//N/Dud2/e++sWG36OKoQ4AOAmALJ50a8KIR4WQnxMCOQ5OKEAACAASURBVCE7suwDcFb7tSn3Wtj1pxSCCKE8cQCAXLIOPPooAE0hLLBCGAUhzJpMfTHcscPsJ7OwAMxP3mC+QAdOo4GmMnNzrBB2iXYKYSLhV+zmD9xsXgjp1SRhj1PjIuV1ZLCGDKqYnzRzDnmc+kPHCqHEO94BfPCD3rctxFBHyuvlmcYa/mD8d9G6+TZgjF5wYgJ4wxvW83/x1EaQQgiYwamx/I2OqhpCIJAQ6vPMqB+cfdJ7TIRQLcZMCKORy5Fyu7ZG93jXLnVINj8f7jTqEcLjx9u+fmc1hNKxkmsIe0EiQeuinkYP0AGnXUM4NQXgOc8xL1p7nExTNFJGK/PASWVoV4zv8AyhAB6zfqH7UkhCmMlQvD42ZqZcXwrYUEIohCgA+CyAtzuOswLgTwAcBvAMABcA/OEA/9abhBDfFkJ8e9aINLY+ohTC7Mxpb8WfxQQ1pE9xD8JOEJY2WqlQ7ZGOxytXmM3SFhbIgrQNfAqh47BC2APaKYSAWTMDAGd23WJe+Pa3gWPHQl9fVwj1lhNeD8JxU2ZiQtgf9PpdXSHUCeHioqkY4t57gT/5EwBAE3HUkCKrexHDI3e8BY897eeMiOZtb+N2E/0gnaZ1q143VQZdfdLHC6OjmMEuNOBGPREKoUEILxAhceCmsRWYEHYDIUwCn0yqvcRxlCG2RyzciXEW7oWHHzZtKS0EuYzaKaO2Qsjj1j1keKGHGYuLfoXw3Dmg+UyLEFrGMkE1hPkTZk/e4hVm5gsrhP0hkaCvZpMIoOTalUpoJ54tjQ0jhEKIJIgMfspxnM8BgOM4047jNB3HaQH4M1BKKACcA6CfkVzmXgu77oPjOB9xHOcWx3FumbjEWFJkyujpx7zHM9jl1Q8CrBBGQT/R0TewYlGd8EgEOo22SRvVnRCTSXfMikWgVlUKYWrSGFgmGsFIJFTzZV25lXND95ABgMcXdwHPe5558VOfCn19n8OoTQgLB4zncw1hf0gkaE60WioIicUU8QfMQNbDm98MfOxjaGULRAgnJtD4xTfhK9U7PWMEALjjDv8BOqN7BBnL6PVpc3PamjUyAgcC03BPZ44e9Q1goELoOMifPQoAqCGFBhLeHpZImPOdEQ5b0dWJuzSW8YhFJg0kkspptFRyC+WD0V3KKJvK9Ap5UKYTwqUlmod6O4pGA5g+9Czzlx95xBuUVovWz1jMyiZ77DvGrxSvMH0ReMz6hxy7atWMIdtMry2LjXIZFQA+CuAxx3Hep13XljC8AsCj7uMvAHi1ECIthDgI4CoA/wngQQBXCSEOCiFSIOOZL2zE/2EjEUkIT9JtqiJlnK7G46pQmRGMMIUwiBAePYqujGVkmk4qRWSmXIZHNDyFcMdVAFQBHCuEwZCkGjDTLuTpp4+8Pw5K3NfxyU+GpviG9SD0mtJn9hrPZ+LeP/TTcJnelsuZjmyBXhdveAOaX/pn1N7zf0O84fWYje3xWavfe++6ve1tBbs2DaCsEzkHl5a0LJR4wl9HqNUrAebBS7HoXlxcxFCFiOMqCip3DhQIs5tvZ7BrPoOMZZT7oQCGh0yn0QcfDH1t22W02VR/R6xVMIQi1xAOAJIQ6mURi4v0r89YpjhqxiOOA3yLKq/kPIvFrFjx4f8wXmN1r5kGxWPWP/TsF5sQRlQYbTlslEL4HAD3AHih1WLi94QQjwghHgbwAgC/BgCO4/wAwKcB/BDA/QB+xVUSGwB+FcA/gYxpPu0+9ymFyBrCY5ahjHu6Oj7O7npRaNd6IohkdGMso49bs+l+PzcHAcdzRbyYNd1rmBCGQxJC/TMtCWFgm5Cf+zlz0pw8CfyHuSFK6IHq0mzda9TsKYSOyQCZEPYPOffqdXVwNT5upomGlTZVEkOoje5GIimwvGx+Jp7zHM6MGBSC6gjjcbMUwQgi29QROo6Zmr2y4v7gwgUMg75ZRcE90GRDmW7RzlhGKoRGLdrQkEoZBSitPgS2QriyooLboeoc4mixy+gAIA/J9G0rrPXE2bMAnv1s86JbR6jPM2/uVirI6SmjQqC4wyxO5PnWP3SR4cABleGwsHBp9SAENs5l9JuO4wjHcW7UW0w4jnOP4zg3uNf/i+M4F7Tfea/jOIcdx7nacZwvade/6DjOEfdn792I97/RsJvSA9qpj+Mg90NKA2CH0e7RrvXE5ZebqRsrK8D0vh8xX6BDhdB2GBUAGohjOmYqT3vNbxka5MKqB/+SdO/fb6aWzc8D881R4GUvM1/kk58MfG2jB+HRC9AdRrF/P+ZXksbzmRD2jyCn0bExsy4tqOyzXqevRkOl+OqfCfskndE7glJGAXOd0vcljI6aDc81QhhEKgD4CaFW8sABaufQx8pxTIVwZobWuF27tLTsoSGsYBhFSeS6IIReumi9jpFVqtJhU5n+IQ/J9L1MEsKOjGU++UmgVjP2My/2OHMGeWgT+cYbUaxljF9nEt8/dMO0RMIsZ7nU0kZZT9piCGpKD2iTfGEB2TI5Is5gl5tuQ0/kU/JotKshjMUCjGUSVspoG6dRXSEMajlxEXvg5JXD6M6dVnDFMNBOIUwkgCuvNJ8fmDb6138NI7/QhZEyelwZBY1iCdWrb/QpJCMjYPSJIGOZ8XGTEB4/7p9eci7Jz8HKivmZsAMnRu/QFUJ9HHSyodd9YmQEZ7Bfff/oo14nZru1i5cyGqgQEphUdA7ZOqnZpHUxm1V1Z80mZcELQb0kAQBD5HjhpY0+9JDlEqQQSghnZjCCJWUGlM16JiU8dt1DrolCqHlSKtGWZa9rZ84AeNGLzMLOkyeB++7z0u4NQnj6tBd7AADuvFPNQRdMCPuHftAJXNp1hEwItxiCmtI7jjbJL1zwJvksJox0G1YIoxGPK3MLnYzJhdJXR7i815QN5+ctf3yFKEJ4DvsMG8R9T7mGKYOFPDVttcxaFhmsBNYRvvjFpgPMwgJw//2+1zZSRp9c9K6PYBkLB0xVeMcOrmsaBPTDGEkIbaK9tOTxCQ96TQxAwak+HqwQDg7xOI2TsefAJITGXEgkcWb0RvNFvvENACEKoeMAFy5gCLTgskLYHzppUO8RizS5kXtpo2troSUQtsuoRwgvXsQIlrGGDJqIA3l6A8kkH272gmSS7nFQ6wk7ZXRqCsTu3/hG8we//dtoLtIBi1FDaBPC5z7XqA0GmBAOAnJfK5cpfr/qKrVGnj3rz7bYymBCuMUQVD9Yq6kFOjkzhSRop2WH0d4gJ7BuVlIs0qIc6DRqF6yFbKK+lNFSCaiUFSFMHAAyilwyIWwPqUQ0GsGtJwIJYSoFvOpV5g8C0kZlsNpsApULlKMTRxMFrGJ+nxngcrroYBCkEArhN8Ky6whlgOM4NG6lkjosE4Ln0aAhVUKPBMBs82K0BgFwsXAYVWg5b27aqE4qHMc9dFtZASplpRCmdwJZdUjGhLA72MYyQQ3qDWIxNGym+IYYy4QqhNPTpsOo+waYWPQOuS7qLXOWlmhd0w9fZmfdve83f1NNUgCYm0Pzgx8CQHOu2QRQqyF18bQXKwJA89l3+g7X9Jdh9IZ0mmLKep2mU7msDmFGR1UK8KUAJoRbDJGGMtOnvMdKISSwQtgZJCGUSixAG+Damp/7HTsGONcFpI0GwKcQulKHRwh33WSs8BzItkdYc/qg1AxAc/W65x7zB1/4ghndwurZ5Cq+QyhCAJjfab4wE8LBIJ2mj3+tZlqq2yTAriPUCaE8uJGEcNcuViYGDWl0JQkFQPNPEnchzPFzRncE1hHqadmlkjvnLlxAFhUvUF294jpjTWRi0R1sYxldIZTGMsY+M2Q5jQbUEUozID2N0SaEXv1gIS9fltEjggjh4iKRcdtjYGoKNMjveIdxvfnh+4CFBVUdMTWFXEuTA6+6CquFPcbv5PNsQjgICAE84xm0PtbrFB4ePAj80i8Bb33rpRXn8cdhi6FtywnHQfac8mVnhbA3tGs9MTlpnpqVSsC5y59pvkCIsYyPED5G/SI9Qjhi9jScnASjDfTm9LqKJDN2r7jC7842OwvgWc+iFVmiWgU+8xnjtSUhrJbqnhWYLMCfHzpgPJd7EA4GQpgmCnJ8h4bMHtntFEIZmMpAhtNFB4+JCQpGV1bM9hM62dAJIdURagPx0EPAAw8YCmGQoQwArF5mHr6wQtgd7JRRWyF0HKsWbTjaadRuOQG4885tFGoSwoLxPhjdI6g5fZjT6Pnz7oN3vtNQAJqVKvCXf6kIIdcPbijSaeDGGyldNBajve7UqUtLHQSYEG45tCWExSJyJWWAMRvfY6TbsELYGdq1nhAiQHnK3WRe6CRldLEKfJ8snz1COGamIl5KJ0ebAV0h1FPWZtwpEIsBR46Yv3P0KGgQg3oSapBBz9oT54AWRa4FrAKTk1iomnk0rBAODnoT3zBjGV0hbLVongpBpFESC0kI2VBm8IjH1XyTKhNgEkJdzUAyhdNHftx8kXe/G426472ebigj6wcBoDhx2Pg1JhbdIZejuVGp0PwYGlJjU62S0mSmjA5hCpfB8wt6+GGVg+/CThf1DmKWl4Gq1pQ+Hjf6RzJ6QzunUTtGOHfOfTA0RKmjLpqIA//wD6g+8jjJiMePmw6jXD+47pDlC7fcAgwP0/wLsZvYsmBCuMXQlhBqhjJVpFDM7/HSbbgpfedo13oC8DuNnklZdpbf+x7s47ZGgzbkeJw20vJX/h1Yo9zGHMpojk3gYtqUM5gQtoeuEOrq94w6EwmuIwSA177W/MG//qtH0AFNITx+1rtWwCpw3XW+3kFMCAeHoDrC0VHT0XJuTgVEcu1LJIgcSkMZmWXICuH6QCpN09NqrujqU9LsyoIzz3mNeeGBB1D5+y8DoDEPVQi5L1pfEMJ0hhXCbywzNqal3CdTKGfGsAzXzanRIFKowSaEa2vuYef0NFKoIYM1IoT5AqShHZOL3tGuOX0oIQSAN73J63HQQgxoNVF76zuAj/45cOF8pELIc219kMsBN91ELuiHD0c/fyuBCeFmQrI/DW17ENoOo9oqzE3pO4fudmgrhIBfdThb2WmygmIR+MAHjOfo6iAAlO//uvezHMqYedWvotFSOTijo9YpO8MHeS9tQjg9rR4HNqgHiCneeqv5w1e8wksPbTQANJuofPGr3o+ZEK4/5NxbWFD3NRZTdWsSMm1Urn2SgMgehJIQskK4PigU6JS70VCn3DrRcByTxJ8WB4CXvtR4jdLvfQhoNpHPu4RwdRVYLSpCmEphNWemtXCQ2j3aGctcuBBgvGQ3qLeMZaIMZQTclhPaYDEh7B1yTdQPWSIVQoA2yPdSK+4mKLaoaeZOHiGcnAQOHuSU0Q2EEKTM62nXlwKYQmwGvvMdSmk7csRHCtuayly86E3yBYwZC7IdUDHCkUrRRK3Xzbz9MEI4dT4G/MqvmBf/8A+NBHFj3B5+GOUTauXOoYxzL3q98eusDkYjHqfgv9mkU25JAhYWVIphqLEM4Cu8x5NPAnfdBTQaaDYc4H3vQ+VBdTpewCpwyy0+Qsg1hIPD7t0UaC4smNft7AaZNioJYTyuUte4B+HGQNY4y7olPR0xmzXdRqemgNZ7f8cwiCk9OQ3cf78ihK5LjUcIb7wRq2UzYuIgtXu0M5bxtZ4AvLRRD1YdYWjLCZcQArJ/pEqt53HrHXovQl2VLZcjCCEAvPKVwM03e4RwDUpm9FJG3/EOQAgmhIxIMCHcSDgO8FM/RUnGn/oUdRr9m78xfty2Kb3r0AZIQqhmNKeLdge5COuncnLBDOr/47z918ybvLQEvO993rcGIbzvPpSh5L/cc2/FuTini/YCfXwkMXMc1atun9naEaurWt3TXXeR1ZeOr3wFeNe7yKb7i/+ICtSJQP7QHuBVr/KRFSaEg0MqRak0AE0hGXzaypCtEMZiFCTV64oQZjJ8ELaesM1l9HTETMasearVgOldNwCvex0AoIE4qkgj9hcfRaZVprXVnZheDeGP/IivrokVwu7Rzlgm2Gl02HQa7VQhdHsQApIQcv/IQSAWU/GeHmKcOkXzzW49YWgIsRjwiU+guYtOb6qxPLBzArj6GuRefxftd7/2awDANYSMSDAh3EgI4U8qfv/7PUkjqCk94AZF5TKwsqwUwvguww6Tg9buEJSmIQnhjh0myahUgIXWKDl76fijP/JSEL2U0XoJ+MQnTEL45p9X7mAumBB2Bt1YJihtNNAE6HHtmw9+ELjjDvMJf/RHaPx/dBDjjdPwCAqfug9rTtruUMFza8DYs4fuaT7vTR+kUmYKoq0QCuF3GL38cqtJOmOgCDKXCXUaBZ1v4rd+C0inUQLtTfn50xBvvRcrf/Ip4N/+DYCmEN50E9c1DQC2Qjg2pva11VX6Mo1lCmbK6GOPGWzBdhldXgadxCwseISwiGEj/mBy0R/kAbWu5B4/TuOoG6oBptETAODaa9H8yr8Cn/0cqh/4U8pmevWrkXv3vcALX+gtkjzXGFFgQrjReOtbzSjmoYe8vk1B6aKAGxS5uR+SEC5OXm+wRlYIu4NcgOUpKKAWzKBm11NToLHTJYliEfj93wegjd39n0e9WEEdtCPHCzmkfvZlvlQPJoSdoS9jGYCYxmc+Y0REDlTNRQVZIJcHfv7nUTgyiRMnTGKyb5/5GWEMBkeO0KFLq0XKxtAQPZa4cIGUKV0hZIfRjYdtLqMTQj3dHnAJ4RVXAPfeqwghSnA+9jGs/McPPJMtSQidm/wKITfK7h6pFH01m6Six2L+tFFjriSSODP8NPV9q0VxiItAhXBmBoCjCOHo5UBcLYxMLvqDjEf0uOD4cdqLItNGATSdGDA2hkpNpWDbHgWcMsqIAhPCjcbhw8DLX25ee//7AYQTwkoF3rGQpxDuNnvasYrRHWQwoyuxq6uKDPiMZc6Cdr13v9v8wR//MTA9rRTCT37MVAdvvwEilWRC2COiWk8AfkJ49Kj1Irt3A3/3d96u23KXvRiaKKXGqZ53fByFgr8puv3ajMEgk6GlcHiYVEIh/CfhP/whxaqpFAWpUiFkQ5mNg20uoxMN+6Dk9Gn3wXveg9IQMck8Sqgi7ZldJFFHBmvAoUNYu/Ym4xBAEhtG97CNZewG9fv3m88/nb0GVc2ARE8bDSSEbkqGRwjHzBdkctEfZDyim83J8odOCKGMHfX2PfbhChNCRhSYEG4G3JxuD3//98CJE+0VwpMnAcCrIVzccch4DhPC7iAX4GZT3e9GQ7m82nWE3iL8lreYu225DPzGb6D6n98HvvQlpB95UCOEArnn3wbHAaeM9ogwhVB3GrVJ27FjpsoHALj5ZuCjHwUANECRTjOZReM19wB79yKZpGDUUBcDXpsxOOzdS1+tFpnM6OMLECEEKEBaW+Om9JsFqRKeP08JEpIoJJOqBhRwFUIAGBtD6RfuBUCEsAgVeQ5lGhB33w18+ctYrZq9K1hl6h122qhdR1goKJMgAHCGhnECWjslzVgmkBC6GUpeDeGw9gfA5KJfSIWwVjPbXh07Fk0IHUdlUuhGT7ZCyDWEjCgwIdwM3HknBagSjgN84AOBhNBxgPJjp4GTTwDQFMIhMxpiQtgdum49IdvV5XLAe95j/vDP/gzVX3gz8Hv/L9KoKkJ45ZXIXT6OuTlVYwjQ3+PFuDPoCuHYmApSikXlvrtnj3k/KxVtvHT81/8KfPCDaGYKwM4J1H/rfwAHDgBQwSgTwo2DEMCNN9K/soeajsceo39zOToBD6ohZKw/du1S5jLFolJyR0bMdc0jhABKr7gb+MkXI5cTWNl7DXDrbcA9P4/hP34v8IlPAAcPcoA6QNjGMjr5O3WKDl2OHNF+YWgIx6Bd0BRC3WW02XT3xOlpCDheuu9q1jy9YTLfH/T+rPo4BRFC+3B5bY3GVzqnA7RG6n0NAa4hZESDCeFmQAi/Svixj6E6QxGPTgjrNQeNf/4XAEACDSRRB+64Awtxs38TE8LukE7TMKytmakV7ZxGPbzpTb6aNJkSlUJNEcJbbkEu51/A9c2a0R66QhiLkfOhhEwbjTSW0XHvvWhMzwOf/jRqN9ziXS4U6JRVD2oB87SWMXgcOaICk4UF8xT7xAn6N5dT5hgAE8KNRjyugtIzZ1SCRKFgKvHLy/RVqwF1J4H4//VuZErzWPnHbwAveQlw6BCGx1SeKQeog4OdMrp7t7pWLtP+ZayRhSEcE9qF48e9Nkq6Qri4CDgtB5iexjBWEAfl+BYTpqMQk/n+oB9QHz6s1rjz5/3mTbZCKA8B9J53uZz/gI1TRhlRYEK4WXjlK01mUC5j7a//DoDVcuILXwbOUHFGDmUIAM57/wcWF83ZzqYy3UG3etZP0hYX6d+gNA2v3iWTAX7zN72f1ZGEA4Ek6ojBIUK4azdw1VXI5fwLOKeLdg69OT3Qo7GMhWYTgBCGfXehAJ+hzOWX+9NuGIPF5ZcDz342PU4kqJ5QjvXUFCmDuRylvcmxkQcD9gk4Y/2wbx/d9/l5UgYBCjhtEnfmjEpfkwdt0gwIoHpECXtdZELYO3I5Gp9KhQidEH6lyVgj43EcG7nNfJFvfQuA6TK6sABi+dU1jMN11C6MoRZXjkJBahSjO6RSNGa1Gh2C6unwtpJ+8aKZGqo7MUsE7VtMCBlRYEK4WUilgF/9VeNS9a8+BzQaihC2Wqj89u97P8+hDLz4xVi96U5jQchmeUHuBXoht4TehFm/3miYdWv4b/+NLNavvx61a54OXHsdUrc+A3jFK1B+9S8Cr30tEIsxIewTUiGUqWmd1hHKPnZBkClRNiG0SaSRYsVYN/zMzwDj4xRYZrPUY9JxaJzOn4dPZY/FWB3caKRSqi5Nrxu0nUZPn1aKhSSEeiCqE8KvftX8XVbje4dOziWB0NfExx/3398ns9eaxjJ/+qcATIVwYQHeQjsGatBavO52AIp9DA1x+5d+IYSK4dbWzLE6dco0N3ccr6QTgJpv7Qhhq6X1s3bBBzAMG0wINxO/9EvejuoAqM0uAV//uiKEn/kMyo8+4T09iwrw3vf6GmezOtgb5AKsEz+9x0/btFEhSCV89FFUv/kg8OEPI/3nHwI+9zmU736TF/kwIewPesooEO406pYCetA3TBsy4JEGQgAFr1w/uDlIp4FXv5qmVD5P47K4SEHMuXP0c31eMiHcHMi+j82mSRr0w8kzZ/yEUFcIpSqxtAR85zvm67/whevzvrcLZIAvCfihQ6rmenaWDtUMY5lde0xjmc9/Hjh6NJIQrl6t+R+AlaZBQU8b1Q8jT540TYIAM6aQRE937F1bAz7wAeDjH6dxt1VGqSgzGDr4I7GZGBsDXvc6AFSD5kAgdd8fI/aRP6WI6Dd+w2xh8KxnADfd5COEXD/YG+QCrJ9aT0+rACfUWMaCbQakn8QxIewPuqkM4E8ZlWmEtkPl3JypZOgIUwi55cTm4elPB37kR2hOOg4RhloNePRR+vnsrHpuLMYOo5uBTEYZzMi00ZERRQCBaEIo19qvfc0MYA8cAA4eXLe3vi0giZkM/pNJIoUSx45ZWQ87RnHs0IvV944D/MEfRCuEB24w/i4rTYOBrhDu3KkO+mXavA4ZUziOmm9yL2w2gb/6K+q09Bd/Afz3/27OQYBJPCMYTAg3G29/OwCgCmIT6ZkzwC//Mkkhx44pQihiyL3ulQDAhHBAkISw1aKUNflYpiIGNqcPgNeD0CUvOiHMZrnlRD+IxaiWxXGIqA8NmRun3OjSaVPpbbWIFAYhiBDG4ybhF4JT2DYSqRTw3OcCV15J49NqUenS8ePAP/2TqQazQrh5kPd9504ap5ERU33olBD+y7+Yr/ujP7o+73c7wVYIAX/aqHnIJXDs9nuM12h+/FNwZucQi9E8W1iAl27hKYRXXGf8DpOLwUB3Go3afyQhrFZVr1YZh8zNKUdmgAxk3Y5LHnjMGEFgQrjZuPpq4Kd/WhFCmB1GK3BZyzOegdwRymGUxicSnDLaG/QUDT2VRhK4QSiEjYb5fSbjdw1jtIeuEtoNzHWioLeHBKyaTw3yBFy2rbBfByAFyq6PYqwvJieBV7yCDGNaLSIcy8vAhz5kknsmhJuHQoEOIPfto/kzPExblVwDz5+n9U729dTXv1iMSOLsLPDww+brcrpo/8jnaX0sl9Whl64Inj7tb1B/LH2DISOW6wngs59FJkOvsTRb806gd2AREALFXYeN12CFcDDQ4xHAHDt9rwIUIdQPX+Tj2VnTcRQA/vVfze+ZEDKCwIRwK+C++1B96c8CIqYIoYsyckAsDjzved6CwQrhYKCfyOlkQhLCtjWEGtophK6Tt4d9+7gAv1vYdYRhTqM6UQTC6whlsKTXENoqLqeLbjzGx+lw6/WvV3OkWqXgR1dz02n/WDM2DldcQWvdjh1E8goFFYw2GhSQBhnKFAr0fNtM5ppruBXPICAJN6BU26EhdW9bLX/d2KkzMVTf9n943y9jBPjCFzAsilheBloXZwG3/2ASDeDwYRQb5kkZk4vBQM98ASiNWu59jqP2P0ARQhlr5HLq8exsdH0gk3hGEJgQbgXs2oXqB/4U+Ju/Qfptv2ywkzJywLOeBYyMeHnktkLIhLA3JBK04LZapouXNLCwUzunp81FWUJXCB3HJIQ2eed00e7Rro5QVwFtktBOIXQc89T19GnzOewwuvGIxWgMn/50Sh8FiBguLqr6GCGIkPChyuZhdJSUwf37ifDJOkLpDDsz0126KKuDg4NdRwiYh1tTUyb5brWAJ+54ndfgdQXDQLmEkc9/HAtfewT4h38AoNJF8fSn+wxKmBAOBvoBNUDxiRRvh4bMuOLCBZprukJYLtMcDFIIbfCYMYLAhHCLoFoFMDGB9Ht+nQox/vZvgTe+EeW7fsHbMSUhZIVwcJCqq552OzNDxC+dNsmH4/iVJMAkhLWaUqCSST8pYULYPXpVCMMIYbNJYyZJRjqtmqBLeyLObAAAIABJREFUsEK4OZBnYW94A42zEGZvSDaU2RrYv5+cD2s1InqNBikbzSbNuzBCODVluvkKAbzgBRv73p/KCKoj1A+3TpygOl0dx85kgLe+FYCrEAIYfv9vYf5n3ghcpNNRjxDedJOvnx2rTYNBMkkksNlUe50cu2TSVP3kwYuuEJZK9H25HK0QMiFkBIEJ4RaBTBNIp0Gz/+d+DvjIR1B+/ku82c0K4eAhT+V0lVA3lrHTRu06wlaLFm8haNjYYXTwaNecfm5OuRXaNYTtUkbX1tSmmUya4ySEP2hibAwKBfpKJslvS9rmS3D94NbA+DjtO1dcoQ5sVlfV2hnWg9BWB2+80czOYPSHIIVwzx6lzlYq/vt97BiAt7wF1dwOVJFGAg3kyrNYgDolHcc8Lbz33MMK4TrCThvVjWXicdM5+9w5v0Io3ZilQnj11cF7GY8ZIwhMCLcAWi01sW17YT2tLUwhZFOZ3iEVwkrF7PUTZixj1xHq9YOyoF+CCeFgYDenz2ZVgNNoAPPz9LiblNFKRRFCO8DZv19tzIyNhyT2t90GvPa15s+YEG4dHDxIKW1SxS2VaG7NzKi9SlcICwV2F11vSGOZUkkdlAlhZjzY6tHjjwMYG8Py3b8CABjGCgSABaiT5rEX3w5873vAFVf4FEImF4ODnTY6PKxSfIeGzHjw1CkiiFJZrFT8hPDGG6ldsr2fsbEdIwhMCLcAVlZoYufzSg2RsFsYOA67jA4SOiEMchqNUghlumiQoUwQIWTzhO5hK4RAcNpoECHU0w0lmk2TENrGP5wuurmYnCSicfAg8J73mM6IiQTXd24VjI7SWOzeTURQzquZGXXAqRPCYtGs1Y3HVa0oYzCIx2nf0fvTAeac0fcogIhFtQqs3P0WIBbHCKhnwQLGgB1jwGvvxthfvM/bvDhldP0g4xFZkwuosbMJ4ZNP0r/5PF2XrZZiMVVjfd11dID2jneoa+k02VIwGDaYEG4BSIJnE7t6XQXB8TgFxsvLZkPfIBLJ6BxhrSeksUynCmFQywkhzM0zmfRq9xldwFYIgeDWE4WCqbDX6/7DE0AphHKD1JueA0w4NhuyTjCfp/n5P/8nqfeZDHDXXX7rfMbm4dAh4NpraQ9qtVSq2yc+Qf/qhPD73zd/95ZbVIN7xuAQVEd48KCKE0olM9ZotYAnngCWc3uBt70Nw+kaWoVhLD3vp6kn8pVXGs/nlNH1g1TyTp8GvvEN6iEoBM2j4WFlHAOQ1QSg0kUlIdQNZa5zW0b+2I8BH/wg8Ja3AB/7GJcZMYLBhHALIIwQ2mqTEGwoM2joKRp79yqSII1loprTt+tBaJ/ETk6yO2IvsE1lgHCn0U56Edo1hPZzWCHcWrjhBgqOvvUtSn9ibB0MDVFq7/79dNAi18PPf56yIyQhLBaBr3zF/F12F10fBNURJhLAYa19oO1C+dhj9Hzx8v+C4bmTWH5iDs3n/yiQTGJoyDx0ZoVw/TAxQV+ZjColkgct0hFdHrrIWES2nFhcpL1N7mtjY+YB9NOeBrzylZylxAgHE8JNRqNBC6wQ/rxumxACnC46aKRStIDW6/SvLLh3HDIl2bPH3DwXFsxxadeD0D5J5frB3mC3nQBMhbCb1hPSGl8qhNWqmTIai7GhzFZEIsFq0lbF059Oap+ebdFsAh/5CK2BjgN885vm/M3lgDvu2Jz3+1RHkEIIAM9+tnpst9159FG6ls8D8UIWC8Wk9zP90LlU8scgsp6b0T9SKeD664FnPhO4807g5pspbrj8cpUZJsftwgU1ZkGGMtddxwfQjO7AhHCTsbREk3p42H9qpy/Y3JR+fSCEGcjYxjKJhHkNMFXCdgqhvXGyGUZv0BVCmS6zc6c6CV1cVOMQ1ZxeurRVqzT28/PmvDtwQI0lg8GIRj5PNUnSEXF1lebZv/wLZVr84AeqtknizW/2G6gxBgNJCPU6NID2nxtvpMfj4xRLyJ//8If0rzx00eMMPcZ44AE6xJbYu1c5yjIGi3ic1N5Dh0iBb7WUk7njqMNM2XIiiBAyGN2ACeEmIyxdFAhWCJkQDh562miQsYxN5HRjmXamMnZt2oEDfb/VbQnZ0gNQaaOJhGmfLpXAKIVQBjMy7cYmhJwuymB0j5/8SZo7w8MUuC4tESl84AHgu98159jttwM/9VOb916f6kgk6JBTdy+XeNGLaK8aGyPFVqqIp06pnpJAOCG0XWI57Xf9kUgAN91E/6bTtAfKPrpLSzSeukIoD16uvXbz3jPj0gQTwk1Gt4SQexAOHmFOo9JYxnYa1Z1D25nK6E3TASaE/SCojlCvF5TEL6qGcJkM9DylcHHRDFb1vk8MBqMz5PPA3XeTUijNtCoVml+tluonOTwMvOtdnMq23giqI5TX77yT9quhIUXc63Xa7yQhlK18ABVjFIvAt79tvt4LXrA+759h4sABik1SKdNBVtbozs2px/G4v9UIg9EJmBBuIqpVIhDxeHAePiuEGwOdEO7ZY7pP1mrtW0+EpYy2Wn6F8IorBvu+txOCWk8E1RFGKYTz8zQ2cowXF81UtkOHBvN+GYzthttuA57/fApcpXohIQ9dfv3XKV2Rsb4IqyNsNEih3bGDxqHZpJii1aJ+hEGlKTLG+MY3zMbol1/O6+VGIZuldN9slsZQEkJ5wHnsmHpuLEausnIsGYxOwYRwEyHVvtHR4BPToBpCNpUZPGTK6NoaEQ/bWCas9USzqVy95Am4JIS63TpArphcM9M7olpPyFrBdjWEjkOBTrWqXNxWV01CyCoug9EbhAB+4ifIYEY2q5dIJChd8XnP27z3t51gK4Rra0T4HngAeOQRakMgTc6Wl+mg7bvfpTWx1TLjDEkIv/pV82+84AWs9G4knv1sUuKlS3arBTz0EJH+kyfV8+Jxrh9k9AYmhJuIdumiACuEGwV5b4tFOn2z6whthfDMGVXUDSh1UHduW1oyiQb3TusPQQqhnh46M0P3f8cORR4BmkMyKFpZUTWEyaRy95UYHWUnSwajH0xOElGwlaOdO4G3vnVz3tN2hFQIV1dJPfrWt5QrZbFIpOL5zyei3mrRteVlIoUrK0oJLBRof1taop/p4PrBjcXkJHDNNfS42aT4Y2aGWvE88YR6XizGhJDRG5gQbiIGQQhZIewf2Szdx0aD0kFtQrhzp3JScxwalzNn/IRQntoBrDwNGkEKYaGgxqVWo/kkRHjaqJw7klzapP3gwcG/bwZju+GFL6S0RKlSJZPA29/O/eo2EskkZUE0m8ocbfduSjtMJmktvPFG2pdaLVo/Gw3g/vuD00W//nW1twG0VvIh58bjRS9S8Um1SmrgQw+RKZAEK4SMXsGEcJNQKtEinEqF2zbbhLDZVDnjEkwIBwNJBqamTPdKmR567bV0crq8TP/+4z+qnwU5jNrubrx59ocgUxmgs7RRSQilUYJ8raUlf8sJBoPRH0ZGKCXxpS+lAPanf5rqCxkbC7mP7doF3Hor7WFjY9SgPBajbJbbb1epvaurVCeot1Vqly7K2Hjcfjv1/dyxg+JH6Taqk/VCgf0KGL2BCeEmIUodBPyEUPYslBgeVrVrjP4wPKyK7CVRB+i09MknaZyaTdXY/PvfVwQjyGHUriFkstEfZJ2n7ZoX5DQaVEdYrSrVVqqCTAgZjPXBHXcAExNUpzY2RunYjI3F4cPkKHrddWb9+siIcqC8+WbKkBGCCOLqqkn+xsZon/v+983XZkK4OZiYoH3qpptISLh4kVKBJSEUghrbc20noxcwIdxgtFqU0y+D13aE0DaVYUOZ9YVUCWdnaVGV+OIX6bRVCCKOQ0O0Se7fT+kbsjhfdxi1lVw+sesPo6NE5FZWwo1l2jmNyjSoHTvUONktJzhllMEYDFIpakNx663AXXeZdb2MjYEQ5vqmY/duIhZjY8ANN6hDsuVl4JvfVM8bGwO+9jXzIPqqq/x19YyNgRDUw3NigtT3dFqVrgCcLsroD6wvbTDOnwfe9CYKbMfHacM8coSK8K+5htI7AMrnl4FvLEYTnw1l1heFAt3/mRnVfqJWA77zHWq8nM+rDXZ+nu6/bkIiLb5tq+/xcVVPw+gN8TiRufl5+tq7l64HpYwG9SKUc2d8HDh+nEj7yoo5h1ghZDAGh927KW2UsTVx4ACRiZe+lOrQACp1OHmSDkRHR2l9tNNF2Uxmc3HzzXR4+cADFBt+6UvKBCgWo5RgBqMXsEK4wXjySZq8rRY1E/3614H77gPe+U7g5S8HPvIRep6dLioEN6XfCBw4QPd6bY0UwNlZ1a7APnn7wQ/UY8ch4ghQKqJ+Is71g4OB7F+mN02emFAkfWmJxi0oZVQSwrExSotaWaE5KH+XSTuDwdhuuPpq4Bd/keoLAVUSceIEfT83Bzz6qPk7z3/+hr5FRgDGxoCXvQz4nd8BfvmXVbxx/fUkMjAYvYAJ4QbjiSfIyfLiRVKipqYoB3xmhv798IfpX245sTnI5YhQOA4phrUa1WnOzpKSq0PfKE+coHEDiHDo5IKVp8FAEsLFRVUzEY+bJkAzM35CePYsBTn5PNUilkqqabYkhDxGDAZjOyKRAF79avPaE08ADz5I7rA6rr3Wn4HB2DwMDZGY8NWvAp/8JPCZz5jO2QxGN+CPzgYjnabUt1QqOL9/eZly9oOa0jMh3BhIlXB8nMjGxISpJklIhdBxSOmVyOXM57JCOBik07QBNpumWm6njcp6T4n5eSL2cr6srjIhZDAYDInXvMY8xKxUiBTqtYMAuccyth4uu4yMnMJqRhmMTsCEcIPxmtcAb34zcM89ZMf94z8OXHmlkvwdB/hf/8usQ5MKIZvKbAwyGTKKEYLGR7qILi2Z9s5Hj1Kt56lTpEIBtCDbmyiTjcEhKG3UdhpNJIjES9TrNHbj/397dx5kV1nmcfz7CyEL6SSEpIOQdEAyWUgySLMMq5rSKEx0oiWOBQQG3AbUUVEoZoJO6ZRT4zals4COiKKjICAoqzhsgUJNGANEGAhKgIBkIUASOwmQIskzf7zn1L196XSTTvddzvl9qm71veec+573nifd6aef933P+BS/l15Kr6sXXXCMzKysRoyAk07qvq12BfNjjvGcULMi86IyDbBwYaoQ7r13+qX03nth0aK04MzOnWme4T33VI73kNH6mzo1rSA6fHgaGprfRmLYsJQEQqo6rVwJ991Xed+b3pTiWc3JxsAZPz4l4NUJ4a5WGl2/PlUT8/t3jhmThotGVG5Kn1cSvcKomZXZWWfBDTdU7vWa/5G6oyP9Efu443w7A7Mic4WwAdraUmKR/3CdMSP98M3v1bRhQ5qzlttVQugK4eCRKkM/85sqSylBrK4ALl6cEnhICcbUqd1vnr7vvikRsYExenRlqe28il6bEO7cWdlWnbwPGZKGi+YrjHpYr5lZMmcOHH98ei6ln6HnnQff/37a7mTQrNhcIWwC7e0pcYhIFYxt21JFI0/4XCFsrCOPTBXbV19NsXjqqRSvYcPgjjvSfZwgfc3npuVcHRx448enavqLL6YEsa0tPbZsSTHauDH9MrNzZ+UeTa+8kr5u3Zq+t6rnhLa3p/ebmZWVBJdckhYmGTEirWI5alSje2Vm9eIKYROQ0j0I84VMpLTcc27kyFTpqJ5XKHW/B54NnpEjobMzPZ84MSUVq1enBUxWrEiJvARvfnMazljNlaeB19M8wtoqYXt7itOOHWkuTL5IkxeUMTPrWVsbnH12WnXUyaBZudQlIZTUIWmxpEclPSLp09n2r0t6TNJDkn4uad9s+8GSXpa0PHv8V1VbR0p6WNJKSf8hFWMgw4wZ6evw4SnRq/5ld599XrugzNixXlGqnubNS7ed2G+/ymT7l19OC5Rs3ZruUThhAjz9dPf3OdkYeOPGpeGfmzdXKoDVCeGaNSkm27en75FRoypDsJ0QmpmZmXVXrwrhduD8iJgFHAt8QtIs4HZgTkQcBvwBWFT1nici4vDscW7V9m8DHwWmZY+T6/IJBtnMmZXnY8emX1xz48d7uGijDRsGp58O55wD06Z137d+faoOgiuE9TBkSOXff/6Hk3yl0QhYvjwle0OGpGRwyJBUzYVUea9NCL2gjJmZmZVZXRLCiFgbEQ9kzzcDK4BJEXFbRGTLPrAUmNxbO5IOAMZExNKICOC/gfcOYtfrJq8QQvoFdujQVHWaPz8lhLUVQieEjTFpEpxySvra1pYWAxo3LiUkEfDMM92Pd/VpcNQOG21vTwvHbNiQkvIJE1IymCd9L7wA99+fksU8IcxX0XOMzMzMrMzqvqiMpIOBTuC+ml0fAq6uev1GSQ8CXcDnI+JeYBLwbNUxz2bbWt64cWl+2vr16bWUbmGQJ4quEDaP2bNTxTC/110+P23dusoQRkgLnuQrx9rAyhPCDRtg6dI0RHTdusp9IqdPT0lhnvxt2QJXX52GZHd1pXmhI0emfa7impmZWZnVdVEZSW3AdcB5EdFVtf1zpGGlV2Sb1gJTIqIT+CxwpaTdWrxf0t9KWiZp2fPV93BoYtVVQoDf/77y3Delbx6zZ3d/vXJlmkvY0/zBYsxwbT7DhlVW5n3llVRRnzgxJXn77w9XXZWufb7/+efT3MKurlQZbG9P+ydOrKzia2ZmZlZGdasQStqblAxeERE/q9p+NvBu4O3ZMFAiYhuwLXt+v6QngOnAaroPK52cbXuNiLgUuBTgqKOOip6OaTYzZnS/qfljj8GCBem5K4TNY8wYmDKlMjw0Ai6++LWL/LjyNLhmz06VvxEj0kOCu+5K+/LbS6xenaqGO3fCkiXp1iD77+/5g2ZmZma5eq0yKuB7wIqI+EbV9pOBC4EFEfFS1fZ2SXtlzw8hLR7zZESsBbokHZu1+TfADfX4DPVQvbAMdK8Qrl3bfZ8rhI1VWyW89Va4+ebu2zw3bXDl8zdHjqzc9uN976vcjmXmzLTS6I4dlfe88EJllVhwQmhmZmZWryGjJwBnAm+rupXEfOBiYDRwe83tJd4CPCRpOXAtcG5E5DWyjwOXASuBJ4Bb6/QZBt306d1fP/VUmpP2xz+meVLVDjywfv2y1zruuL6PcYWwviQ47DD45CfhHe+Ajg44/vi0b8iQVBnMF5LJOWk3MzOzsqvLkNGI+BXQ02yqX+zi+OtIw0t72rcMmDNwvWseo0en1StXZ4NgI+Dxx+HnP0/Pc1OmwKGHNqaPlpx4IixcCNddl+ao9cTVp8YYOhROOAE6O+E3v0lzDR94IC0oU8sJoZmZmZVdXReVsb7VLizzy1/C4sXdt33wg6niYY0jwUc+AtdfD1/4QkoQq4ciHn98ZSVMa4x99oF589L8zrPO6vkYV3HNzMys7Op+2wnr3cyZlYUxAG65pfv+Qw6Bt761vn2yXRs+HObOTY+tW2HZslTNPfHERvfMchKcc05aYfTWqgHmHR1pMRozMzOzMnNC2GRqK4S1PvQh38qgWY0a5WS9WUlw/vlpgZnbbkurjO6qamhmZmZWJk4Im8y0aZX7p9WaMaOySIaZ7Z699oJFi+CMM1JlsL290T0yMzMzazzPRGsyI0emRWN64uqg2Z7r6HAyaGZmZpZzQtiEau9HCDBnDhx9dP37YmZmZmZmxeWEsAn1NI/Q1UEzMzMzMxtoTgib0BFHdH/d2ZkeZmZmZmZmA8kJYRM66KB0j7tRo9Lw0YsuanSPzMzMzMysiLzKaJNauBBOP93DRM3MzMzMbPC4QtjEnAyamZmZmdlgckJoZmZmZmZWUk4IzczMzMzMSsoJoZmZmZmZWUk5ITQzMzMzMyspJ4RmZmZmZmYl5YTQzMzMzMyspJwQmpmZmZmZlZQTQjMzMzMzs5JyQmhmZmZmZlZSTgjNzMzMzMxKygmhmZmZmZlZSTkhNDMzMzMzKyknhGZmZmZmZiXlhNDMzMzMzKyknBCamZmZmZmVlBNCMzMzMzOzklJENLoPg07S88DTje5HDyYALzS6E7ZbHLPW45i1Jset9Thmrccxa02OW+tplpgdFBHttRtLkRA2K0nLIuKoRvfDXj/HrPU4Zq3JcWs9jlnrccxak+PWepo9Zh4yamZmZmZmVlJOCM3MzMzMzErKCWFjXdroDthuc8xaj2PWmhy31uOYtR7HrDU5bq2nqWPmOYRmZmZmZmYl5QqhmZmZmZlZSTkhzEjqkLRY0qOSHpH06Wz7fpJul/R49nVctn2mpCWStkm6oIf29pL0oKSbeznnLyVtqj1G0t9JWikpJE3o5f1vlHRfduzVkoZl298i6QFJ2yW9v7/XpBUULG6fzT7HQ5LulHRQf69LMytYzM6V9LCk5ZJ+JWlWf69LsytS3Kr2n5K10bQrv+2JIsVM0tmSns++15ZL+kh/r0szK1LMsn0fqPosV/bnmrSCIsVN0jervs/+IGlTf69LMytYzKZkn+VBpd8h5+/u9XBCWLEdOD8iZgHHAp9Q+uXuH4A7I2IacGf2GmAD8CngX3fR3qeBFX2c8+vAmT1s/zUwj77vnfhV4JsR8WfARuDD2fZngLOBwv7wrVKkuD0IHBURhwHXAl/ro51WVaSYXRkRfx4Rh5Pi9Y0+2mllRYobkkZnfbivjzZaWaFiBlwdEYdnj8v6aKdVFSZmkqYBi4ATImI2cF4f7bSywsQtIj6Tf58B/wn8rI92WlVhYgZ8HrgmIjqBU4Fv9dHOazghzETE2oh4IHu+mRTUScB7gB9mh/0QeG92zPqI+C3wam1bkiYD7wJ6/Q8rIu4ENvew/cGIWNXbeyUJeBspcajt26qIeAjY2VsbRVCwuC2OiJey7UuByb211aoKFrOuqkNHAYWdlF2kuGW+RPrP9ZXe2mllBYxZ4RUsZh8FLomIjXlfe2urlRUsbtVOA37SW1utqmAxC2BM9nwssKa3tnrihLAHkg4GOkl/Od4/ItZmu9YB+7+OJv4NuJDBTcjGA5siYnv2+lnSP+TSKljcPgzcOoj9aApFiJmkT0h6glQh/NQg9qNptHrcJB0BdETELYN4/qbS6jHLnJINh7pWUscg9qMpFCBm04Hpkn4taamkkwexH02jAHEDQGnayhuBuwaxH02hADH7InCGpGeBXwCf3N3GnRDWkNQGXAecV/PXfyItydprBUDSu4H1EXH/4PXSahUpbpLOAI4iDS0orKLELCIuiYipwN+Thm0UWqvHTdIQ0tDe8xtx/kZo9ZhlbgIOjjSk/nYqf8EvpILEbCgwDZhLqjR9V9K+DezPoCtI3HKnAtdGxI5Gd2QwFSRmpwE/iIjJwHzgR9n/da+bE8IqkvYm/aO4IiLyMdPPSTog238A0NeQhxOABZJWAVcBb5P0Y0nHVE3SXdDP/v1P9v7LgBeBfSUNzXZPBlb3p91WV6S4SZoHfA5YEBHb+nO+VlCkmFW5ioIPbytI3EYDc4C7sz4cC9yo4i4sU4SYEREvVv1MvAw4sj/nawVFiRmpgnFjRLwaEU8BfyAliIVUoLjlTqWgw0VzBYrZh4FrACJiCTAC2OXiND0Z2vch5SBJwPeAFRFRvTDEjcBZwFeyrzf01k5ELCJNokbSXOCCiDgj2334nvQxIk6q6fNi4P2kf4B99q2IihQ3SZ3Ad4CTizzXomAxmxYRj2eHvQt4nIIqStwi4k9U/Ucp6e6sD8v25NzNqCgxy7YfUDWMawF9L97QkooUM+B6UuXicqWVE6cDT+7JuZtVweKGpJnAOGDJnpyzmRUsZs8Abwd+IOlQUkL4/O6ezI8IgBNJZeGHgOXZYz5pzO6dpF/07gD2y45/A+mvX13Apuz5mJo25wI393LOe7OAvZy9/6Rs+6ey19tJE0Mv28X7DwH+F1gJ/BQYnm0/Onv/VtJfFB5p9PV13F5X3O4Anqv6HDc2+vo6Zn3G7N+BR7LPsBiY3ejr67j1HbeaY+4mre7b8GvsmPX6vfbl7Hvtd9n32sxGX1/HrM+YiTQ8+1HgYeDURl9fx63vuGX7vgh8pdHX1TF7fTEDZpFWKv1d9jneubvXQ1lDZmZmZmZmVjKeQ2hmZmZmZlZSTgjNzMzMzMxKygmhmZmZmZlZSTkhNDMzMzMzKyknhGZmZmZmZiXlhNDMzEpH0hRJWyTt1ei+mJmZNZITQjMzKwVJqyTNA4iIZyKiLSJ21PH8cyU9W6/zmZmZvR5OCM3MzMzMzErKCaGZmRWepB8BU4CbsqGiF0oKSUOz/XdL+mdJv8n23yRpvKQrJHVJ+q2kg6vamynpdkkbJP1e0geq9s2X9KikzZJWS7pA0ijgVuDArP0tkg6U9BeSlkjaJGmtpIslDatqKyR9XNLjWXtfkjQ162eXpGvy4/MKpKSLJL2QVUQX1ucKm5lZq3JCaGZmhRcRZwLPAH8VEW3ANT0cdipwJjAJmAosAS4H9gNWAF8AyJK724ErgYnZ+74laVbWzveAcyJiNDAHuCsitgJ/CazJhqq2RcQaYAfwGWACcBzwduDjNf06CTgSOBa4ELgUOAPoyNo/rerYN2RtTQLOAi6VNGO3LpaZmZWKE0IzM7Pk8oh4IiL+RKrmPRERd0TEduCnQGd23LuBVRFxeURsj4gHgeuAv872vwrMkjQmIjZGxAO7OmFE3B8RS7N2VgHfAd5ac9jXIqIrIh4B/g+4LSKerOpnZ83x/xgR2yLiHuAW4AOYmZntghNCMzOz5Lmq5y/38Lote34QcEw2zHOTpE3AQlJ1DuAUYD7wtKR7JB23qxNKmi7pZknrJHUB/0Kq8PWnXwAbs2pk7mngwF2d38zMzAmhmZmVRQxQO38E7omIfasebRHxMYCI+G1EvIc0nPR6KsNTezr/t4HHgGkRMQa4CNAe9G1cNqQ1NwVYswftmZlZwTkhNDOzsngOOGQA2rkZmC7pTEl7Z4+jJR0qaZikhZLGRsSrQBews+r84yWNrWprdHbMFkkzgY8NQP/+KevHm0kHWrrrAAAA00lEQVTDW386AG2amVlBOSE0M7Oy+DLw+WyI5/v720hEbAbeSVpMZg2wDvgqMDw75ExgVTYE9FzScFIi4jHgJ8CT2VDTA4ELgNOBzcB3gav726/MOmBj1q8rgHOz85qZmfVIEQM1gsbMzMwaRdJc4McRMbnRfTEzs9bhCqGZmZmZmVlJOSE0MzMzMzMrKQ8ZNTMzMzMzKylXCM3MzMzMzErKCaGZmZmZmVlJOSE0MzMzMzMrKSeEZmZmZmZmJeWE0MzMzMzMrKScEJqZmZmZmZXU/wPrmC4zipOMMwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_df = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+1')][['timestamp', 'actual']]\n", + "for t in range(1, HORIZON+1):\n", + " plot_df['t+'+str(t)] = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+'+str(t))]['prediction'].values\n", + "\n", + "fig = plt.figure(figsize=(15, 8))\n", + "ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(plot_df['timestamp'], plot_df['t+1'], color='blue', linewidth=4.0, alpha=0.75)\n", + "ax.plot(plot_df['timestamp'], plot_df['t+2'], color='blue', linewidth=3.0, alpha=0.5)\n", + "ax.plot(plot_df['timestamp'], plot_df['t+3'], color='blue', linewidth=2.0, alpha=0.25)\n", + "plt.xlabel('timestamp', fontsize=12)\n", + "plt.ylabel('load', fontsize=12)\n", + "ax.legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6 (dltf)", + "language": "python", + "name": "dltf" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ReferenceNotebook/Quiz_2_Answer.ipynb b/ReferenceNotebook/Quiz_2_Answer.ipynb new file mode 100644 index 0000000..9a51d55 --- /dev/null +++ b/ReferenceNotebook/Quiz_2_Answer.ipynb @@ -0,0 +1,654 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Practice: One step multivariate RNN model\n", + "\n", + "In this notebook, we work through:\n", + "- preparing the time series data for training a RNN forecasting model\n", + "- get data in the required shape for the keras API\n", + "- implement a RNN model in keras to predict the next step ahead (time *t+1*) in the time series. This model uses recent values of temperature, as well as load, as the model input.\n", + "- enable early stopping to reduce the likelihood of model overfitting\n", + "- evaluate the model on a test dataset\n", + "\n", + "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load and temperature data.\n", + "\n", + "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import tensorflow as tf\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the data from `data/energy.parquet` into a Pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
loadtemp
2012-01-01 00:00:002,698.0032.00
2012-01-01 01:00:002,558.0032.67
2012-01-01 02:00:002,444.0030.00
2012-01-01 03:00:002,402.0031.00
2012-01-01 04:00:002,403.0032.00
\n", + "
" + ], + "text/plain": [ + " load temp\n", + "2012-01-01 00:00:00 2,698.00 32.00\n", + "2012-01-01 01:00:00 2,558.00 32.67\n", + "2012-01-01 02:00:00 2,444.00 30.00\n", + "2012-01-01 03:00:00 2,402.00 31.00\n", + "2012-01-01 04:00:00 2,403.00 32.00" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "# Insert code START\n", + "file_name = os.path.join('data', 'energy.parquet')\n", + "energy = pd.read_parquet(file_name)\n", + "# Insert code END\n", + "assert energy.shape == (26304, 2)\n", + "energy.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create train, validation and test sets\n", + "\n", + "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", + "\n", + "We will allocate the period 1st November 2014 to 31st December 2014 to the test set. The period 1st September 2014 to 31st October is allocated to validation set. All other time periods are available for the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Insert code START\n", + "valid_start_dt = '2014-08-31 23:30:00'\n", + "test_start_dt = '2014-10-31 23:30:00'\n", + "# Insert code END\n", + "\n", + "train = energy.copy()[:valid_start_dt]\n", + "valid = energy.copy()[valid_start_dt:test_start_dt]\n", + "test = energy.copy()[test_start_dt:]\n", + "\n", + "assert train.index.max() == pd.to_datetime('2014-08-31 23:00:00')\n", + "assert valid.index.min() == pd.to_datetime('2014-09-01 00:00:00')\n", + "assert valid.index.max() == pd.to_datetime('2014-10-31 23:00:00')\n", + "assert test.index.min() == pd.to_datetime('2014-11-01 00:00:00')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preparation\n", + "\n", + "For this example, we will set *T=6*. This means that the input for each sample is a vector of the prevous 6 hours of the energy load. The choice of *T=6* was arbitrary but should be selected through experimentation.\n", + "\n", + "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "T = 6\n", + "HORIZON = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data preparation - training set" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# Fit a scaler for the y values\n", + "y_scaler = MinMaxScaler()\n", + "y_scaler.fit(train[['load']])\n", + "\n", + "# Also scale the input features data (load and temp values)\n", + "X_scaler = MinMaxScaler()\n", + "train[['load', 'temp']] = X_scaler.fit_transform(train)\n", + "valid[['load', 'temp']] = X_scaler.transform(valid)\n", + "test[['load', 'temp']] = X_scaler.transform(test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the TimeSeriesTensor convenience class to:\n", + "1. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n", + "2. Discard any samples with missing values\n", + "3. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras\n", + "\n", + "The class takes the following parameters:\n", + "\n", + "- **dataset**: original time series\n", + "- **H**: the forecast horizon\n", + "- **tensor_structure**: a dictionary discribing the tensor structure in the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) }\n", + "- **freq**: time series frequency\n", + "- **drop_incomplete**: (Boolean) whether to drop incomplete samples" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from common.utils import TimeSeriesTensor\n", + "\n", + "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n", + "train_inputs = TimeSeriesTensor(dataset=train,\n", + " target='load',\n", + " H=HORIZON,\n", + " tensor_structure=tensor_structure,\n", + " freq='H',\n", + " drop_incomplete=True)\n", + "\n", + "\n", + "X_train = train_inputs['X']\n", + "y_train = train_inputs['target']\n", + "\n", + "assert y_train.shape == (23370, 1)\n", + "assert X_train.shape == (23370, 6, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Data preparation - validation set" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to allow T lags, we need to add the last T-1 train samples to the validation set\n", + "valid = pd.concat([train.iloc[-(T-1):], valid])\n", + "\n", + "# Create TimeSeriesTensor\n", + "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)\n", + "y_valid = valid_inputs['target']\n", + "X_valid = valid_inputs['X']\n", + "\n", + "assert y_valid.shape == (1463, 1)\n", + "assert X_valid.shape == (1463, 6, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quiz: Implement multivariate RNN" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Multivariate, multilayer RNN](./images/one_step_RNN_multivariate_mutilayer.png)\n", + "\n", + "Implement your RNN model with the data prepared above and the following requirements:\n", + "1. Use 2 features: past load and temperature\n", + "2. Stack 2 GRU layers\n", + "3. 6 hidden units in the first GRU layer\n", + "4. 4 hidden units in the second GRU layer\n", + "5. 5 epochs\n", + "6. Batch size 32" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "FIRST_LAYER_LATENT_DIM = 6 # number of units in the 1st RNN layer\n", + "SECOND_LAYER_LATENT_DIM = 4 # number of units in the 2nd RNN layer\n", + "BATCH_SIZE = 32 # number of samples per mini-batch\n", + "EPOCHS = 5 # maximum number of times the training algorithm will cycle through all samples" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.models import Model, Sequential\n", + "from keras.layers import GRU, Dense\n", + "from keras.callbacks import EarlyStopping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fill in your code below and replace the question mark" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Fill in your code to replace the question mark\n", + "# Hint: there is a parameter you need to add when stacking multiple RNN layers\n", + "# Insert code START\n", + "model = Sequential()\n", + "model.add(GRU(FIRST_LAYER_LATENT_DIM, input_shape=(T, 2), return_sequences=True))\n", + "model.add(GRU(SECOND_LAYER_LATENT_DIM))\n", + "model.add(Dense(HORIZON))\n", + "# Insert code END" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once you done, run the rest of the notebook to check if your model works." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_2\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "gru_1 (GRU) (None, 6, 6) 162 \n", + "_________________________________________________________________\n", + "gru_2 (GRU) (None, 4) 132 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 1) 5 \n", + "=================================================================\n", + "Total params: 299\n", + "Trainable params: 299\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.compile(optimizer='RMSprop', loss='mse')\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Specify the early stopping criteria. We **monitor** the validation loss (in this case the mean squared error) on the validation set after each training epoch. If the validation loss has not improved by **min_delta** after **patience** epochs, we stop the training." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 23370 samples, validate on 1463 samples\n", + "Epoch 1/5\n", + "23370/23370 [==============================] - 5s 203us/step - loss: 0.0178 - val_loss: 0.0016\n", + "Epoch 2/5\n", + "23370/23370 [==============================] - 4s 166us/step - loss: 7.8720e-04 - val_loss: 5.7741e-04\n", + "Epoch 3/5\n", + "23370/23370 [==============================] - 4s 167us/step - loss: 6.0092e-04 - val_loss: 4.5872e-04\n", + "Epoch 4/5\n", + "23370/23370 [==============================] - 4s 169us/step - loss: 5.7493e-04 - val_loss: 4.6584e-04\n", + "Epoch 5/5\n", + "23370/23370 [==============================] - 4s 171us/step - loss: 5.6263e-04 - val_loss: 4.5794e-04\n" + ] + } + ], + "source": [ + "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)\n", + "\n", + "history = model.fit(X_train,\n", + " y_train,\n", + " batch_size=BATCH_SIZE,\n", + " epochs=EPOCHS,\n", + " validation_data=(X_valid, y_valid),\n", + " callbacks=[earlystop],\n", + " verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_df = pd.DataFrame.from_dict({'train_loss':history.history['loss'], 'val_loss':history.history['val_loss']})\n", + "plot_df.plot(logy=True, figsize=(10,8), fontsize=12)\n", + "plt.xlabel('epoch', fontsize=12)\n", + "plt.ylabel('loss', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the test set" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to allow T lags, we need to add the last T-1 validation samples to the test set\n", + "test = pd.concat([valid.iloc[-(T-1):], test])\n", + "\n", + "# Create TimeSeriesTensor\n", + "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)\n", + "y_test = test_inputs['target']\n", + "X_test = test_inputs['X']\n", + "\n", + "assert y_test.shape == (1463, 1)\n", + "assert X_test.shape == (1463, 6, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
timestamphpredictionactual
02014-11-01 00:00:00t+12,451.822,434.00
12014-11-01 01:00:00t+12,428.212,390.00
22014-11-01 02:00:00t+12,409.272,382.00
32014-11-01 03:00:00t+12,443.472,419.00
42014-11-01 04:00:00t+12,523.052,520.00
\n", + "
" + ], + "text/plain": [ + " timestamp h prediction actual\n", + "0 2014-11-01 00:00:00 t+1 2,451.82 2,434.00\n", + "1 2014-11-01 01:00:00 t+1 2,428.21 2,390.00\n", + "2 2014-11-01 02:00:00 t+1 2,409.27 2,382.00\n", + "3 2014-11-01 03:00:00 t+1 2,443.47 2,419.00\n", + "4 2014-11-01 04:00:00 t+1 2,523.05 2,520.00" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from common.utils import create_evaluation_df\n", + "\n", + "predictions = model.predict(X_test)\n", + "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n", + "eval_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE: 1.50%\n" + ] + } + ], + "source": [ + "from common.utils import mape\n", + "\n", + "print('MAPE: {:.2f}%'.format(100 * mape(eval_df['prediction'], eval_df['actual'])))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "eval_df[eval_df.timestamp<'2014-11-08'].plot(x='timestamp', y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8), fontsize=12)\n", + "plt.xlabel('timestamp', fontsize=12)\n", + "plt.ylabel('load', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ReferenceNotebook/Quiz_3_Answer.ipynb b/ReferenceNotebook/Quiz_3_Answer.ipynb new file mode 100644 index 0000000..b31e9c7 --- /dev/null +++ b/ReferenceNotebook/Quiz_3_Answer.ipynb @@ -0,0 +1,732 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Practice: Multi step model (simple encoder-decoder)\n", + "\n", + "In this notebook, we walk through:\n", + "- preparing the time series data for training a RNN forecasting model\n", + "- getting data in the required shape for the keras API\n", + "- implement a RNN model in keras to predict the next 3 steps ahead (time *t+1* to *t+3*) in the time series. This model uses a simple encoder decoder approach in which the final hidden state of the encoder is replicated across each time step of the decoder. \n", + "- enable early stopping to reduce the likelihood of model overfitting\n", + "- evaluate the model on a test dataset\n", + "\n", + "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load.\n", + "\n", + "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import tensorflow as tf\n", + "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load data into Pandas dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
loadtemp
2012-01-01 00:00:002,698.0032.00
2012-01-01 01:00:002,558.0032.67
2012-01-01 02:00:002,444.0030.00
2012-01-01 03:00:002,402.0031.00
2012-01-01 04:00:002,403.0032.00
\n", + "
" + ], + "text/plain": [ + " load temp\n", + "2012-01-01 00:00:00 2,698.00 32.00\n", + "2012-01-01 01:00:00 2,558.00 32.67\n", + "2012-01-01 02:00:00 2,444.00 30.00\n", + "2012-01-01 03:00:00 2,402.00 31.00\n", + "2012-01-01 04:00:00 2,403.00 32.00" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "# Insert code START\n", + "file_name = os.path.join('data', 'energy.parquet')\n", + "energy = pd.read_parquet(file_name)\n", + "# Insert code END\n", + "assert energy.shape == (26304, 2)\n", + "energy.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create train, validation and test sets\n", + "\n", + "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", + "\n", + "We will allocate the period 1st November 2014 to 31st December 2014 to the test set. The period 1st September 2014 to 31st October is allocated to validation set. All other time periods are available for the training set." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Insert code START\n", + "valid_start_dt = '2014-08-31 23:30:00'\n", + "test_start_dt = '2014-10-31 23:30:00'\n", + "# Insert code END\n", + "\n", + "train = energy.copy()[:valid_start_dt]\n", + "valid = energy.copy()[valid_start_dt:test_start_dt]\n", + "test = energy.copy()[test_start_dt:]\n", + "\n", + "assert train.index.max() == pd.to_datetime('2014-08-31 23:00:00')\n", + "assert valid.index.min() == pd.to_datetime('2014-09-01 00:00:00')\n", + "assert valid.index.max() == pd.to_datetime('2014-10-31 23:00:00')\n", + "assert test.index.min() == pd.to_datetime('2014-11-01 00:00:00')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preparation\n", + "\n", + "For this example, we will set *T=6*. This means that the input for each sample is a vector of the prevous 6 hours of the energy load. The choice of *T=6* was arbitrary but should be selected through experimentation.\n", + "\n", + "*HORIZON=1* specifies that we have a forecasting horizon of 3 (*t+1*, *t+2*, *t+3*)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "T = 6\n", + "HORIZON = 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create training set:\n", + "\n", + "1. Scale (This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data.)\n", + "2. Create TiemSeriesTensor " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "# Fit a scaler for the y values\n", + "y_scaler = MinMaxScaler()\n", + "y_scaler.fit(train[['load']])\n", + "\n", + "# Also scale the input features data (load and temp values)\n", + "X_scaler = MinMaxScaler()\n", + "train[['load', 'temp']] = X_scaler.fit_transform(train)\n", + "valid[['load', 'temp']] = X_scaler.transform(valid)\n", + "test[['load', 'temp']] = X_scaler.transform(test)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from common.utils import TimeSeriesTensor\n", + "\n", + "tensor_structure = {'X':(range(-T+1, 1), ['load', 'temp'])}\n", + "train_inputs = TimeSeriesTensor(dataset=train,\n", + " target='load',\n", + " H=HORIZON,\n", + " tensor_structure=tensor_structure,\n", + " freq='H',\n", + " drop_incomplete=True)\n", + "\n", + "\n", + "X_train = train_inputs['X']\n", + "y_train = train_inputs['target']\n", + "\n", + "assert y_train.shape == (23368, 3)\n", + "assert X_train.shape == (23368, 6, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Construct validation set \n", + "(keeping T hours from the training set in order to construct initial features)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to allow T lags, we need to add the last T-1 train samples to the validation set\n", + "valid = pd.concat([train.iloc[-(T-1):], valid])\n", + "\n", + "# Create TimeSeriesTensor\n", + "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)\n", + "y_valid = valid_inputs['target']\n", + "X_valid = valid_inputs['X']\n", + "\n", + "assert y_valid.shape == (1461, 3)\n", + "assert X_valid.shape == (1461, 6, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quiz: Implement an encoder-decoder RNN" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Encoder-Decoder](./images/encoder_decoder_multilayer.png)\n", + "\n", + "Implement your RNN model with the data prepared above and the following requirements:\n", + "1. Use 2 features: past load and temperature\n", + "2. Stack 2 **LSTM** layers for the encoder RNN\n", + "3. 12 hidden units in the first LSTM layer\n", + "4. 6 hidden units in the second LSTM layer\n", + "5. Repeat the vector output of the second encoder layer 3 times (one for each time step in the forecast horizon)\n", + "5. Add a decoder LSTM with 6 hidden units\n", + "6. 5 epochs\n", + "7. Batch size 32\n", + "\n", + "The model will have the following structure:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from keras.models import Model, Sequential\n", + "from keras.layers import LSTM, Dense, RepeatVector, TimeDistributed, Flatten\n", + "from keras.callbacks import EarlyStopping" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "ENCODER_LAYER_1_DIM = 12\n", + "ENCODER_LAYER_2_DIM = 6\n", + "DECODER_DIM = 6\n", + "BATCH_SIZE = 32\n", + "EPOCHS = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "model = Sequential()\n", + "# Insert code BEGIN\n", + "model.add(LSTM(ENCODER_LAYER_1_DIM, input_shape=(T, 2), return_sequences=True))\n", + "model.add(LSTM(ENCODER_LAYER_2_DIM))\n", + "model.add(RepeatVector(HORIZON))\n", + "model.add(LSTM(DECODER_DIM, return_sequences=True))\n", + "model.add(TimeDistributed(Dense(1)))\n", + "# Insert code END\n", + "model.add(Flatten())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer='RMSprop', loss='mse')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "lstm_1 (LSTM) (None, 6, 12) 720 \n", + "_________________________________________________________________\n", + "lstm_2 (LSTM) (None, 6) 456 \n", + "_________________________________________________________________\n", + "repeat_vector_1 (RepeatVecto (None, 3, 6) 0 \n", + "_________________________________________________________________\n", + "lstm_3 (LSTM) (None, 3, 6) 312 \n", + "_________________________________________________________________\n", + "time_distributed_1 (TimeDist (None, 3, 1) 7 \n", + "_________________________________________________________________\n", + "flatten_1 (Flatten) (None, 3) 0 \n", + "=================================================================\n", + "Total params: 1,495\n", + "Trainable params: 1,495\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 23368 samples, validate on 1461 samples\n", + "Epoch 1/5\n", + "23368/23368 [==============================] - 8s 326us/step - loss: 0.0259 - val_loss: 0.0077\n", + "Epoch 2/5\n", + "23368/23368 [==============================] - 6s 270us/step - loss: 0.0078 - val_loss: 0.0050\n", + "Epoch 3/5\n", + "23368/23368 [==============================] - 6s 266us/step - loss: 0.0053 - val_loss: 0.0046\n", + "Epoch 4/5\n", + "23368/23368 [==============================] - 6s 271us/step - loss: 0.0043 - val_loss: 0.0031\n", + "Epoch 5/5\n", + "23368/23368 [==============================] - 6s 268us/step - loss: 0.0038 - val_loss: 0.0040\n" + ] + } + ], + "source": [ + "history = model.fit(\n", + " train_inputs['X'],\n", + " train_inputs['target'],\n", + " batch_size=BATCH_SIZE,\n", + " epochs=EPOCHS,\n", + " validation_data=(valid_inputs['X'], valid_inputs['target']),\n", + " callbacks=[earlystop],\n", + " verbose=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_df = pd.DataFrame.from_dict({'train_loss':history.history['loss'], 'val_loss':history.history['val_loss']})\n", + "plot_df.plot(logy=True, figsize=(10,8), fontsize=12)\n", + "plt.xlabel('epoch', fontsize=12)\n", + "plt.ylabel('loss', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# In order to allow T lags, we need to add the last T-1 validation samples to the test set\n", + "test = pd.concat([valid.iloc[-(T-1):], test])\n", + "\n", + "# Create TimeSeriesTensor\n", + "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)\n", + "y_test = test_inputs['target']\n", + "X_test = test_inputs['X']\n", + "\n", + "assert y_test.shape == (1461, 3)\n", + "assert X_test.shape == (1461, 6, 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = model.predict(test_inputs['X'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.11, 0.12, 0.14],\n", + " [0.12, 0.15, 0.18],\n", + " [0.12, 0.16, 0.21],\n", + " ...,\n", + " [0.56, 0.51, 0.46],\n", + " [0.51, 0.45, 0.4 ],\n", + " [0.48, 0.44, 0.39]], dtype=float32)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
timestamphpredictionactual
02014-11-01 00:00:00t+12,351.642,434.00
12014-11-01 01:00:00t+12,371.162,390.00
22014-11-01 02:00:00t+12,380.142,382.00
32014-11-01 03:00:00t+12,420.842,419.00
42014-11-01 04:00:00t+12,490.102,520.00
\n", + "
" + ], + "text/plain": [ + " timestamp h prediction actual\n", + "0 2014-11-01 00:00:00 t+1 2,351.64 2,434.00\n", + "1 2014-11-01 01:00:00 t+1 2,371.16 2,390.00\n", + "2 2014-11-01 02:00:00 t+1 2,380.14 2,382.00\n", + "3 2014-11-01 03:00:00 t+1 2,420.84 2,419.00\n", + "4 2014-11-01 04:00:00 t+1 2,490.10 2,520.00" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from common.utils import create_evaluation_df\n", + "\n", + "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, y_scaler)\n", + "eval_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "h\n", + "t+1 4.03\n", + "t+2 5.02\n", + "t+3 6.65\n", + "Name: APE, dtype: float64" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n", + "eval_df.groupby('h')['APE'].mean() * 100" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE: 5.23%\n" + ] + } + ], + "source": [ + "from common.utils import mape\n", + "\n", + "print('MAPE: {:.2f}%'.format(100 * mape(eval_df['prediction'], eval_df['actual'])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot actuals vs predictions at each horizon for first week of the test period. As is to be expected, predictions for one step ahead (*t+1*) are more accurate than those for 2 or 3 steps ahead" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No handles with labels found to put in legend.\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_df = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+1')][['timestamp', 'actual']]\n", + "for t in range(1, HORIZON+1):\n", + " plot_df['t+'+str(t)] = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+'+str(t))]['prediction'].values\n", + "\n", + "fig = plt.figure(figsize=(15, 8))\n", + "ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(plot_df['timestamp'], plot_df['t+1'], color='blue', linewidth=4.0, alpha=0.75)\n", + "ax.plot(plot_df['timestamp'], plot_df['t+2'], color='blue', linewidth=3.0, alpha=0.5)\n", + "ax.plot(plot_df['timestamp'], plot_df['t+3'], color='blue', linewidth=2.0, alpha=0.25)\n", + "plt.xlabel('timestamp', fontsize=12)\n", + "plt.ylabel('load', fontsize=12)\n", + "ax.legend(loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Requirements.txt b/Requirements.txt index ec2646a..0c1812e 100644 --- a/Requirements.txt +++ b/Requirements.txt @@ -1,8 +1,11 @@ -keras==2.2.4 +keras==2.2.5 numpy==1.16.2 pandas==0.23.4 -tensorflow==1.12.0 -matplotlib==3.0.0 +tensorflow==1.15.0 +matplotlib==3.1.1 scikit-learn==0.20.3 -pyramid-arima==0.8.1 -statsmodels==0.9.0 \ No newline at end of file +#pyramid-arima==0.8.1 +statsmodels==0.9.0 +xlrd==1.2.0 +pyarrow==0.11.1 + diff --git a/Slides/2019 KDD-Deep Learning for Time-series Forecasting.pdf b/Slides/2019 KDD-Deep Learning for Time-series Forecasting.pdf deleted file mode 100644 index 9132acb..0000000 Binary files a/Slides/2019 KDD-Deep Learning for Time-series Forecasting.pdf and /dev/null differ diff --git a/Slides/Deep Learning for Time-series Forecasting.pdf b/Slides/Deep Learning for Time-series Forecasting.pdf new file mode 100644 index 0000000..c26c6bc Binary files /dev/null and b/Slides/Deep Learning for Time-series Forecasting.pdf differ diff --git a/automl/1_AzureAutoML_local.ipynb b/automl/1_AzureAutoML_local.ipynb new file mode 100644 index 0000000..1a79af2 --- /dev/null +++ b/automl/1_AzureAutoML_local.ipynb @@ -0,0 +1,2855 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# AutoML model\n", + "\n", + "In this notebook, we demonstrate how to:\n", + "- prepare time series data for training a forecasting model using an automated ML model builder\n", + "- Creating an Experiment using an existing Workspace\n", + "- Configure AutoML using 'AutoMLConfig'\n", + "- Train the model on local compute \n", + "- Explore the engineered features and results\n", + "- Configuration and run AutoML for a time-series model with lag and rolling window features\n", + "- Run and explore the forecast\n", + "- evaluate the model on a test dataset\n", + "\n", + "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load and temperature data only.\n", + "\n", + "This notebook is based on the energy forecasting notebook provided [here](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb).\n", + "\n", + "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Imports\n", + "We start with the usual imports and settings" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", + "\n", + "# Squash warning messages for cleaner output in the notebook\n", + "import warnings\n", + "warnings.showwarning = lambda *args, **kwargs: None\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Data\n", + "Load the data from csv into a Pandas dataframe. Make sure to first complete the [0_data_setup](../0_data_setup.ipynb) notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
loadtemp
2012-01-01 00:00:002,698.0032.00
2012-01-01 01:00:002,558.0032.67
2012-01-01 02:00:002,444.0030.00
2012-01-01 03:00:002,402.0031.00
2012-01-01 04:00:002,403.0032.00
\n", + "
" + ], + "text/plain": [ + " load temp\n", + "2012-01-01 00:00:00 2,698.00 32.00\n", + "2012-01-01 01:00:00 2,558.00 32.67\n", + "2012-01-01 02:00:00 2,444.00 30.00\n", + "2012-01-01 03:00:00 2,402.00 31.00\n", + "2012-01-01 04:00:00 2,403.00 32.00" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "file_name = os.path.join('../data', 'energy.parquet')\n", + "energy = pd.read_parquet(file_name)\n", + "energy.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Create train, validation and test sets\n", + "\n", + "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", + "\n", + "We will allocate data as follows:\n", + "* November 1, 2014 to December 31, 2014: **test** set. \n", + "* September 1, 2014 to October 31, 2014: **validation** set. \n", + "* Everything up to August 31, 2014: **training** set." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "valid_start_dt = '2014-08-31 23:59:59'\n", + "test_start_dt = '2014-10-31 23:59:59'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "energy[:valid_start_dt][['load']] \\\n", + " .rename(columns={'load':'train'}) \\\n", + " .join(energy[valid_start_dt:test_start_dt][['load']] \\\n", + " .rename(columns={'load':'validation'}), how='outer') \\\n", + " .join(energy[test_start_dt:][['load']] \\\n", + " .rename(columns={'load':'test'}), how='outer') \\\n", + " .plot(y=['train', 'validation', 'test'], figsize=(15, 8), fontsize=12)\n", + "plt.xlabel('timestamp', fontsize=12)\n", + "plt.ylabel('load', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Data preparation - training set\n", + "\n", + "AutoML takes care of a lot of the details. We simply need to prepare a Pandas DataFrame for each of Train, Test, and Validation.\n", + "\n", + "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "HORIZON = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "Our data preparation for the training set will involve the following steps:\n", + "\n", + "1. Create a time column from the index\n", + "2. Filter the original dataset to include only that time period reserved for the training set\n", + "3. Drop rown with missing values " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### 1. Add time column" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "energy['timestamp'] = energy.index" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "### 2. Filter the original dataset to include only that time period reserved for each set\n", + "Create training, test and validations sets" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "ds_train = energy[:valid_start_dt].copy()\n", + "ds_valid = energy[valid_start_dt:test_start_dt].copy()\n", + "ds_test = energy[test_start_dt:].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Verify that the series are continuous." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " load temp timestamp\n", + "2014-08-31 19:00:00 3,969.00 74.67 2014-08-31 19:00:00\n", + "2014-08-31 20:00:00 3,869.00 74.00 2014-08-31 20:00:00\n", + "2014-08-31 21:00:00 3,643.00 73.00 2014-08-31 21:00:00\n", + "2014-08-31 22:00:00 3,365.00 72.00 2014-08-31 22:00:00\n", + "2014-08-31 23:00:00 3,097.00 71.33 2014-08-31 23:00:00\n", + " load temp timestamp\n", + "2014-09-01 00:00:00 2,886.00 71.00 2014-09-01 00:00:00\n", + "2014-09-01 01:00:00 2,768.00 70.00 2014-09-01 01:00:00\n", + "2014-09-01 02:00:00 2,699.00 69.33 2014-09-01 02:00:00\n", + "2014-09-01 03:00:00 2,681.00 68.33 2014-09-01 03:00:00\n", + "2014-09-01 04:00:00 2,690.00 68.33 2014-09-01 04:00:00\n" + ] + } + ], + "source": [ + "print(ds_train.tail())\n", + "print(ds_valid.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### 3. Discard any samples with missing values\n", + "We will discard these." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "ds_train.dropna(how=\"any\", inplace=True)\n", + "ds_valid.dropna(how=\"any\", inplace=True)\n", + "ds_test.dropna(how=\"any\", inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "We now have data of shape:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train data shape: (23376, 3)\n", + "Valid data shape: (1464, 3)\n", + "Test data shape: (1464, 3)\n" + ] + } + ], + "source": [ + "print('Train data shape:', ds_train.shape)\n", + "print('Valid data shape:', ds_valid.shape)\n", + "print('Test data shape:', ds_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Set Up AzureML Workspace" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "As part of the setup you should have access to an Azure ML Workspace. For Automated ML you will need to create an Experiment object, which is a named object in a Workspace used to run experiments." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SDK version1.0.76
WorkspaceAML2
Resource GroupAML2
Locationsouthcentralus
Experiment Nameautoml-forecasting-GEFCom2014
Project Folder./project
\n", + "
" + ], + "text/plain": [ + " \n", + "SDK version 1.0.76 \n", + "Workspace AML2 \n", + "Resource Group AML2 \n", + "Location southcentralus \n", + "Experiment Name automl-forecasting-GEFCom2014\n", + "Project Folder ./project " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import azureml.core\n", + "from azureml.core import Experiment, Workspace\n", + "\n", + "assert os.path.exists('config.json'), 'Download `config.json` from the Auzre Portal and place in this folder'\n", + "ws = Workspace.from_config()\n", + "\n", + "# Define a project folder where artifacts will be stored\n", + "project_folder = './project'\n", + "os.makedirs(project_folder, exist_ok=True)\n", + "\n", + "# choose a name for the run history container in the workspace\n", + "experiment_name = 'automl-forecasting-GEFCom2014'\n", + "experiment = Experiment(ws, experiment_name)\n", + "\n", + "output = {}\n", + "output['SDK version'] = azureml.core.VERSION\n", + "output['Workspace'] = ws.name\n", + "output['Resource Group'] = ws.resource_group\n", + "output['Location'] = ws.location\n", + "output['Experiment Name'] = experiment_name\n", + "output['Project Folder'] = project_folder\n", + "pd.set_option('display.max_colwidth', -1)\n", + "outputDf = pd.DataFrame(data = output, index = [''])\n", + "outputDf.T" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Train\n", + "\n", + "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the name of the time column and the maximum forecast horizon. See [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py) for more options.\n", + "\n", + "|Property|Description|\n", + "|-|-|\n", + "|**task**|forecasting|\n", + "|**primary_metric**|This is the metric that you want to optimize.
Forecasting supports the following primary metrics
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error|\n", + "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.constants.supportedmodels.regression?view=azure-ml-py).|\n", + "|**experiment_timeout_minutes**|Maximum amount of time in minutes that the experiment take before it terminates.|\n", + "|**training_data**|The training data to be used within the experiment.|\n", + "|**label_column_name**|The name of the label column.|\n", + "|**compute_target**|The remote compute for training.|\n", + "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n", + "|**enable_early_stopping**|Flag to enble early termination if the score is not improving in the short term.|\\n\",\n", + "|**time_column_name**|The name of your time column.|\n", + "|**max_horizon**|The number of periods out you would like to predict past your training data. Periods are inferred from your data.|" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "import logging\n", + "from azureml.train.automl import AutoMLConfig\n", + "\n", + "#These models are blacklisted for tutorial purposes, remove this for real use cases. \n", + "blacklist_models = ['ExtremeRandomTrees', 'AutoArima', 'ElasticNet']\n", + "\n", + "automl_config = AutoMLConfig(\n", + " # Data Parameters\n", + " training_data=ds_train,\n", + " validation_data=ds_valid,\n", + " time_column_name='timestamp',\n", + " label_column_name='load',\n", + " # Forecasting Parameters\n", + " task='forecasting',\n", + " max_horizon=HORIZON,\n", + " primary_metric='normalized_root_mean_squared_error',\n", + " # AutoML Settings\n", + " enable_early_stopping=True,\n", + " blacklist_models=blacklist_models,\n", + " experiment_timeout_minutes=5,\n", + " verbosity=logging.ERROR,\n", + " path=project_folder,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Submit the job\n", + "\n", + "Call the submit method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. \n", + "\n", + "The optional `show_output=True` causes currently running iterations to print to the console." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on local machine\n", + "Parent Run ID: AutoML_43eb3d2d-b500-4383-91a1-c2e398742f31\n", + "\n", + "Current status: DatasetFeaturization. Beginning to featurize the dataset.\n", + "Current status: DatasetFeaturizationCompleted. Completed featurizing the dataset.\n", + "Current status: ModelSelection. Beginning model selection.\n", + "\n", + "****************************************************************************************************\n", + "ITERATION: The iteration being evaluated.\n", + "PIPELINE: A summary description of the pipeline being evaluated.\n", + "DURATION: Time taken for the current iteration.\n", + "METRIC: The result of computing score on the fitted pipeline.\n", + "BEST: The best observed score thus far.\n", + "****************************************************************************************************\n", + "\n", + " ITERATION PIPELINE DURATION METRIC BEST\n", + " 0 StandardScalerWrapper RandomForest 0:00:16 0.0521 0.0521\n", + " 1 StandardScalerWrapper LightGBM 0:00:14 0.0660 0.0521\n", + " 2 StandardScalerWrapper LassoLars 0:00:15 0.1518 0.0521\n", + " 3 MinMaxScaler DecisionTree 0:00:13 0.0719 0.0521\n", + " 4 MaxAbsScaler RandomForest 0:00:14 0.0473 0.0473\n", + " 5 MaxAbsScaler DecisionTree 0:00:14 0.0641 0.0473\n", + " 6 MinMaxScaler DecisionTree 0:00:14 0.0663 0.0473\n", + " 7 StandardScalerWrapper DecisionTree 0:00:14 0.0464 0.0464\n", + " 8 MaxAbsScaler DecisionTree 0:00:14 0.0637 0.0464\n", + " 9 StandardScalerWrapper LightGBM 0:00:14 0.0506 0.0464\n", + " 10 MinMaxScaler DecisionTree 0:00:14 0.0674 0.0464\n", + " 11 MaxAbsScaler SGD 0:00:13 0.1433 0.0464\n", + " 12 StandardScalerWrapper RandomForest 0:00:14 0.0504 0.0464\n", + " 13 MinMaxScaler DecisionTree 0:00:14 0.0502 0.0464\n", + " 14 MinMaxScaler DecisionTree 0:00:13 0.0691 0.0464\n", + " 15 SparseNormalizer RandomForest 0:00:24 0.0423 0.0423\n", + " 16 MaxAbsScaler LightGBM 0:00:15 0.0377 0.0377\n", + " 17 StandardScalerWrapper GradientBoosting 0:00:16 0.0480 0.0377\n", + " 18 MaxAbsScaler RandomForest 0:00:16 0.0709 0.0377\n", + " 19 MaxAbsScaler RandomForest 0:00:15 0.0734 0.0377\n", + " 20 VotingEnsemble 0:00:31 0.0359 0.0359\n", + " 21 StackEnsemble 0:00:38 0.0351 0.0351\n", + "Stopping criteria reached at iteration 21. Ending experiment.\n", + "CPU times: user 46.2 s, sys: 8.27 s, total: 54.5 s\n", + "Wall time: 6min 29s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "run = experiment.submit(automl_config, show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "The `run` object contains a link to the experiment in the AzureML Workspace." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
ExperimentIdTypeStatusDetails PageDocs Page
automl-forecasting-GEFCom2014AutoML_43eb3d2d-b500-4383-91a1-c2e398742f31automlCompletedLink to Azure Machine Learning studioLink to Documentation
" + ], + "text/plain": [ + "Run(Experiment: automl-forecasting-GEFCom2014,\n", + "Id: AutoML_43eb3d2d-b500-4383-91a1-c2e398742f31,\n", + "Type: automl,\n", + "Status: Completed)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3c4c46370ecd48888667a79cac737bcd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "_AutoMLWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'NOTSET', …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/automl-forecasting-GEFCom2014/runs/AutoML_43eb3d2d-b500-4383-91a1-c2e398742f31?wsid=/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourcegroups/AML2/workspaces/AML2\", \"run_id\": \"AutoML_43eb3d2d-b500-4383-91a1-c2e398742f31\", \"run_properties\": {\"run_id\": \"AutoML_43eb3d2d-b500-4383-91a1-c2e398742f31\", \"created_utc\": \"2019-12-08T05:39:24.117444Z\", \"properties\": {\"num_iterations\": \"1000\", \"training_type\": \"TrainFull\", \"acquisition_function\": \"EI\", \"primary_metric\": \"normalized_root_mean_squared_error\", \"train_split\": \"0\", \"MaxTimeSeconds\": \"0\", \"acquisition_parameter\": \"0\", \"num_cross_validation\": null, \"target\": \"local\", \"RawAMLSettingsString\": \"{'name': 'automl-forecasting-GEFCom2014', 'path': './project', 'subscription_id': '6fa1b60b-c4be-4966-a446-261a3ad62d42', 'resource_group': 'AML2', 'workspace_name': 'AML2', 'region': 'southcentralus', 'compute_target': 'local', 'spark_service': None, 'azure_service': None, 'iterations': 1000, 'primary_metric': 'normalized_root_mean_squared_error', 'task_type': 'regression', 'data_script': None, 'validation_size': 0.0, 'n_cross_validations': None, 'y_min': 1979.0, 'y_max': 5224.0, 'num_classes': None, 'featurization': 'off', 'preprocess': False, 'lag_length': 0, 'is_timeseries': True, 'max_cores_per_iteration': 1, 'max_concurrent_iterations': 1, 'iteration_timeout_minutes': None, 'mem_in_mb': None, 'enforce_time_on_windows': False, 'experiment_timeout_minutes': 5, 'experiment_exit_score': None, 'whitelist_models': None, 'blacklist_algos': ['ExtremeRandomTrees', 'AutoArima', 'ElasticNet', 'XGBoostRegressor', 'Prophet', 'KNN', 'SVM'], 'supported_models': ['XGBoostRegressor', 'KNN', 'TCNForecaster', 'LightGBM', 'AutoArima', 'Prophet', 'ElasticNet', 'SGD', 'LassoLars', 'GradientBoosting', 'ExtremeRandomTrees', 'FastLinearRegressor', 'TensorFlowLinearRegressor', 'TensorFlowDNN', 'RandomForest', 'OnlineGradientDescentRegressor', 'DecisionTree'], 'auto_blacklist': True, 'blacklist_samples_reached': True, 'exclude_nan_labels': True, 'verbosity': 40, 'debug_log': 'automl.log', 'show_warnings': False, 'model_explainability': False, 'service_url': None, 'sdk_url': None, 'sdk_packages': None, 'enable_onnx_compatible_models': False, 'enable_split_onnx_featurizer_estimator_models': False, 'vm_type': None, 'telemetry_verbosity': 'NOTSET', 'send_telemetry': False, 'enable_dnn': False, 'enable_feature_sweeping': False, 'time_column_name': 'timestamp', 'grain_column_names': None, 'drop_column_names': None, 'max_horizon': 1, 'dropna': False, 'overwrite_columns': True, 'transform_dictionary': {'min': '_automl_target_col', 'max': '_automl_target_col', 'mean': '_automl_target_col'}, 'window_size': None, 'country_or_region': None, 'lags': None, 'seasonality': -1, 'use_stl': None, 'short_series_handling': False, 'enable_early_stopping': True, 'early_stopping_n_iters': 10, 'metrics': None, 'enable_ensembling': True, 'enable_stack_ensembling': True, 'ensemble_iterations': 15, 'enable_tf': False, 'enable_cache': True, 'enable_subsampling': False, 'subsample_seed': None, 'enable_nimbusml': False, 'enable_streaming': False, 'label_column_name': 'load', 'weight_column_name': None, 'cost_mode': 0, 'metric_operation': 'minimize'}\", \"AMLSettingsJsonString\": \"{\\\"name\\\":\\\"automl-forecasting-GEFCom2014\\\",\\\"path\\\":\\\"./project\\\",\\\"subscription_id\\\":\\\"6fa1b60b-c4be-4966-a446-261a3ad62d42\\\",\\\"resource_group\\\":\\\"AML2\\\",\\\"workspace_name\\\":\\\"AML2\\\",\\\"region\\\":\\\"southcentralus\\\",\\\"compute_target\\\":\\\"local\\\",\\\"spark_service\\\":null,\\\"azure_service\\\":null,\\\"iterations\\\":1000,\\\"primary_metric\\\":\\\"normalized_root_mean_squared_error\\\",\\\"task_type\\\":\\\"regression\\\",\\\"data_script\\\":null,\\\"validation_size\\\":0.0,\\\"n_cross_validations\\\":null,\\\"y_min\\\":1979.0,\\\"y_max\\\":5224.0,\\\"num_classes\\\":null,\\\"featurization\\\":\\\"off\\\",\\\"preprocess\\\":false,\\\"lag_length\\\":0,\\\"is_timeseries\\\":true,\\\"max_cores_per_iteration\\\":1,\\\"max_concurrent_iterations\\\":1,\\\"iteration_timeout_minutes\\\":null,\\\"mem_in_mb\\\":null,\\\"enforce_time_on_windows\\\":false,\\\"experiment_timeout_minutes\\\":5,\\\"experiment_exit_score\\\":null,\\\"whitelist_models\\\":null,\\\"blacklist_algos\\\":[\\\"ExtremeRandomTrees\\\",\\\"AutoArima\\\",\\\"ElasticNet\\\",\\\"XGBoostRegressor\\\",\\\"Prophet\\\",\\\"KNN\\\",\\\"SVM\\\"],\\\"supported_models\\\":[\\\"XGBoostRegressor\\\",\\\"KNN\\\",\\\"TCNForecaster\\\",\\\"LightGBM\\\",\\\"AutoArima\\\",\\\"Prophet\\\",\\\"ElasticNet\\\",\\\"SGD\\\",\\\"LassoLars\\\",\\\"GradientBoosting\\\",\\\"ExtremeRandomTrees\\\",\\\"FastLinearRegressor\\\",\\\"TensorFlowLinearRegressor\\\",\\\"TensorFlowDNN\\\",\\\"RandomForest\\\",\\\"OnlineGradientDescentRegressor\\\",\\\"DecisionTree\\\"],\\\"auto_blacklist\\\":true,\\\"blacklist_samples_reached\\\":true,\\\"exclude_nan_labels\\\":true,\\\"verbosity\\\":40,\\\"debug_log\\\":\\\"automl.log\\\",\\\"show_warnings\\\":false,\\\"model_explainability\\\":false,\\\"service_url\\\":null,\\\"sdk_url\\\":null,\\\"sdk_packages\\\":null,\\\"enable_onnx_compatible_models\\\":false,\\\"enable_split_onnx_featurizer_estimator_models\\\":false,\\\"vm_type\\\":null,\\\"telemetry_verbosity\\\":\\\"NOTSET\\\",\\\"send_telemetry\\\":false,\\\"enable_dnn\\\":false,\\\"enable_feature_sweeping\\\":false,\\\"time_column_name\\\":\\\"timestamp\\\",\\\"grain_column_names\\\":null,\\\"drop_column_names\\\":null,\\\"max_horizon\\\":1,\\\"dropna\\\":false,\\\"overwrite_columns\\\":true,\\\"transform_dictionary\\\":{\\\"min\\\":\\\"_automl_target_col\\\",\\\"max\\\":\\\"_automl_target_col\\\",\\\"mean\\\":\\\"_automl_target_col\\\"},\\\"window_size\\\":null,\\\"country_or_region\\\":null,\\\"lags\\\":null,\\\"seasonality\\\":-1,\\\"use_stl\\\":null,\\\"short_series_handling\\\":false,\\\"enable_early_stopping\\\":true,\\\"early_stopping_n_iters\\\":10,\\\"metrics\\\":null,\\\"enable_ensembling\\\":true,\\\"enable_stack_ensembling\\\":true,\\\"ensemble_iterations\\\":15,\\\"enable_tf\\\":false,\\\"enable_cache\\\":true,\\\"enable_subsampling\\\":false,\\\"subsample_seed\\\":null,\\\"enable_nimbusml\\\":false,\\\"enable_streaming\\\":false,\\\"label_column_name\\\":\\\"load\\\",\\\"weight_column_name\\\":null,\\\"cost_mode\\\":0,\\\"metric_operation\\\":\\\"minimize\\\"}\", \"DataPrepJsonString\": null, \"EnableSubsampling\": \"False\", \"runTemplate\": \"AutoML\", \"azureml.runsource\": \"automl\", \"display_task_type\": \"forecasting\", \"dependencies_versions\": \"{\\\"azureml-widgets\\\": \\\"1.0.76\\\", \\\"azureml-train\\\": \\\"1.0.76\\\", \\\"azureml-train-restclients-hyperdrive\\\": \\\"1.0.76\\\", \\\"azureml-train-core\\\": \\\"1.0.76\\\", \\\"azureml-train-automl\\\": \\\"1.0.76\\\", \\\"azureml-train-automl-runtime\\\": \\\"1.0.76.1\\\", \\\"azureml-train-automl-client\\\": \\\"1.0.76\\\", \\\"azureml-telemetry\\\": \\\"1.0.76\\\", \\\"azureml-sdk\\\": \\\"1.0.76\\\", \\\"azureml-pipeline\\\": \\\"1.0.76\\\", \\\"azureml-pipeline-steps\\\": \\\"1.0.76\\\", \\\"azureml-pipeline-core\\\": \\\"1.0.76\\\", \\\"azureml-model-management-sdk\\\": \\\"1.0.1b6.post1\\\", \\\"azureml-interpret\\\": \\\"1.0.76\\\", \\\"azureml-explain-model\\\": \\\"1.0.76\\\", \\\"azureml-defaults\\\": \\\"1.0.76\\\", \\\"azureml-dataprep\\\": \\\"1.1.33\\\", \\\"azureml-dataprep-native\\\": \\\"13.1.0\\\", \\\"azureml-core\\\": \\\"1.0.76\\\", \\\"azureml-automl-runtime\\\": \\\"1.0.76.1\\\", \\\"azureml-automl-core\\\": \\\"1.0.76\\\"}\", \"ProblemInfoJsonString\": \"{\\\"dataset_num_categorical\\\": 0, \\\"is_sparse\\\": false, \\\"subsampling\\\": false, \\\"dataset_classes\\\": 2632, \\\"dataset_features\\\": 13, \\\"dataset_samples\\\": 23376, \\\"single_frequency_class_detected\\\": false}\", \"azureml.git.repository_uri\": \"git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git\", \"mlflow.source.git.repoURL\": \"git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git\", \"azureml.git.branch\": \"jacob/tf2\", \"mlflow.source.git.branch\": \"jacob/tf2\", \"azureml.git.commit\": \"85fc22b3271fb98c50abb390a18345008c70b77a\", \"mlflow.source.git.commit\": \"85fc22b3271fb98c50abb390a18345008c70b77a\", \"azureml.git.dirty\": \"False\"}, \"tags\": {\"model_explain_run\": \"best_run\", \"experiment_status\": \"ModelSelection\", \"experiment_status_descr\": \"Beginning model selection.\"}, \"end_time_utc\": \"2019-12-08T05:45:47.016683Z\", \"status\": \"Completed\", \"log_files\": {}, \"log_groups\": [], \"run_duration\": \"0:06:22\"}, \"child_runs\": [{\"run_id\": \"AutoML_43eb3d2d-b500-4383-91a1-c2e398742f31_0\", \"run_number\": 648, \"metric\": null, \"status\": \"Completed\", \"run_type\": null, \"training_percent\": \"100\", \"start_time\": \"2019-12-08T05:39:33.650362Z\", \"end_time\": \"2019-12-08T05:39:46.508894Z\", \"created_time\": \"2019-12-08T05:39:33.096621Z\", \"created_time_dt\": \"2019-12-08T05:39:33.096621Z\", \"duration\": \"0:00:13\", \"iteration\": \"0\", \"goal\": \"normalized_root_mean_squared_error_min\", \"run_name\": \"StandardScalerWrapper, RandomForest\", \"run_properties\": \"\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DroppedEngineeredFeatureCountRawFeatureNameTransformationsTypeDetected
0No2temp[MeanImputer, ImputationMarker]Numeric
1No11timestamp[DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer]DateTime
\n", + "
" + ], + "text/plain": [ + " Dropped EngineeredFeatureCount RawFeatureName \\\n", + "0 No 2 temp \n", + "1 No 11 timestamp \n", + "\n", + " Transformations \\\n", + "0 [MeanImputer, ImputationMarker] \n", + "1 [DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer] \n", + "\n", + " TypeDetected \n", + "0 Numeric \n", + "1 DateTime " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the featurization summary as a list of JSON\n", + "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n", + "# View the featurization summary as a pandas dataframe\n", + "pd.DataFrame.from_records(featurization_summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Forecasting\n", + "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Forecast Function\n", + "For forecasting, we will use the forecast function instead of the predict function. There are two reasons for this.\n", + "\n", + "We need to pass the recent values of the target variable y, whereas the scikit-compatible predict function only takes the non-target variables 'test'. In our case, the test data immediately follows the training data, and we fill the target variable with NaN. The NaN serves as a question mark for the forecaster to fill with the actuals. Using the forecast function will produce forecasts using the shortest possible forecast horizon. The last time at which a definite (non-NaN) value is seen is the forecast origin - the last time when the value of the target is known.\n", + "\n", + "Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# First, we remove the target values from the test set:\n", + "X_test = ds_test.copy()\n", + "y_test = X_test.pop('load').values\n", + "\n", + "# The forecast origin will be at the beginning of the first forecast period.\n", + "# (Which is the same time as the end of the last training period.)\n", + "y_query = np.empty_like(y_test)\n", + "y_query.fill(np.nan)\n", + "\n", + "# The featurized data, aligned to y, will also be returned.\n", + "# This contains the assumptions that were made in the forecast\n", + "# and helps align the forecast to the original data\n", + "y_predictions, X_trans = fitted_model.forecast(X_test, y_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "It is useful to look at how the inputs were transformed to create the predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
temptemp_WASNULLyearhalfquartermonthdayhouram_pmhour12wdayqdayweek_automl_target_col
timestamp_automl_dummy_grain_col
2014-11-01 00:00:00_automl_dummy_grain_col38.330201424111000532442538.87
2014-11-01 01:00:00_automl_dummy_grain_col37.330201424111101532442416.35
2014-11-01 02:00:00_automl_dummy_grain_col36.330201424111202532442437.24
2014-11-01 03:00:00_automl_dummy_grain_col36.330201424111303532442431.05
2014-11-01 04:00:00_automl_dummy_grain_col36.000201424111404532442491.47
\n", + "
" + ], + "text/plain": [ + " temp temp_WASNULL year half \\\n", + "timestamp _automl_dummy_grain_col \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 38.33 0 2014 2 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 37.33 0 2014 2 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 36.33 0 2014 2 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 36.33 0 2014 2 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 36.00 0 2014 2 \n", + "\n", + " quarter month day hour am_pm \\\n", + "timestamp _automl_dummy_grain_col \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 4 11 1 0 0 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 4 11 1 1 0 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 4 11 1 2 0 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 4 11 1 3 0 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 4 11 1 4 0 \n", + "\n", + " hour12 wday qday week \\\n", + "timestamp _automl_dummy_grain_col \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 0 5 32 44 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 1 5 32 44 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2 5 32 44 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 3 5 32 44 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 4 5 32 44 \n", + "\n", + " _automl_target_col \n", + "timestamp _automl_dummy_grain_col \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2538.87 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2416.35 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2437.24 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2431.05 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2491.47 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_trans.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Evaluate\n", + "To evaluate the accuracy of the forecast, we'll compare against the actual load values using the mean absolute percentage error (MAPE) metric." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Collect the target and predictions in a dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
actualtempprediction
2014-11-01 00:00:002514.0038.332538.87
2014-11-01 01:00:002434.0037.332416.35
2014-11-01 02:00:002390.0036.332437.24
2014-11-01 03:00:002382.0036.332431.05
2014-11-01 04:00:002419.0036.002491.47
\n", + "
" + ], + "text/plain": [ + " actual temp prediction\n", + "2014-11-01 00:00:00 2514.00 38.33 2538.87 \n", + "2014-11-01 01:00:00 2434.00 37.33 2416.35 \n", + "2014-11-01 02:00:00 2390.00 36.33 2437.24 \n", + "2014-11-01 03:00:00 2382.00 36.33 2431.05 \n", + "2014-11-01 04:00:00 2419.00 36.00 2491.47 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_df = ds_test.copy().drop('timestamp', axis=1)\n", + "eval_df['prediction'] = y_predictions\n", + "eval_df.rename(columns={'load':'actual'}, inplace=True)\n", + "eval_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Compute the mean absolute percentage error over all predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "def mape(predictions, actuals):\n", + " \"\"\"Mean absolute percentage error\"\"\"\n", + " return ((predictions - actuals).abs() / actuals).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE: 3.28%\n" + ] + } + ], + "source": [ + "print(\"MAPE: {:.2f}%\".format(100* mape(eval_df['prediction'], eval_df['actual'])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Plot the predictions vs the actuals for the first week of the test set" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "eval_df[:'2014-11-08'] \\\n", + " .plot(y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8))\n", + "plt.xlabel('timestamp', fontsize=12)\n", + "plt.ylabel('load', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Using lags and rolling window features\n", + "Now we will configure the target lags, that is the previous values of the target variables, meaning the prediction is no longer horizon-less. We therefore must still specify the max_horizon that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the max, min and sum features.\n", + "\n", + "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "slideshow": { + "slide_type": "notes" + } + }, + "outputs": [], + "source": [ + "# Instead of a separate validation set, we'll use cross_validation to train\n", + "ds_train2 = energy[:test_start_dt].copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "#These models are blacklisted for tutorial purposes, remove this for real use cases. \n", + "blacklist_models = ['ElasticNet', 'ExtremeRandomTrees', 'GradientBoosting', 'XGBoostRegressor',\n", + " 'ExtremeRandomTrees', 'AutoArima']\n", + "max_lag = 12\n", + "\n", + "automl_config = AutoMLConfig(\n", + " # Data Parameters\n", + " training_data=ds_train2,\n", + " #validation_data=ds_valid,\n", + " time_column_name='timestamp',\n", + " label_column_name='load',\n", + " # Parameters for lags and window\n", + " target_lags=max_lag,\n", + " target_rolling_window_size=6,\n", + " # Forecasting Parameters\n", + " task='forecasting',\n", + " max_horizon=HORIZON,\n", + " primary_metric='normalized_root_mean_squared_error',\n", + " # AutoML Settings\n", + " n_cross_validations=5,\n", + " enable_early_stopping=True,\n", + " blacklist_models=blacklist_models,\n", + " experiment_timeout_minutes=10,\n", + " verbosity=logging.ERROR,\n", + " path=project_folder,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Submit the job\n", + "We now start a new remote run, this time with lag and rolling window featurization. AutoML applies featurizations in the setup stage, prior to iterating over ML models. The full training set is featurized first, followed by featurization of each of the CV splits. Lag and rolling window features introduce additional complexity, so the run will take longer than in the previous example that lacked these featurizations." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running on local machine\n", + "Parent Run ID: AutoML_314e15e3-a29a-422c-8896-f603c4097aac\n", + "\n", + "Current status: DatasetFeaturization. Beginning to featurize the dataset.\n", + "Current status: DatasetFeaturizationCompleted. Completed featurizing the dataset.\n", + "Current status: DatasetCrossValidationSplit. Generating CV splits.\n", + "Current status: DatasetFeaturization. Beginning to featurize the CV split.\n", + "Current status: DatasetFeaturizationCompleted. Completed featurizing the CV split.\n", + "Current status: DatasetFeaturization. Beginning to featurize the CV split.\n", + "Current status: DatasetFeaturizationCompleted. Completed featurizing the CV split.\n", + "Current status: DatasetFeaturization. Beginning to featurize the CV split.\n", + "Current status: DatasetFeaturizationCompleted. Completed featurizing the CV split.\n", + "Current status: DatasetFeaturization. Beginning to featurize the CV split.\n", + "Current status: DatasetFeaturizationCompleted. Completed featurizing the CV split.\n", + "Current status: DatasetFeaturization. Beginning to featurize the CV split.\n", + "Current status: DatasetFeaturizationCompleted. Completed featurizing the CV split.\n", + "\n", + "****************************************************************************************************\n", + "DATA GUARDRAILS SUMMARY:\n", + "For more details, use API: run.get_guardrails()\n", + "\n", + "TYPE: Memory Issues Detection\n", + "STATUS: PASSED\n", + "DESCRIPTION: The selected horizon, lag and rolling window values were analyzed, and no potential memory issues were detected.\n", + "\n", + "****************************************************************************************************\n", + "Current status: ModelSelection. Beginning model selection.\n", + "\n", + "****************************************************************************************************\n", + "ITERATION: The iteration being evaluated.\n", + "PIPELINE: A summary description of the pipeline being evaluated.\n", + "DURATION: Time taken for the current iteration.\n", + "METRIC: The result of computing score on the fitted pipeline.\n", + "BEST: The best observed score thus far.\n", + "****************************************************************************************************\n", + "\n", + " ITERATION PIPELINE DURATION METRIC BEST\n", + " 0 StandardScalerWrapper RandomForest 0:00:24 0.0220 0.0220\n", + " 1 StandardScalerWrapper LightGBM 0:00:20 0.0570 0.0220\n", + " 2 StandardScalerWrapper LassoLars 0:00:20 0.0614 0.0220\n", + " 3 MinMaxScaler DecisionTree 0:00:20 0.0528 0.0220\n", + " 4 MaxAbsScaler RandomForest 0:00:22 0.0247 0.0220\n", + " 5 MaxAbsScaler DecisionTree 0:00:19 0.0406 0.0220\n", + " 6 MinMaxScaler DecisionTree 0:00:19 0.0567 0.0220\n", + " 7 StandardScalerWrapper DecisionTree 0:00:20 0.0257 0.0220\n", + " 8 MaxAbsScaler DecisionTree 0:00:21 0.0323 0.0220\n", + " 9 StandardScalerWrapper LightGBM 0:00:23 0.0313 0.0220\n", + " 10 MinMaxScaler DecisionTree 0:00:22 0.0550 0.0220\n", + " 11 MaxAbsScaler SGD 0:00:20 0.0934 0.0220\n", + " 12 StandardScalerWrapper RandomForest 0:00:23 0.0355 0.0220\n", + " 13 MinMaxScaler DecisionTree 0:00:21 0.0245 0.0220\n", + " 14 MinMaxScaler DecisionTree 0:00:20 0.0220 0.0220\n", + " 15 MaxAbsScaler LightGBM 0:00:24 0.0317 0.0220\n", + " 16 SparseNormalizer RandomForest 0:01:44 0.0305 0.0220\n", + " 17 MaxAbsScaler RandomForest 0:00:37 0.0435 0.0220\n", + " 18 MaxAbsScaler RandomForest 0:00:23 0.0479 0.0220\n", + " 19 MaxAbsScaler RandomForest 0:00:33 0.0381 0.0220\n", + " 20 SparseNormalizer DecisionTree 0:00:21 0.0356 0.0220\n", + " 21 StandardScalerWrapper RandomForest 0:00:31 0.0277 0.0220\n", + " 22 VotingEnsemble 0:00:37 0.0072 0.0072\n", + " 23 StackEnsemble 0:00:41 0.0096 0.0072\n", + "Stopping criteria reached at iteration 23. Ending experiment.\n", + "CPU times: user 1min 2s, sys: 9.87 s, total: 1min 12s\n", + "Wall time: 11min 49s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "run = experiment.submit(automl_config, show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "The `run` object contains a link to the experiment in the AzureML Workspace." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
ExperimentIdTypeStatusDetails PageDocs Page
automl-forecasting-GEFCom2014AutoML_314e15e3-a29a-422c-8896-f603c4097aacautomlCompletedLink to Azure Machine Learning studioLink to Documentation
" + ], + "text/plain": [ + "Run(Experiment: automl-forecasting-GEFCom2014,\n", + "Id: AutoML_314e15e3-a29a-422c-8896-f603c4097aac,\n", + "Type: automl,\n", + "Status: Completed)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Retrieve the Best Model\n", + "Below we select the best model from all the training iterations using the `get_output` method." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[('timeseriestransformer', TimeSeriesTransformer(logger=None,\n", + " pipeline_type=)),\n", + " ('prefittedsoftvotingregressor',\n", + " PreFittedSoftVotingRegressor(estimators=[('14', Pipeline(memory=None,\n", + " steps=[('minmaxscaler', MinMaxScaler(copy=True, feature_range=(0, 1))), ('decisiontreeregressor', DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=0.6,\n", + " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", + " min_impurity_...True,\n", + " subsample=0.65, subsample_for_bin=200000, subsample_freq=1,\n", + " verbose=-1))]))],\n", + " flatten_transform=None,\n", + " weights=[0.4666666666666667, 0.06666666666666667, 0.13333333333333333, 0.3333333333333333]))]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "best_run, fitted_model = run.get_output()\n", + "fitted_model.steps" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Analysis\n", + "Below we take a look at the model produced" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "### Featurization\n", + "You can access the engineered feature names generated in time-series featurization" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['horizon_origin',\n", + " 'temp',\n", + " 'temp_WASNULL',\n", + " '_automl_target_col_lag12H',\n", + " '_automl_target_col_min_window6H',\n", + " '_automl_target_col_max_window6H',\n", + " '_automl_target_col_mean_window6H',\n", + " 'year',\n", + " 'half',\n", + " 'quarter',\n", + " 'month',\n", + " 'day',\n", + " 'hour',\n", + " 'am_pm',\n", + " 'hour12',\n", + " 'wday',\n", + " 'qday',\n", + " 'week']" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fitted_model.named_steps['timeseriestransformer'].get_engineered_feature_names()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### View featurization summary\n", + "You can also see what featurization steps were performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:\n", + "\n", + "- Raw feature name\n", + "- Number of engineered features formed out of this raw feature\n", + "- Type detected\n", + "- If feature was dropped\n", + "- List of feature transformations for the raw feature" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DroppedEngineeredFeatureCountRawFeatureNameTransformationsTypeDetected
0No12timestamp[MaxHorizonFeaturizer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer]DateTime
1No2temp[MeanImputer, ImputationMarker]Numeric
2No4_automl_target_col[Lag, minRollingWindow, maxRollingWindow, meanRollingWindow]Numeric
\n", + "
" + ], + "text/plain": [ + " Dropped EngineeredFeatureCount RawFeatureName \\\n", + "0 No 12 timestamp \n", + "1 No 2 temp \n", + "2 No 4 _automl_target_col \n", + "\n", + " Transformations \\\n", + "0 [MaxHorizonFeaturizer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer] \n", + "1 [MeanImputer, ImputationMarker] \n", + "2 [Lag, minRollingWindow, maxRollingWindow, meanRollingWindow] \n", + "\n", + " TypeDetected \n", + "0 DateTime \n", + "1 Numeric \n", + "2 Numeric " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the featurization summary as a list of JSON\n", + "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n", + "# View the featurization summary as a pandas dataframe\n", + "pd.DataFrame.from_records(featurization_summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Forecasting\n", + "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Forecast Function\n", + "For forecasting, we will use the forecast function instead of the predict function. There are two reasons for this.\n", + "\n", + "We need to pass the recent values of the target variable y, whereas the scikit-compatible predict function only takes the non-target variables 'test'. In our case, the test data immediately follows the training data, and we fill the target variable with NaN. The NaN serves as a question mark for the forecaster to fill with the actuals. Using the forecast function will produce forecasts using the shortest possible forecast horizon. The last time at which a definite (non-NaN) value is seen is the forecast origin - the last time when the value of the target is known.\n", + "\n", + "Since this model is needs the most recent 12 lag values to make predictions and we set the HORIZON to 1, we can only predict the 13th value in a single call, not the entire test set." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# First, we remove the target values from the test set:\n", + "X_test = ds_test.iloc[:13].copy()\n", + "y_test = X_test.pop('load').values\n", + "\n", + "# The forecast origin will be at the beginning of the first forecast period.\n", + "# (Which is the same time as the end of the last training period.)\n", + "y_query = y_test.copy()\n", + "y_query[-1] = np.nan\n", + "\n", + "# The featurized data, aligned to y, will also be returned.\n", + "# This contains the assumptions that were made in the forecast\n", + "# and helps align the forecast to the original data\n", + "y_predictions, X_trans = fitted_model.forecast(X_test, y_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "It is useful to look at how the inputs were transformed to create the predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
horizon_origintemptemp_WASNULL_automl_target_col_lag12H_automl_target_col_min_window6H_automl_target_col_max_window6H_automl_target_col_mean_window6Hyearhalfquartermonthdayhouram_pmhour12wdayqdayweek_automl_target_col
timestamp_automl_dummy_grain_colorigin
2014-11-01 00:00:00_automl_dummy_grain_col2014-10-31 23:00:00138.3303369.002664.003466.003142.50201424111000532442514.00
2014-11-01 01:00:00_automl_dummy_grain_col2014-11-01 00:00:00137.3303342.002514.003374.002983.83201424111101532442434.00
2014-11-01 02:00:00_automl_dummy_grain_col2014-11-01 01:00:00136.3303287.002434.003315.002827.17201424111202532442390.00
2014-11-01 03:00:00_automl_dummy_grain_col2014-11-01 02:00:00136.3303293.002390.003142.002673.00201424111303532442382.00
2014-11-01 04:00:00_automl_dummy_grain_col2014-11-01 03:00:00136.0003358.002382.002894.002546.33201424111404532442419.00
2014-11-01 05:00:00_automl_dummy_grain_col2014-11-01 04:00:00137.3303437.002382.002664.002467.17201424111505532442520.00
2014-11-01 06:00:00_automl_dummy_grain_col2014-11-01 05:00:00137.3303466.002382.002520.002443.17201424111606532442714.00
2014-11-01 07:00:00_automl_dummy_grain_col2014-11-01 06:00:00138.3303374.002382.002714.002476.50201424111707532442970.00
2014-11-01 08:00:00_automl_dummy_grain_col2014-11-01 07:00:00139.0003315.002382.002970.002565.83201424111808532443189.00
2014-11-01 09:00:00_automl_dummy_grain_col2014-11-01 08:00:00141.3303142.002382.003189.002699.00201424111909532443356.00
2014-11-01 10:00:00_automl_dummy_grain_col2014-11-01 09:00:00143.0002894.002419.003356.002861.3320142411110010532443436.00
2014-11-01 11:00:00_automl_dummy_grain_col2014-11-01 10:00:00142.0002664.002520.003436.003030.8320142411111011532443464.00
2014-11-01 12:00:00_automl_dummy_grain_col2014-11-01 11:00:00142.3302514.002714.003464.003188.1720142411112112532443455.25
\n", + "
" + ], + "text/plain": [ + " horizon_origin \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 1 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 1 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 1 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 1 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 1 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 1 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 1 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 1 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 1 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 1 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 1 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 1 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 1 \n", + "\n", + " temp \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 38.33 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 37.33 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 36.33 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 36.33 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 36.00 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 37.33 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 37.33 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 38.33 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 39.00 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 41.33 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 43.00 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 42.00 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 42.33 \n", + "\n", + " temp_WASNULL \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 0 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 0 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 0 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 0 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 0 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 0 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 0 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 0 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 0 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 0 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 0 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 0 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 0 \n", + "\n", + " _automl_target_col_lag12H \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 3369.00 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 3342.00 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 3287.00 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 3293.00 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 3358.00 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 3437.00 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 3466.00 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 3374.00 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 3315.00 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 3142.00 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 2894.00 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 2664.00 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 2514.00 \n", + "\n", + " _automl_target_col_min_window6H \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 2664.00 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 2514.00 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 2434.00 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 2390.00 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 2382.00 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 2382.00 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 2382.00 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 2382.00 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 2382.00 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 2382.00 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 2419.00 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 2520.00 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 2714.00 \n", + "\n", + " _automl_target_col_max_window6H \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 3466.00 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 3374.00 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 3315.00 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 3142.00 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 2894.00 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 2664.00 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 2520.00 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 2714.00 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 2970.00 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 3189.00 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 3356.00 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 3436.00 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 3464.00 \n", + "\n", + " _automl_target_col_mean_window6H \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 3142.50 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 2983.83 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 2827.17 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 2673.00 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 2546.33 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 2467.17 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 2443.17 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 2476.50 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 2565.83 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 2699.00 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 2861.33 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 3030.83 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 3188.17 \n", + "\n", + " year half \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 2014 2 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 2014 2 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 2014 2 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 2014 2 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 2014 2 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 2014 2 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 2014 2 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 2014 2 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 2014 2 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 2014 2 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 2014 2 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 2014 2 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 2014 2 \n", + "\n", + " quarter \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 4 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 4 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 4 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 4 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 4 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 4 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 4 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 4 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 4 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 4 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 4 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 4 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 4 \n", + "\n", + " month day \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 11 1 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 11 1 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 11 1 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 11 1 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 11 1 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 11 1 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 11 1 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 11 1 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 11 1 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 11 1 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 11 1 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 11 1 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 11 1 \n", + "\n", + " hour am_pm \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 0 0 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 1 0 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 2 0 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 3 0 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 4 0 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 5 0 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 6 0 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 7 0 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 8 0 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 9 0 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 10 0 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 11 0 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 12 1 \n", + "\n", + " hour12 wday \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 0 5 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 1 5 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 2 5 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 3 5 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 4 5 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 5 5 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 6 5 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 7 5 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 8 5 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 9 5 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 10 5 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 11 5 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 12 5 \n", + "\n", + " qday week \\\n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 32 44 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 32 44 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 32 44 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 32 44 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 32 44 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 32 44 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 32 44 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 32 44 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 32 44 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 32 44 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 32 44 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 32 44 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 32 44 \n", + "\n", + " _automl_target_col \n", + "timestamp _automl_dummy_grain_col origin \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2014-10-31 23:00:00 2514.00 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2014-11-01 00:00:00 2434.00 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2014-11-01 01:00:00 2390.00 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2014-11-01 02:00:00 2382.00 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2014-11-01 03:00:00 2419.00 \n", + "2014-11-01 05:00:00 _automl_dummy_grain_col 2014-11-01 04:00:00 2520.00 \n", + "2014-11-01 06:00:00 _automl_dummy_grain_col 2014-11-01 05:00:00 2714.00 \n", + "2014-11-01 07:00:00 _automl_dummy_grain_col 2014-11-01 06:00:00 2970.00 \n", + "2014-11-01 08:00:00 _automl_dummy_grain_col 2014-11-01 07:00:00 3189.00 \n", + "2014-11-01 09:00:00 _automl_dummy_grain_col 2014-11-01 08:00:00 3356.00 \n", + "2014-11-01 10:00:00 _automl_dummy_grain_col 2014-11-01 09:00:00 3436.00 \n", + "2014-11-01 11:00:00 _automl_dummy_grain_col 2014-11-01 10:00:00 3464.00 \n", + "2014-11-01 12:00:00 _automl_dummy_grain_col 2014-11-01 11:00:00 3455.25 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_trans" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Rolling Forecast\n", + "In order to predict the test set, we need to do a ***rolling forecast***. For each value we want to forecast, we need to send to the model a window of data covering at least as many historical values as the maximum lag of the target used (12 in this case). The AutoML model will cache the tail of the training sequence to allow lookback for making predictions for the period immediately following the training window, so for the first few predictions we can get away with providing fewer than 12 observations in the call to `predict`.\n", + "\n", + "This is not the most efficient manner to do rolling forecasts, but allows the model to be stateless. Making predictions for the test set takes an order of magnitude longer than for the simple regression model." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "from common.progress_bar import update_progress" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Progress: [####################] 100.0%\n", + "CPU times: user 4min 51s, sys: 6.4 s, total: 4min 57s\n", + "Wall time: 4min 53s\n" + ] + } + ], + "source": [ + "%%time\n", + "predict = np.empty(0)\n", + "# for ix in range(max_lag, ds_test.shape[0]):\n", + "for ix in range(0, ds_test.shape[0]):\n", + " # First, we remove the target values from the test set:\n", + " X_test = ds_test.iloc[max(0, ix-max_lag):ix+1].copy()\n", + " y_test = X_test.pop('load').values\n", + " # The forecast origin will be the second last item (forecast 1 forward)\n", + " y_query = y_test.copy()\n", + " y_query[-1] = np.nan\n", + " # predict\n", + " y_predictions, X_trans = fitted_model.forecast(X_test, y_query)\n", + " # save\n", + " predict = np.append(predict, y_predictions[-1])\n", + " # Progress update\n", + " if not ix%10:\n", + " update_progress(ix/ds_test.shape[0])\n", + "update_progress(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Evaluate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Collect the target and predictions in a dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
actualtempprediction
2014-11-01 00:00:002514.0038.332554.32
2014-11-01 01:00:002434.0037.332450.95
2014-11-01 02:00:002390.0036.332420.73
2014-11-01 03:00:002382.0036.332382.93
2014-11-01 04:00:002419.0036.002386.18
2014-11-01 05:00:002520.0037.332545.12
2014-11-01 06:00:002714.0037.332750.57
2014-11-01 07:00:002970.0038.332908.37
2014-11-01 08:00:003189.0039.003118.41
2014-11-01 09:00:003356.0041.333280.78
2014-11-01 10:00:003436.0043.003318.37
2014-11-01 11:00:003464.0042.003427.60
2014-11-01 12:00:003439.0042.333455.25
\n", + "
" + ], + "text/plain": [ + " actual temp prediction\n", + "2014-11-01 00:00:00 2514.00 38.33 2554.32 \n", + "2014-11-01 01:00:00 2434.00 37.33 2450.95 \n", + "2014-11-01 02:00:00 2390.00 36.33 2420.73 \n", + "2014-11-01 03:00:00 2382.00 36.33 2382.93 \n", + "2014-11-01 04:00:00 2419.00 36.00 2386.18 \n", + "2014-11-01 05:00:00 2520.00 37.33 2545.12 \n", + "2014-11-01 06:00:00 2714.00 37.33 2750.57 \n", + "2014-11-01 07:00:00 2970.00 38.33 2908.37 \n", + "2014-11-01 08:00:00 3189.00 39.00 3118.41 \n", + "2014-11-01 09:00:00 3356.00 41.33 3280.78 \n", + "2014-11-01 10:00:00 3436.00 43.00 3318.37 \n", + "2014-11-01 11:00:00 3464.00 42.00 3427.60 \n", + "2014-11-01 12:00:00 3439.00 42.33 3455.25 " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_df = ds_test.copy().drop('timestamp', axis=1)\n", + "eval_df['prediction'] = predict\n", + "eval_df.rename(columns={'load':'actual'}, inplace=True)\n", + "eval_df.head(max_lag+1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Compute the mean absolute percentage error over all predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE: 2.61%\n" + ] + } + ], + "source": [ + "print(\"MAPE: {:.2f}%\".format(100* mape(eval_df['prediction'], eval_df['actual'])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Plot the predictions vs the actuals for the first week of the test set" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "eval_df[:'2014-11-08'] \\\n", + " .plot(y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8))\n", + "plt.xlabel('timestamp', fontsize=12)\n", + "plt.ylabel('load', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "dftf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/automl/2_AzureAutoML_remote.ipynb b/automl/2_AzureAutoML_remote.ipynb new file mode 100644 index 0000000..9851bd5 --- /dev/null +++ b/automl/2_AzureAutoML_remote.ipynb @@ -0,0 +1,1552 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# AutoML model\n", + "\n", + "In this notebook, we demonstrate how to:\n", + "- prepare time series data for training a forecasting model using an automated ML model builder\n", + "- Creating an Experiment using an existing Workspace\n", + "- Configure AutoML using 'AutoMLConfig'\n", + "- Train the model on local compute \n", + "- Explore the engineered features and results\n", + "- Configuration and run AutoML for a time-series model with lag and rolling window features\n", + "- Run and explore the forecast\n", + "- evaluate the model on a test dataset\n", + "\n", + "The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load and temperature data only.\n", + "\n", + "This notebook is based on the energy forecasting notebook provided [here](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb).\n", + "\n", + "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Imports\n", + "We start with the usual imports and settings" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('..')\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import pandas as pd\n", + "pd.options.display.float_format = '{:,.2f}'.format\n", + "\n", + "import numpy as np\n", + "np.set_printoptions(precision=2)\n", + "\n", + "# Squash warning messages for cleaner output in the notebook\n", + "import warnings\n", + "warnings.showwarning = lambda *args, **kwargs: None\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Data\n", + "Load the data from csv into a Pandas dataframe. Make sure to first complete the [0_data_setup](../0_data_setup.ipynb) notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
loadtemp
2012-01-01 00:00:002,698.0032.00
2012-01-01 01:00:002,558.0032.67
2012-01-01 02:00:002,444.0030.00
2012-01-01 03:00:002,402.0031.00
2012-01-01 04:00:002,403.0032.00
\n", + "
" + ], + "text/plain": [ + " load temp\n", + "2012-01-01 00:00:00 2,698.00 32.00\n", + "2012-01-01 01:00:00 2,558.00 32.67\n", + "2012-01-01 02:00:00 2,444.00 30.00\n", + "2012-01-01 03:00:00 2,402.00 31.00\n", + "2012-01-01 04:00:00 2,403.00 32.00" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "file_name = os.path.join('../data', 'energy.parquet')\n", + "energy = pd.read_parquet(file_name)\n", + "energy.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Create train, validation and test sets\n", + "\n", + "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n", + "\n", + "We will allocate data as follows:\n", + "* November 1, 2014 to December 31, 2014: **test** set. \n", + "* September 1, 2014 to October 31, 2014: **validation** set. \n", + "* Everything up to August 31, 2014: **training** set." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "valid_start_dt = '2014-08-31 23:59:59'\n", + "test_start_dt = '2014-10-31 23:59:59'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Data preparation - training set\n", + "\n", + "AutoML takes care of a lot of the details. We simply need to prepare a Pandas DataFrame for each of Train, Test, and Validation.\n", + "\n", + "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "HORIZON = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "Our data preparation for the training set will involve the following steps:\n", + "\n", + "1. Create a time column from the index\n", + "2. Filter the original dataset to include only that time period reserved for the training set\n", + "3. Drop rown with missing values " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### 1. Add time column" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "energy['timestamp'] = energy.index" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "### 2. Filter the original dataset to include only that time period reserved for each set\n", + "Create training, test and validations sets" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "ds_train = energy[:valid_start_dt].copy()\n", + "ds_valid = energy[valid_start_dt:test_start_dt].copy()\n", + "ds_test = energy[test_start_dt:].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Verify that the series are continuous." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " load temp timestamp\n", + "2014-08-31 19:00:00 3,969.00 74.67 2014-08-31 19:00:00\n", + "2014-08-31 20:00:00 3,869.00 74.00 2014-08-31 20:00:00\n", + "2014-08-31 21:00:00 3,643.00 73.00 2014-08-31 21:00:00\n", + "2014-08-31 22:00:00 3,365.00 72.00 2014-08-31 22:00:00\n", + "2014-08-31 23:00:00 3,097.00 71.33 2014-08-31 23:00:00\n", + " load temp timestamp\n", + "2014-09-01 00:00:00 2,886.00 71.00 2014-09-01 00:00:00\n", + "2014-09-01 01:00:00 2,768.00 70.00 2014-09-01 01:00:00\n", + "2014-09-01 02:00:00 2,699.00 69.33 2014-09-01 02:00:00\n", + "2014-09-01 03:00:00 2,681.00 68.33 2014-09-01 03:00:00\n", + "2014-09-01 04:00:00 2,690.00 68.33 2014-09-01 04:00:00\n" + ] + } + ], + "source": [ + "print(ds_train.tail())\n", + "print(ds_valid.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### 3. Discard any samples with missing values\n", + "We will discard these." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "ds_train.dropna(how=\"any\", inplace=True)\n", + "ds_valid.dropna(how=\"any\", inplace=True)\n", + "ds_test.dropna(how=\"any\", inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "We now have data of shape:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train data shape: (23376, 3)\n", + "Valid data shape: (1464, 3)\n", + "Test data shape: (1464, 3)\n" + ] + } + ], + "source": [ + "print('Train data shape:', ds_train.shape)\n", + "print('Valid data shape:', ds_valid.shape)\n", + "print('Test data shape:', ds_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Set Up AzureML Workspace" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "source": [ + "As part of the setup you should have access to an Azure ML Workspace. For Automated ML you will need to create an Experiment object, which is a named object in a Workspace used to run experiments." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SDK version1.0.76
WorkspaceAML2
Resource GroupAML2
Locationsouthcentralus
Experiment Nameautoml-forecasting-GEFCom2014
Project Folder./project
\n", + "
" + ], + "text/plain": [ + " \n", + "SDK version 1.0.76 \n", + "Workspace AML2 \n", + "Resource Group AML2 \n", + "Location southcentralus \n", + "Experiment Name automl-forecasting-GEFCom2014\n", + "Project Folder ./project " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import azureml.core\n", + "from azureml.core import Experiment, Workspace\n", + "\n", + "assert os.path.exists('config.json'), 'Download `config.json` from the Auzre Portal and place in this folder'\n", + "ws = Workspace.from_config()\n", + "\n", + "# Define a project folder where artifacts will be stored\n", + "project_folder = './project'\n", + "os.makedirs(project_folder, exist_ok=True)\n", + "\n", + "# choose a name for the run history container in the workspace\n", + "experiment_name = 'automl-forecasting-GEFCom2014'\n", + "experiment = Experiment(ws, experiment_name)\n", + "\n", + "output = {}\n", + "output['SDK version'] = azureml.core.VERSION\n", + "output['Workspace'] = ws.name\n", + "output['Resource Group'] = ws.resource_group\n", + "output['Location'] = ws.location\n", + "output['Experiment Name'] = experiment_name\n", + "output['Project Folder'] = project_folder\n", + "pd.set_option('display.max_colwidth', -1)\n", + "outputDf = pd.DataFrame(data = output, index = [''])\n", + "outputDf.T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create/Attach Remote Compute\n", + "Create the AmlCompute target in your workspace if it doesn't already exist.\n", + "\n", + "You can also configure several advanced properties when you create Azure Machine Learning Compute. The properties allow you to create a persistent cluster of fixed size, or within an existing Azure Virtual Network in your subscription. See the [AmlCompute class](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcompute?view=azure-ml-py) for details." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found existing cluster, using it.\n" + ] + } + ], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your CPU cluster\n", + "cluster_name = \"cpucluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " aml_compute = ComputeTarget(workspace=ws, name=cluster_name)\n", + " print('Found existing cluster, using it.')\n", + "except ComputeTargetException:\n", + " compute_config = AmlCompute.provisioning_configuration(\n", + " vm_size='STANDARD_D2_V2',\n", + " # for GPU, use \"STANDARD_NC6\"\n", + " # vm_priority = 'lowpriority', # optional\n", + " max_nodes=6\n", + " )\n", + " aml_compute = ComputeTarget.create(ws, cluster_name, compute_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Wait for compute to spin up\n", + "Can poll for a minimum number of nodes and for a specific timeout.\n", + "If no min_node_count is provided, it will use the scale settings for the cluster.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Succeeded\n", + "AmlCompute wait for completion finished\n", + "Minimum number of nodes requested have been provisioned\n" + ] + } + ], + "source": [ + "aml_compute.wait_for_completion(\n", + " show_output=True, \n", + " min_node_count=None, \n", + " timeout_in_minutes=10\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data for Remote Compute\n", + "We have to make the datasets available to the remote compute nodes. Easiest is to upload to the default datastore associated with the AML Workspace" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Uploading an estimated of 2 files\n", + "Uploading ../data/ds_train.csv\n", + "Uploading ../data/ds_valid.csv\n", + "Uploaded ../data/ds_valid.csv, 1 files out of an estimated total of 2\n", + "Uploaded ../data/ds_train.csv, 2 files out of an estimated total of 2\n", + "Uploaded 2 files\n" + ] + } + ], + "source": [ + "from azureml.core.dataset import Dataset\n", + "from azureml.data.data_reference import DataReference\n", + "\n", + "# First save our training sets as CSV files\n", + "data_dir = os.path.join('..', 'data')\n", + "ds_train.to_csv(os.path.join(data_dir, 'ds_train.csv'), index=False)\n", + "ds_valid.to_csv(os.path.join(data_dir, 'ds_valid.csv'), index=False)\n", + "\n", + "# Upload to AML DataStore\n", + "ds = ws.get_default_datastore()\n", + "files = [os.path.join('..', 'data', x) for x in ['ds_train.csv', 'ds_valid.csv']]\n", + "ds.upload_files(files, target_path='GEFCom2014', overwrite=True, show_progress=True)\n", + "\n", + "# Create Data References\n", + "train_data_ref = ds.path('GEFCom2014/ds_train.csv')\n", + "valid_data_ref = ds.path('GEFCom2014/ds_valid.csv')\n", + "\n", + "# Define as AML Datasets\n", + "amlds_train = Dataset.Tabular.from_delimited_files(train_data_ref)\n", + "amlds_valid = Dataset.Tabular.from_delimited_files(valid_data_ref)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Train\n", + "\n", + "Instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. We can provide extra configurations within 'automl_settings', for this forecasting task we add the name of the time column and the maximum forecast horizon. See [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py) for more options.\n", + "\n", + "|Property|Description|\n", + "|-|-|\n", + "|**task**|forecasting|\n", + "|**primary_metric**|This is the metric that you want to optimize.
Forecasting supports the following primary metrics
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error|\n", + "|**blacklist_models**|Models in blacklist won't be used by AutoML. All supported models can be found at [here](https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.constants.supportedmodels.regression?view=azure-ml-py).|\n", + "|**experiment_timeout_minutes**|Maximum amount of time in minutes that the experiment take before it terminates.|\n", + "|**training_data**|The training data to be used within the experiment.|\n", + "|**label_column_name**|The name of the label column.|\n", + "|**compute_target**|The remote compute for training.|\n", + "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n", + "|**enable_early_stopping**|Flag to enble early termination if the score is not improving in the short term.|\\n\",\n", + "|**time_column_name**|The name of your time column.|\n", + "|**max_horizon**|The number of periods out you would like to predict past your training data. Periods are inferred from your data.|" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [], + "source": [ + "import logging\n", + "from azureml.train.automl import AutoMLConfig\n", + "\n", + "#These models are blacklisted for tutorial purposes, remove this for real use cases. \n", + "blacklist_models = ['ExtremeRandomTrees', 'AutoArima', 'ElasticNet']\n", + "\n", + "automl_config = AutoMLConfig(\n", + " # AzureML Settings\n", + " compute_target=aml_compute,\n", + " max_concurrent_iterations=6,\n", + " max_cores_per_iteration=1,\n", + " # Data Parameters\n", + " training_data=amlds_train,\n", + " validation_data=amlds_valid,\n", + " time_column_name='timestamp',\n", + " label_column_name='load',\n", + " # Forecasting Parameters\n", + " task='forecasting',\n", + " max_horizon=HORIZON,\n", + " primary_metric='normalized_root_mean_squared_error',\n", + " # AutoML Settings\n", + " enable_early_stopping=True,\n", + " blacklist_models=blacklist_models,\n", + " experiment_timeout_minutes=10,\n", + " verbosity=logging.ERROR,\n", + " path=project_folder,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Submit the job\n", + "\n", + "Call the submit method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. \n", + "\n", + "The optional `show_output=True` causes currently running iterations to print to the console." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "run = experiment.submit(automl_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "The `run` object contains a link to the experiment in the AzureML Workspace." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
ExperimentIdTypeStatusDetails PageDocs Page
automl-forecasting-GEFCom2014AutoML_03f67a3a-64c1-40ea-bb5b-e8ffba18bb3bautomlStartingLink to Azure Machine Learning studioLink to Documentation
" + ], + "text/plain": [ + "Run(Experiment: automl-forecasting-GEFCom2014,\n", + "Id: AutoML_03f67a3a-64c1-40ea-bb5b-e8ffba18bb3b,\n", + "Type: automl,\n", + "Status: Starting)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wait for run to complete" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6a7c97a3910047489187540ba4713da9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "_AutoMLWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'NOTSET', …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/aml.mini.widget.v1": "{\"status\": \"Preparing\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/automl-forecasting-GEFCom2014/runs/AutoML_03f67a3a-64c1-40ea-bb5b-e8ffba18bb3b?wsid=/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourcegroups/AML2/workspaces/AML2\", \"run_id\": \"AutoML_03f67a3a-64c1-40ea-bb5b-e8ffba18bb3b\", \"run_properties\": {\"run_id\": \"AutoML_03f67a3a-64c1-40ea-bb5b-e8ffba18bb3b\", \"created_utc\": \"2019-12-08T05:37:12.357979Z\", \"properties\": {\"num_iterations\": \"1000\", \"training_type\": \"TrainFull\", \"acquisition_function\": \"EI\", \"primary_metric\": \"normalized_root_mean_squared_error\", \"train_split\": \"0\", \"MaxTimeSeconds\": \"0\", \"acquisition_parameter\": \"0\", \"num_cross_validation\": null, \"target\": \"cpucluster\", \"RawAMLSettingsString\": \"{'name': 'automl-forecasting-GEFCom2014', 'path': './project', 'subscription_id': '6fa1b60b-c4be-4966-a446-261a3ad62d42', 'resource_group': 'AML2', 'workspace_name': 'AML2', 'region': 'southcentralus', 'compute_target': 'cpucluster', 'spark_service': None, 'azure_service': None, 'iterations': 1000, 'primary_metric': 'normalized_root_mean_squared_error', 'task_type': 'regression', 'data_script': None, 'validation_size': 0.0, 'n_cross_validations': None, 'y_min': None, 'y_max': None, 'num_classes': None, 'featurization': 'off', 'preprocess': False, 'lag_length': 0, 'is_timeseries': True, 'max_cores_per_iteration': 1, 'max_concurrent_iterations': 6, 'iteration_timeout_minutes': None, 'mem_in_mb': None, 'enforce_time_on_windows': False, 'experiment_timeout_minutes': 10, 'experiment_exit_score': None, 'whitelist_models': None, 'blacklist_algos': ['ExtremeRandomTrees', 'AutoArima', 'ElasticNet', 'XGBoostRegressor', 'Prophet'], 'supported_models': ['TensorFlowLinearRegressor', 'TCNForecaster', 'XGBoostRegressor', 'LightGBM', 'RandomForest', 'GradientBoosting', 'TensorFlowDNN', 'SGD', 'LassoLars', 'ExtremeRandomTrees', 'OnlineGradientDescentRegressor', 'DecisionTree', 'AutoArima', 'KNN', 'ElasticNet', 'Prophet', 'FastLinearRegressor'], 'auto_blacklist': True, 'blacklist_samples_reached': False, 'exclude_nan_labels': True, 'verbosity': 40, 'debug_log': 'azureml_automl.log', 'show_warnings': False, 'model_explainability': False, 'service_url': None, 'sdk_url': None, 'sdk_packages': None, 'enable_onnx_compatible_models': False, 'enable_split_onnx_featurizer_estimator_models': False, 'vm_type': 'STANDARD_D2_V2', 'telemetry_verbosity': 'NOTSET', 'send_telemetry': False, 'enable_dnn': False, 'enable_feature_sweeping': False, 'time_column_name': 'timestamp', 'grain_column_names': None, 'drop_column_names': None, 'max_horizon': 1, 'dropna': False, 'overwrite_columns': True, 'transform_dictionary': {'min': '_automl_target_col', 'max': '_automl_target_col', 'mean': '_automl_target_col'}, 'window_size': None, 'country_or_region': None, 'lags': None, 'seasonality': -1, 'use_stl': None, 'short_series_handling': False, 'enable_early_stopping': True, 'early_stopping_n_iters': 10, 'metrics': None, 'enable_ensembling': True, 'enable_stack_ensembling': True, 'ensemble_iterations': 15, 'enable_tf': False, 'enable_cache': True, 'enable_subsampling': False, 'subsample_seed': None, 'enable_nimbusml': False, 'enable_streaming': False, 'label_column_name': 'load', 'weight_column_name': None, 'cost_mode': 0, 'metric_operation': 'minimize'}\", \"AMLSettingsJsonString\": \"{\\\"name\\\":\\\"automl-forecasting-GEFCom2014\\\",\\\"path\\\":\\\"./project\\\",\\\"subscription_id\\\":\\\"6fa1b60b-c4be-4966-a446-261a3ad62d42\\\",\\\"resource_group\\\":\\\"AML2\\\",\\\"workspace_name\\\":\\\"AML2\\\",\\\"region\\\":\\\"southcentralus\\\",\\\"compute_target\\\":\\\"cpucluster\\\",\\\"spark_service\\\":null,\\\"azure_service\\\":null,\\\"iterations\\\":1000,\\\"primary_metric\\\":\\\"normalized_root_mean_squared_error\\\",\\\"task_type\\\":\\\"regression\\\",\\\"data_script\\\":null,\\\"validation_size\\\":0.0,\\\"n_cross_validations\\\":null,\\\"y_min\\\":null,\\\"y_max\\\":null,\\\"num_classes\\\":null,\\\"featurization\\\":\\\"off\\\",\\\"preprocess\\\":false,\\\"lag_length\\\":0,\\\"is_timeseries\\\":true,\\\"max_cores_per_iteration\\\":1,\\\"max_concurrent_iterations\\\":6,\\\"iteration_timeout_minutes\\\":null,\\\"mem_in_mb\\\":null,\\\"enforce_time_on_windows\\\":false,\\\"experiment_timeout_minutes\\\":10,\\\"experiment_exit_score\\\":null,\\\"whitelist_models\\\":null,\\\"blacklist_algos\\\":[\\\"ExtremeRandomTrees\\\",\\\"AutoArima\\\",\\\"ElasticNet\\\",\\\"XGBoostRegressor\\\",\\\"Prophet\\\"],\\\"supported_models\\\":[\\\"TensorFlowLinearRegressor\\\",\\\"TCNForecaster\\\",\\\"XGBoostRegressor\\\",\\\"LightGBM\\\",\\\"RandomForest\\\",\\\"GradientBoosting\\\",\\\"TensorFlowDNN\\\",\\\"SGD\\\",\\\"LassoLars\\\",\\\"ExtremeRandomTrees\\\",\\\"OnlineGradientDescentRegressor\\\",\\\"DecisionTree\\\",\\\"AutoArima\\\",\\\"KNN\\\",\\\"ElasticNet\\\",\\\"Prophet\\\",\\\"FastLinearRegressor\\\"],\\\"auto_blacklist\\\":true,\\\"blacklist_samples_reached\\\":false,\\\"exclude_nan_labels\\\":true,\\\"verbosity\\\":40,\\\"debug_log\\\":\\\"azureml_automl.log\\\",\\\"show_warnings\\\":false,\\\"model_explainability\\\":false,\\\"service_url\\\":null,\\\"sdk_url\\\":null,\\\"sdk_packages\\\":null,\\\"enable_onnx_compatible_models\\\":false,\\\"enable_split_onnx_featurizer_estimator_models\\\":false,\\\"vm_type\\\":\\\"STANDARD_D2_V2\\\",\\\"telemetry_verbosity\\\":\\\"NOTSET\\\",\\\"send_telemetry\\\":false,\\\"enable_dnn\\\":false,\\\"enable_feature_sweeping\\\":false,\\\"time_column_name\\\":\\\"timestamp\\\",\\\"grain_column_names\\\":null,\\\"drop_column_names\\\":null,\\\"max_horizon\\\":1,\\\"dropna\\\":false,\\\"overwrite_columns\\\":true,\\\"transform_dictionary\\\":{\\\"min\\\":\\\"_automl_target_col\\\",\\\"max\\\":\\\"_automl_target_col\\\",\\\"mean\\\":\\\"_automl_target_col\\\"},\\\"window_size\\\":null,\\\"country_or_region\\\":null,\\\"lags\\\":null,\\\"seasonality\\\":-1,\\\"use_stl\\\":null,\\\"short_series_handling\\\":false,\\\"enable_early_stopping\\\":true,\\\"early_stopping_n_iters\\\":10,\\\"metrics\\\":null,\\\"enable_ensembling\\\":true,\\\"enable_stack_ensembling\\\":true,\\\"ensemble_iterations\\\":15,\\\"enable_tf\\\":false,\\\"enable_cache\\\":true,\\\"enable_subsampling\\\":false,\\\"subsample_seed\\\":null,\\\"enable_nimbusml\\\":false,\\\"enable_streaming\\\":false,\\\"label_column_name\\\":\\\"load\\\",\\\"weight_column_name\\\":null,\\\"cost_mode\\\":0,\\\"metric_operation\\\":\\\"minimize\\\"}\", \"DataPrepJsonString\": \"{\\\\\\\"training_data\\\\\\\": \\\\\\\"{\\\\\\\\\\\\\\\"blocks\\\\\\\\\\\\\\\": [{\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"7d9a634f-eb21-4a8f-aaee-98cb707adc11\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"Microsoft.DPrep.GetDatastoreFilesBlock\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"arguments\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"datastores\\\\\\\\\\\\\\\": [{\\\\\\\\\\\\\\\"datastoreName\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"workspacefilestore\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"path\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"GEFCom2014/ds_train.csv\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"resourceGroup\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"AML2\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"subscription\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"6fa1b60b-c4be-4966-a446-261a3ad62d42\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"workspaceName\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"AML2\\\\\\\\\\\\\\\"}]}, \\\\\\\\\\\\\\\"localData\\\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\\\": true, \\\\\\\\\\\\\\\"name\\\\\\\\\\\\\\\": null, \\\\\\\\\\\\\\\"annotation\\\\\\\\\\\\\\\": null}, {\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"a6a3c1b2-0a0f-4587-82d6-7eea01238e80\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"Microsoft.DPrep.ParseDelimitedBlock\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"arguments\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"columnHeadersMode\\\\\\\\\\\\\\\": 3, \\\\\\\\\\\\\\\"fileEncoding\\\\\\\\\\\\\\\": 0, \\\\\\\\\\\\\\\"handleQuotedLineBreaks\\\\\\\\\\\\\\\": false, \\\\\\\\\\\\\\\"preview\\\\\\\\\\\\\\\": false, \\\\\\\\\\\\\\\"separator\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"skipRows\\\\\\\\\\\\\\\": 0, \\\\\\\\\\\\\\\"skipRowsMode\\\\\\\\\\\\\\\": 0}, \\\\\\\\\\\\\\\"localData\\\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\\\": true, \\\\\\\\\\\\\\\"name\\\\\\\\\\\\\\\": null, \\\\\\\\\\\\\\\"annotation\\\\\\\\\\\\\\\": null}, {\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"1c501470-ae50-4825-b51f-ce905bf1179f\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"Microsoft.DPrep.DropColumnsBlock\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"arguments\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"columns\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": 0, \\\\\\\\\\\\\\\"details\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"selectedColumns\\\\\\\\\\\\\\\": [\\\\\\\\\\\\\\\"Path\\\\\\\\\\\\\\\"]}}}, \\\\\\\\\\\\\\\"localData\\\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\\\": true, \\\\\\\\\\\\\\\"name\\\\\\\\\\\\\\\": null, \\\\\\\\\\\\\\\"annotation\\\\\\\\\\\\\\\": null}, {\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"1661b61f-4ad9-4b26-9734-73bf90b1ea77\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"Microsoft.DPrep.SetColumnTypesBlock\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"arguments\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"columnConversion\\\\\\\\\\\\\\\": [{\\\\\\\\\\\\\\\"column\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\\\"details\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"load\\\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\\\": 3}, {\\\\\\\\\\\\\\\"column\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\\\"details\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"temp\\\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\\\": 3}, {\\\\\\\\\\\\\\\"column\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\\\"details\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"timestamp\\\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\\\"typeArguments\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"dateTimeFormats\\\\\\\\\\\\\\\": [\\\\\\\\\\\\\\\"%Y-%m-%d %H:%M:%S\\\\\\\\\\\\\\\"]}, \\\\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\\\": 4}]}, \\\\\\\\\\\\\\\"localData\\\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\\\": true, \\\\\\\\\\\\\\\"name\\\\\\\\\\\\\\\": null, \\\\\\\\\\\\\\\"annotation\\\\\\\\\\\\\\\": null}], \\\\\\\\\\\\\\\"inspectors\\\\\\\\\\\\\\\": [], \\\\\\\\\\\\\\\"meta\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"savedDatasetId\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"bf441b7e-e952-4f85-8f62-db6eaefede6c\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"datasetType\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"tabular\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"subscriptionId\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"6fa1b60b-c4be-4966-a446-261a3ad62d42\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"workspaceId\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2a3eb3af-2a80-46a5-afc5-e7ebb6a68bb6\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"workspaceLocation\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"southcentralus\\\\\\\\\\\\\\\"}}\\\\\\\", \\\\\\\"validation_data\\\\\\\": \\\\\\\"{\\\\\\\\\\\\\\\"blocks\\\\\\\\\\\\\\\": [{\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"f18978ff-ac98-428a-8745-52f5b13a9e29\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"Microsoft.DPrep.GetDatastoreFilesBlock\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"arguments\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"datastores\\\\\\\\\\\\\\\": [{\\\\\\\\\\\\\\\"datastoreName\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"workspacefilestore\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"path\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"GEFCom2014/ds_valid.csv\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"resourceGroup\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"AML2\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"subscription\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"6fa1b60b-c4be-4966-a446-261a3ad62d42\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"workspaceName\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"AML2\\\\\\\\\\\\\\\"}]}, \\\\\\\\\\\\\\\"localData\\\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\\\": true, \\\\\\\\\\\\\\\"name\\\\\\\\\\\\\\\": null, \\\\\\\\\\\\\\\"annotation\\\\\\\\\\\\\\\": null}, {\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"48a1fe97-f2e3-473e-bf73-cb2a776c0d34\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"Microsoft.DPrep.ParseDelimitedBlock\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"arguments\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"columnHeadersMode\\\\\\\\\\\\\\\": 3, \\\\\\\\\\\\\\\"fileEncoding\\\\\\\\\\\\\\\": 0, \\\\\\\\\\\\\\\"handleQuotedLineBreaks\\\\\\\\\\\\\\\": false, \\\\\\\\\\\\\\\"preview\\\\\\\\\\\\\\\": false, \\\\\\\\\\\\\\\"separator\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"skipRows\\\\\\\\\\\\\\\": 0, \\\\\\\\\\\\\\\"skipRowsMode\\\\\\\\\\\\\\\": 0}, \\\\\\\\\\\\\\\"localData\\\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\\\": true, \\\\\\\\\\\\\\\"name\\\\\\\\\\\\\\\": null, \\\\\\\\\\\\\\\"annotation\\\\\\\\\\\\\\\": null}, {\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"01a2f931-f9bc-430c-873b-3da62267aa30\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"Microsoft.DPrep.DropColumnsBlock\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"arguments\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"columns\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": 0, \\\\\\\\\\\\\\\"details\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"selectedColumns\\\\\\\\\\\\\\\": [\\\\\\\\\\\\\\\"Path\\\\\\\\\\\\\\\"]}}}, \\\\\\\\\\\\\\\"localData\\\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\\\": true, \\\\\\\\\\\\\\\"name\\\\\\\\\\\\\\\": null, \\\\\\\\\\\\\\\"annotation\\\\\\\\\\\\\\\": null}, {\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"de1e1ed2-8a4e-44ce-965a-d7f1f584397b\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"Microsoft.DPrep.SetColumnTypesBlock\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"arguments\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"columnConversion\\\\\\\\\\\\\\\": [{\\\\\\\\\\\\\\\"column\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\\\"details\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"load\\\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\\\": 3}, {\\\\\\\\\\\\\\\"column\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\\\"details\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"temp\\\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\\\": 3}, {\\\\\\\\\\\\\\\"column\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\": 2, \\\\\\\\\\\\\\\"details\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"selectedColumn\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"timestamp\\\\\\\\\\\\\\\"}}, \\\\\\\\\\\\\\\"typeArguments\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"dateTimeFormats\\\\\\\\\\\\\\\": [\\\\\\\\\\\\\\\"%Y-%m-%d %H:%M:%S\\\\\\\\\\\\\\\"]}, \\\\\\\\\\\\\\\"typeProperty\\\\\\\\\\\\\\\": 4}]}, \\\\\\\\\\\\\\\"localData\\\\\\\\\\\\\\\": {}, \\\\\\\\\\\\\\\"isEnabled\\\\\\\\\\\\\\\": true, \\\\\\\\\\\\\\\"name\\\\\\\\\\\\\\\": null, \\\\\\\\\\\\\\\"annotation\\\\\\\\\\\\\\\": null}], \\\\\\\\\\\\\\\"inspectors\\\\\\\\\\\\\\\": [], \\\\\\\\\\\\\\\"meta\\\\\\\\\\\\\\\": {\\\\\\\\\\\\\\\"savedDatasetId\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"9171896a-2fc2-428f-8a60-3581467cbd74\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"datasetType\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"tabular\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"subscriptionId\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"6fa1b60b-c4be-4966-a446-261a3ad62d42\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"workspaceId\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"2a3eb3af-2a80-46a5-afc5-e7ebb6a68bb6\\\\\\\\\\\\\\\", \\\\\\\\\\\\\\\"workspaceLocation\\\\\\\\\\\\\\\": \\\\\\\\\\\\\\\"southcentralus\\\\\\\\\\\\\\\"}}\\\\\\\", \\\\\\\"activities\\\\\\\": 0}\", \"EnableSubsampling\": \"False\", \"runTemplate\": \"AutoML\", \"azureml.runsource\": \"automl\", \"display_task_type\": \"forecasting\", \"dependencies_versions\": \"{\\\"azureml-widgets\\\": \\\"1.0.76\\\", \\\"azureml-train\\\": \\\"1.0.76\\\", \\\"azureml-train-restclients-hyperdrive\\\": \\\"1.0.76\\\", \\\"azureml-train-core\\\": \\\"1.0.76\\\", \\\"azureml-train-automl\\\": \\\"1.0.76\\\", \\\"azureml-train-automl-runtime\\\": \\\"1.0.76.1\\\", \\\"azureml-train-automl-client\\\": \\\"1.0.76\\\", \\\"azureml-telemetry\\\": \\\"1.0.76\\\", \\\"azureml-sdk\\\": \\\"1.0.76\\\", \\\"azureml-pipeline\\\": \\\"1.0.76\\\", \\\"azureml-pipeline-steps\\\": \\\"1.0.76\\\", \\\"azureml-pipeline-core\\\": \\\"1.0.76\\\", \\\"azureml-model-management-sdk\\\": \\\"1.0.1b6.post1\\\", \\\"azureml-interpret\\\": \\\"1.0.76\\\", \\\"azureml-explain-model\\\": \\\"1.0.76\\\", \\\"azureml-defaults\\\": \\\"1.0.76\\\", \\\"azureml-dataprep\\\": \\\"1.1.33\\\", \\\"azureml-dataprep-native\\\": \\\"13.1.0\\\", \\\"azureml-core\\\": \\\"1.0.76\\\", \\\"azureml-automl-runtime\\\": \\\"1.0.76.1\\\", \\\"azureml-automl-core\\\": \\\"1.0.76\\\"}\", \"ContentSnapshotId\": \"b68763e7-8453-467b-9ae3-36cf53f4edbd\", \"snapshotId\": \"b68763e7-8453-467b-9ae3-36cf53f4edbd\", \"azureml.git.repository_uri\": \"git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git\", \"mlflow.source.git.repoURL\": \"git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git\", \"azureml.git.branch\": \"jacob/tf2\", \"mlflow.source.git.branch\": \"jacob/tf2\", \"azureml.git.commit\": \"85fc22b3271fb98c50abb390a18345008c70b77a\", \"mlflow.source.git.commit\": \"85fc22b3271fb98c50abb390a18345008c70b77a\", \"azureml.git.dirty\": \"False\", \"SetupRunId\": \"AutoML_03f67a3a-64c1-40ea-bb5b-e8ffba18bb3b_setup\"}, \"tags\": {\"model_explain_run\": \"best_run\"}, \"end_time_utc\": null, \"status\": \"Preparing\", \"log_files\": {}, \"log_groups\": [], \"run_duration\": \"0:05:39\"}, \"child_runs\": [], \"children_metrics\": {}, \"run_metrics\": [], \"run_logs\": \"Your job is submitted in Azure cloud and we are monitoring to get logs...\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"NOTSET\", \"sdk_version\": \"1.0.76\"}, \"loading\": false}" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from azureml.widgets import RunDetails\n", + "\n", + "RunDetails(run).show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "****************************************************************************************************\n", + "ITERATION: The iteration being evaluated.\n", + "PIPELINE: A summary description of the pipeline being evaluated.\n", + "DURATION: Time taken for the current iteration.\n", + "METRIC: The result of computing score on the fitted pipeline.\n", + "BEST: The best observed score thus far.\n", + "****************************************************************************************************\n", + "\n", + " ITERATION PIPELINE DURATION METRIC BEST\n", + " 4 MaxAbsScaler RandomForest 0:01:37 0.0469 0.0469\n", + " 5 MaxAbsScaler DecisionTree 0:03:09 0.0560 0.0469\n", + " 1 StandardScalerWrapper LightGBM 0:04:45 0.0660 0.0469\n", + " 2 StandardScalerWrapper LassoLars 0:06:17 0.1518 0.0469\n", + " 8 MaxAbsScaler DecisionTree 0:02:59 0.0760 0.0469\n", + " 10 MinMaxScaler DecisionTree 0:01:43 0.0708 0.0469\n", + " 0 StandardScalerWrapper RandomForest 0:09:43 0.0489 0.0469\n", + " 6 MinMaxScaler DecisionTree 0:08:24 0.0620 0.0469\n", + " 7 StandardScalerWrapper DecisionTree 0:06:53 0.0447 0.0447\n", + " 3 MinMaxScaler DecisionTree 0:10:11 0.0778 0.0447\n", + " 12 0:00:22 nan 0.0447\n", + " 11 0:00:28 nan 0.0447\n", + " 9 0:04:05 nan 0.0447\n", + " 14 StackEnsemble 0:01:43 0.0411 0.0411\n", + " 13 VotingEnsemble 0:01:43 0.0410 0.0410\n", + "\n", + "Execution Summary\n", + "=================\n", + "RunId: AutoML_03f67a3a-64c1-40ea-bb5b-e8ffba18bb3b\n", + "\n", + "CPU times: user 6.69 s, sys: 683 ms, total: 7.37 s\n", + "Wall time: 28min 46s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "status = run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Retrieve the Best Model\n", + "Below we select the best model from all the training iterations using the `get_output` method." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "best_run, fitted_model = run.get_output()\n", + "#fitted_model.steps" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Analysis\n", + "Below we take a look at the model produced" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "### Featurization\n", + "You can access the engineered feature names generated in time-series featurization" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true, + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['temp',\n", + " 'temp_WASNULL',\n", + " 'year',\n", + " 'half',\n", + " 'quarter',\n", + " 'month',\n", + " 'day',\n", + " 'hour',\n", + " 'am_pm',\n", + " 'hour12',\n", + " 'wday',\n", + " 'qday',\n", + " 'week']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fitted_model.named_steps['timeseriestransformer'].get_engineered_feature_names()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### View featurization summary\n", + "You can also see what featurization steps were performed on different raw features in the user data. For each input field in the user data, the following information is displayed:\n", + "\n", + "- Raw field name\n", + "- Number of engineered features formed out of this raw field\n", + "- Type detected\n", + "- If the feature was dropped\n", + "- List of feature transformations for the raw field" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
DroppedEngineeredFeatureCountRawFeatureNameTransformationsTypeDetected
0No2temp[MeanImputer, ImputationMarker]Numeric
1No11timestamp[DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer]DateTime
\n", + "
" + ], + "text/plain": [ + " Dropped EngineeredFeatureCount RawFeatureName \\\n", + "0 No 2 temp \n", + "1 No 11 timestamp \n", + "\n", + " Transformations \\\n", + "0 [MeanImputer, ImputationMarker] \n", + "1 [DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer, DateTimeTransformer] \n", + "\n", + " TypeDetected \n", + "0 Numeric \n", + "1 DateTime " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the featurization summary as a list of JSON\n", + "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n", + "# View the featurization summary as a pandas dataframe\n", + "pd.DataFrame.from_records(featurization_summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Forecasting\n", + "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Forecast Function\n", + "For forecasting, we will use the forecast function instead of the predict function. There are two reasons for this.\n", + "\n", + "We need to pass the recent values of the target variable y, whereas the scikit-compatible predict function only takes the non-target variables 'test'. In our case, the test data immediately follows the training data, and we fill the target variable with NaN. The NaN serves as a question mark for the forecaster to fill with the actuals. Using the forecast function will produce forecasts using the shortest possible forecast horizon. The last time at which a definite (non-NaN) value is seen is the forecast origin - the last time when the value of the target is known.\n", + "\n", + "Using the predict method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# First, we remove the target values from the test set:\n", + "X_test = ds_test.copy()\n", + "y_test = X_test.pop('load').values\n", + "\n", + "# The forecast origin will be at the beginning of the first forecast period.\n", + "# (Which is the same time as the end of the last training period.)\n", + "y_query = np.empty_like(y_test)\n", + "y_query.fill(np.nan)\n", + "\n", + "# The featurized data, aligned to y, will also be returned.\n", + "# This contains the assumptions that were made in the forecast\n", + "# and helps align the forecast to the original data\n", + "y_predictions, X_trans = fitted_model.forecast(X_test, y_query)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "It is useful to look at how the inputs were transformed to create the predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
temptemp_WASNULLyearhalfquartermonthdayhouram_pmhour12wdayqdayweek_automl_target_col
timestamp_automl_dummy_grain_col
2014-11-01 00:00:00_automl_dummy_grain_col38.330201424111000532442499.94
2014-11-01 01:00:00_automl_dummy_grain_col37.330201424111101532442382.61
2014-11-01 02:00:00_automl_dummy_grain_col36.330201424111202532442503.76
2014-11-01 03:00:00_automl_dummy_grain_col36.330201424111303532442503.76
2014-11-01 04:00:00_automl_dummy_grain_col36.000201424111404532442509.43
\n", + "
" + ], + "text/plain": [ + " temp temp_WASNULL year half \\\n", + "timestamp _automl_dummy_grain_col \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 38.33 0 2014 2 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 37.33 0 2014 2 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 36.33 0 2014 2 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 36.33 0 2014 2 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 36.00 0 2014 2 \n", + "\n", + " quarter month day hour am_pm \\\n", + "timestamp _automl_dummy_grain_col \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 4 11 1 0 0 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 4 11 1 1 0 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 4 11 1 2 0 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 4 11 1 3 0 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 4 11 1 4 0 \n", + "\n", + " hour12 wday qday week \\\n", + "timestamp _automl_dummy_grain_col \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 0 5 32 44 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 1 5 32 44 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2 5 32 44 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 3 5 32 44 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 4 5 32 44 \n", + "\n", + " _automl_target_col \n", + "timestamp _automl_dummy_grain_col \n", + "2014-11-01 00:00:00 _automl_dummy_grain_col 2499.94 \n", + "2014-11-01 01:00:00 _automl_dummy_grain_col 2382.61 \n", + "2014-11-01 02:00:00 _automl_dummy_grain_col 2503.76 \n", + "2014-11-01 03:00:00 _automl_dummy_grain_col 2503.76 \n", + "2014-11-01 04:00:00 _automl_dummy_grain_col 2509.43 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_trans.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Evaluate\n", + "To evaluate the accuracy of the forecast, we'll compare against the actual load values using the mean absolute percentage error (MAPE) metric." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Collect the target and predictions in a dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
actualtempprediction
2014-11-01 00:00:002514.0038.332499.94
2014-11-01 01:00:002434.0037.332382.61
2014-11-01 02:00:002390.0036.332503.76
2014-11-01 03:00:002382.0036.332503.76
2014-11-01 04:00:002419.0036.002509.43
\n", + "
" + ], + "text/plain": [ + " actual temp prediction\n", + "2014-11-01 00:00:00 2514.00 38.33 2499.94 \n", + "2014-11-01 01:00:00 2434.00 37.33 2382.61 \n", + "2014-11-01 02:00:00 2390.00 36.33 2503.76 \n", + "2014-11-01 03:00:00 2382.00 36.33 2503.76 \n", + "2014-11-01 04:00:00 2419.00 36.00 2509.43 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_df = ds_test.copy().drop('timestamp', axis=1)\n", + "eval_df['prediction'] = y_predictions\n", + "eval_df.rename(columns={'load':'actual'}, inplace=True)\n", + "eval_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Compute the mean absolute percentage error over all predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [], + "source": [ + "def mape(predictions, actuals):\n", + " \"\"\"Mean absolute percentage error\"\"\"\n", + " return ((predictions - actuals).abs() / actuals).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "slideshow": { + "slide_type": "-" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAPE: 3.67%\n" + ] + } + ], + "source": [ + "print(\"MAPE: {:.2f}%\".format(100* mape(eval_df['prediction'], eval_df['actual'])))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Plot the predictions vs the actuals for the first week of the test set" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "eval_df[:'2014-11-08'] \\\n", + " .plot(y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8))\n", + "plt.xlabel('timestamp', fontsize=12)\n", + "plt.ylabel('load', fontsize=12)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6", + "language": "python", + "name": "dftf2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/automl/requirements.txt b/automl/requirements.txt new file mode 100644 index 0000000..e85c9dd --- /dev/null +++ b/automl/requirements.txt @@ -0,0 +1,3 @@ +azureml-core==1.0.76 +azureml-sdk[explain,automl] +azureml-widgets diff --git a/common/extract_data.py b/common/extract_data.py index 04636cd..2b7996a 100644 --- a/common/extract_data.py +++ b/common/extract_data.py @@ -4,13 +4,18 @@ import pandas as pd # This function unzips the GEFCom2014 data zip file and extracts the 'extended' -# load forecasting competition data. Data is saved in energy.csv +# load forecasting competition data. Data is saved in energy.csv.gz + + def extract_data(data_dir): GEFCom_dir = os.path.join(data_dir, 'GEFCom2014', 'GEFCom2014 Data') GEFCom_zipfile = os.path.join(data_dir, 'GEFCom2014.zip') if not os.path.exists(GEFCom_zipfile): - sys.exit("Download GEFCom2014.zip from https://www.dropbox.com/s/pqenrr2mcvl0hk9/GEFCom2014.zip?dl=0 and save it to the '{}' directory.".format(data_dir)) + sys.exit( + "Download GEFCom2014.zip from " + + "https://www.dropbox.com/s/pqenrr2mcvl0hk9/GEFCom2014.zip?dl=0 " + + "and save it to the '{}' directory.".format(data_dir)) # unzip root directory zip_ref = zipfile.ZipFile(GEFCom_zipfile, 'r') @@ -18,20 +23,28 @@ def extract_data(data_dir): zip_ref.close() # extract the extended competition data - zip_ref = zipfile.ZipFile(os.path.join(GEFCom_dir, 'GEFCom2014-E_V2.zip'), 'r') + zip_ref = zipfile.ZipFile( + os.path.join(GEFCom_dir, 'GEFCom2014-E_V2.zip'), + 'r') zip_ref.extractall(os.path.join(data_dir, 'GEFCom2014-E')) zip_ref.close() # load the data from Excel file - data = pd.read_excel(os.path.join(data_dir, 'GEFCom2014-E', 'GEFCom2014-E.xlsx'), parse_date='Date') + data = pd.read_excel( + os.path.join(data_dir, 'GEFCom2014-E', 'GEFCom2014-E.xlsx'), + parse_date='Date') # create timestamp variable from Date and Hour - data['timestamp'] = data['Date'].add(pd.to_timedelta(data.Hour - 1, unit='h')) + data['timestamp'] = data['Date'].add( + pd.to_timedelta(data.Hour - 1, unit='h')) data = data[['timestamp', 'load', 'T']] - data = data.rename(columns={'T':'temp'}) + data = data.rename(columns={'T': 'temp'}) # remove time period with no load data data = data[data.timestamp >= '2012-01-01'] # save to csv - data.to_csv(os.path.join(data_dir, 'energy.csv'), index=False) + data.to_csv( + os.path.join(data_dir, 'energy.csv.gz'), + compression="gzip", + index=False) diff --git a/common/progress_bar.py b/common/progress_bar.py new file mode 100644 index 0000000..75d4501 --- /dev/null +++ b/common/progress_bar.py @@ -0,0 +1,24 @@ +from IPython.display import clear_output + + +def update_progress(progress): + '''Displays a nice text progress bar in Jupyter Notebooks. + See [here](https://www.mikulskibartosz.name/how-to-display-a-progress-bar-in-jupyter-notebook/) + ''' + bar_length = 20 + if isinstance(progress, int): + progress = float(progress) + if not isinstance(progress, float): + progress = 0 + if progress < 0: + progress = 0 + if progress >= 1: + progress = 1 + + block = int(round(bar_length * progress)) + + clear_output(wait=True) + text = "Progress: [{0}] {1:.1f}%".format( + "#" * block + "-" * (bar_length - block), progress * 100 + ) + print(text) diff --git a/common/utils.py b/common/utils.py index 43bff23..b97b4c1 100644 --- a/common/utils.py +++ b/common/utils.py @@ -1,16 +1,41 @@ import numpy as np import pandas as pd -import os from collections import UserDict +import os + + +def mape(predictions, actuals): + """Mean absolute percentage error""" + return ((predictions - actuals).abs() / actuals).mean() + + +def create_evaluation_df(predictions, test_inputs, H, scaler): + """Create a data frame for easy evaluation""" + eval_df = pd.DataFrame( + predictions, columns=["t+" + str(t) for t in range(1, H + 1)] + ) + eval_df["timestamp"] = test_inputs.dataframe.index + eval_df = pd.melt( + eval_df, id_vars="timestamp", value_name="prediction", var_name="h" + ) + eval_df["actual"] = np.transpose(test_inputs["target"]).ravel() + eval_df[["prediction", "actual"]] = scaler.inverse_transform( + eval_df[["prediction", "actual"]] + ) + return eval_df + def load_data(data_dir): """Load the GEFCom 2014 energy load data""" - energy = pd.read_csv(os.path.join(data_dir, 'energy.csv'), parse_dates=['timestamp']) + energy = pd.read_csv( + os.path.join(data_dir, 'energy.csv.gz'), parse_dates=['timestamp'] + ) - # Reindex the dataframe such that the dataframe has a record for every time point - # between the minimum and maximum timestamp in the time series. This helps to - # identify missing time periods in the data (there are none in this dataset). + # Reindex the dataframe such that the dataframe has a record for + # every time point between the minimum and maximum timestamp in + # the time series. This helps to identify missing time periods in + # the data (there are none in this dataset). energy.index = energy['timestamp'] energy = energy.reindex(pd.date_range(min(energy['timestamp']), @@ -21,107 +46,104 @@ def load_data(data_dir): return energy -def mape(predictions, actuals): - """Mean absolute percentage error""" - return ((predictions - actuals).abs() / actuals).mean() - - -def create_evaluation_df(predictions, test_inputs, H, scaler): - """Create a data frame for easy evaluation""" - eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, H+1)]) - eval_df['timestamp'] = test_inputs.dataframe.index - eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h') - eval_df['actual'] = np.transpose(test_inputs['target']).ravel() - eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']]) - return eval_df - - class TimeSeriesTensor(UserDict): """A dictionary of tensors for input into the RNN model. - + Use this class to: - 1. Shift the values of the time series to create a Pandas dataframe containing all the data - for a single training example + 1. Shift the values of the time series to create a Pandas dataframe + containing all the data for a single training example 2. Discard any samples with missing values - 3. Transform this Pandas dataframe into a numpy array of shape + 3. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras The class takes the following parameters: - **dataset**: original time series - **target** name of the target column - **H**: the forecast horizon - - **tensor_structures**: a dictionary discribing the tensor structure of the form - { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) } - if features are non-sequential and should not be shifted, use the form + - **tensor_structures**: a dictionary discribing the tensor structure + of the form + { 'tensor_name' : (range(max_backward_shift, max_forward_shift), + [feature, feature, ...] ) } + if features are non-sequential and should not be shifted, use the form { 'tensor_name' : (None, [feature, feature, ...])} - **freq**: time series frequency (default 'H' - hourly) - - **drop_incomplete**: (Boolean) whether to drop incomplete samples (default True) + - **drop_incomplete**: (Boolean) whether to drop incomplete samples + (default True) """ - - def __init__(self, dataset, target, H, tensor_structure, freq='H', drop_incomplete=True): + + def __init__( + self, dataset, target, H, tensor_structure, freq="H", + drop_incomplete=True + ): self.dataset = dataset self.target = target self.tensor_structure = tensor_structure self.tensor_names = list(tensor_structure.keys()) - + self.dataframe = self._shift_data(H, freq, drop_incomplete) self.data = self._df2tensors(self.dataframe) - + def _shift_data(self, H, freq, drop_incomplete): - - # Use the tensor_structures definitions to shift the features in the original dataset. - # The result is a Pandas dataframe with multi-index columns in the hierarchy - # tensor - the name of the input tensor - # feature - the input feature to be shifted - # time step - the time step for the RNN in which the data is input. These labels - # are centred on time t. the forecast creation time + + # Use the tensor_structures definitions to shift the features in the + # original dataset. The result is a Pandas dataframe with multi-index + # columns in the hierarchy: + # tensor - the name of the input tensor + # feature - the input feature to be shifted + # time step - the time step for the RNN in which the data is input. + # These labels are centred on time t. the forecast + # creation time df = self.dataset.copy() - + idx_tuples = [] - for t in range(1, H+1): - df['t+'+str(t)] = df[self.target].shift(t*-1, freq=freq) - idx_tuples.append(('target', 'y', 't+'+str(t))) + for t in range(1, H + 1): + df["t+" + str(t)] = df[self.target].shift(t * -1, freq=freq) + idx_tuples.append(("target", "y", "t+" + str(t))) for name, structure in self.tensor_structure.items(): rng = structure[0] dataset_cols = structure[1] - + for col in dataset_cols: - - # do not shift non-sequential 'static' features + + # do not shift non-sequential 'static' features if rng is None: - df['context_'+col] = df[col] - idx_tuples.append((name, col, 'static')) + df["context_" + col] = df[col] + idx_tuples.append((name, col, "static")) else: for t in rng: - sign = '+' if t > 0 else '' - shift = str(t) if t != 0 else '' - period = 't'+sign+shift - shifted_col = name+'_'+col+'_'+period - df[shifted_col] = df[col].shift(t*-1, freq=freq) + sign = "+" if t > 0 else "" + shift = str(t) if t != 0 else "" + period = "t" + sign + shift + shifted_col = name + "_" + col + "_" + period + df[shifted_col] = df[col].shift(t * -1, freq=freq) idx_tuples.append((name, col, period)) - + df = df.drop(self.dataset.columns, axis=1) - idx = pd.MultiIndex.from_tuples(idx_tuples, names=['tensor', 'feature', 'time step']) + idx = pd.MultiIndex.from_tuples( + idx_tuples, names=["tensor", "feature", "time step"] + ) df.columns = idx if drop_incomplete: - df = df.dropna(how='any') + df = df.dropna(how="any") return df - + def _df2tensors(self, dataframe): - - # Transform the shifted Pandas dataframe into the multidimensional numpy arrays. These - # arrays can be used to input into the keras model and can be accessed by tensor name. - # For example, for a TimeSeriesTensor object named "model_inputs" and a tensor named - # "target", the input tensor can be acccessed with model_inputs['target'] - + + # Transform the shifted Pandas dataframe into the multidimensional + # numpy arrays. These arrays can be used to input into the keras model + # and can be accessed by tensor name. For example, for a + # TimeSeriesTensor object named "model_inputs" and a tensor named + # "target", the input tensor can be acccessed with + # model_inputs['target'] + inputs = {} - y = dataframe['target'] + y = dataframe["target"] y = y.as_matrix() - inputs['target'] = y + inputs["target"] = y for name, structure in self.tensor_structure.items(): rng = structure[0] @@ -135,11 +157,11 @@ def _df2tensors(self, dataframe): inputs[name] = tensor return inputs - + def subset_data(self, new_dataframe): - - # Use this function to recreate the input tensors if the shifted dataframe - # has been filtered. - + + # Use this function to recreate the input tensors if the shifted + # dataframe has been filtered. + self.dataframe = new_dataframe self.data = self._df2tensors(self.dataframe) diff --git a/data/us_holidays.csv b/data/us_holidays.csv deleted file mode 100644 index a754800..0000000 --- a/data/us_holidays.csv +++ /dev/null @@ -1,31 +0,0 @@ -Date,Holiday -02/01/2012,New Year's Day -16/01/2012,Martin Luther King Jr. Day -20/02/2012,Presidents Day -28/05/2012,Memorial Day -04/07/2012,Independence Day -03/09/2012,Labor Day -08/10/2012,Columbus Day -12/11/2012,Veterans Day -22/11/2012,Thanksgiving -25/12/2012,Christmas Day -01/01/2013,New Year's Day -21/01/2013,Martin Luther King Jr. Day -18/02/2013,Presidents Day -27/05/2013,Memorial Day -04/07/2013,Independence Day -02/09/2013,Labor Day -14/10/2013,Columbus Day -11/11/2013,Veterans Day -28/11/2013,Thanksgiving -25/12/2013,Christmas Day -01/01/2014,New Year's Day -20/01/2014,Martin Luther King Jr. Day -17/02/2014,Presidents Day -26/05/2014,Memorial Day -04/07/2014,Independence Day -01/09/2014,Labor Day -13/10/2014,Columbus Day -11/11/2014,Veterans Day -27/11/2014,Thanksgiving -25/12/2014,Christmas Day diff --git a/hyperparameter_tuning/hyperparameter_tuning.ipynb b/hyperparameter_tuning/hyperparameter_tuning.ipynb index 6d2e7bb..993e1ee 100644 --- a/hyperparameter_tuning/hyperparameter_tuning.ipynb +++ b/hyperparameter_tuning/hyperparameter_tuning.ipynb @@ -61,16 +61,7 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/data/anaconda/envs/dnntutorial/lib/python3.6/site-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.25.2) or chardet (3.0.4) doesn't match a supported version!\n", - " RequestsDependencyWarning)\n" - ] - } - ], + "outputs": [], "source": [ "import json\n", "import azureml\n", @@ -159,7 +150,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "You are using Azure ML SDK Version: 1.0.39\n" + "You are using Azure ML SDK Version: 1.0.76\n" ] } ], @@ -177,10 +168,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Workspace name: vapaunic-dnntut-ws\n", - "Azure region: westeurope\n", - "Subscription id: a61eb99b-265d-430b-91c8-911f86ae9be1\n", - "Resource group: vapaunic-aml-rg\n" + "Workspace name: AML2\n", + "Azure region: southcentralus\n", + "Subscription id: 6fa1b60b-c4be-4966-a446-261a3ad62d42\n", + "Resource group: AML2\n" ] } ], @@ -250,18 +241,22 @@ "name": "stdout", "output_type": "stream", "text": [ + "Uploading an estimated of 1 files\n", "Uploading ../data/GEFCom2014.zip\n", "Uploaded ../data/GEFCom2014.zip, 1 files out of an estimated total of 1\n", + "Uploaded 1 files\n", + "Uploading an estimated of 2 files\n", "Uploading ../common/extract_data.py\n", "Uploading ../common/utils.py\n", "Uploaded ../common/extract_data.py, 1 files out of an estimated total of 2\n", - "Uploaded ../common/utils.py, 2 files out of an estimated total of 2\n" + "Uploaded ../common/utils.py, 2 files out of an estimated total of 2\n", + "Uploaded 2 files\n" ] }, { "data": { "text/plain": [ - "$AZUREML_DATAREFERENCE_8809ad27d8104bffa0ff131f955e4a60" + "$AZUREML_DATAREFERENCE_217c331b42f746e786db0ad7ae35d8b3" ] }, "execution_count": 9, @@ -307,7 +302,7 @@ "output_type": "stream", "text": [ "Found existing compute target\n", - "{'currentNodeCount': 0, 'targetNodeCount': 0, 'nodeStateCounts': {'preparingNodeCount': 0, 'runningNodeCount': 0, 'idleNodeCount': 0, 'unusableNodeCount': 0, 'leavingNodeCount': 0, 'preemptedNodeCount': 0}, 'allocationState': 'Steady', 'allocationStateTransitionTime': '2019-06-01T03:28:07.602000+00:00', 'errors': None, 'creationTime': '2019-04-30T12:55:55.065120+00:00', 'modifiedTime': '2019-04-30T14:56:02.842868+00:00', 'provisioningState': 'Succeeded', 'provisioningStateTransitionTime': None, 'scaleSettings': {'minNodeCount': 0, 'maxNodeCount': 4, 'nodeIdleTimeBeforeScaleDown': 'PT1000S'}, 'vmPriority': 'Dedicated', 'vmSize': 'STANDARD_NC6'}\n" + "{'currentNodeCount': 0, 'targetNodeCount': 0, 'nodeStateCounts': {'preparingNodeCount': 0, 'runningNodeCount': 0, 'idleNodeCount': 0, 'unusableNodeCount': 0, 'leavingNodeCount': 0, 'preemptedNodeCount': 0}, 'allocationState': 'Steady', 'allocationStateTransitionTime': '2019-12-11T03:25:24.904000+00:00', 'errors': None, 'creationTime': '2019-12-11T02:13:19.119388+00:00', 'modifiedTime': '2019-12-11T02:13:39.916420+00:00', 'provisioningState': 'Succeeded', 'provisioningStateTransitionTime': None, 'scaleSettings': {'minNodeCount': 0, 'maxNodeCount': 4, 'nodeIdleTimeBeforeScaleDown': 'PT120S'}, 'vmPriority': 'Dedicated', 'vmSize': 'STANDARD_NC6'}\n" ] } ], @@ -374,7 +369,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "framework_version is not specified, defaulting to version 1.13.\n" + "WARNING - framework_version is not specified, defaulting to version 1.13.\n" ] } ], @@ -423,16 +418,23 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9dd268d1bc8e41ffa2e17018de9b8f55", + "model_id": "200e2135c3604b2b8b55da5747dc7bf6", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', '…" + "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'NOTSET',…" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/cnn/runs/cnn_1576034825_f1a0d7f5?wsid=/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourcegroups/AML2/workspaces/AML2\", \"run_id\": \"cnn_1576034825_f1a0d7f5\", \"run_properties\": {\"run_id\": \"cnn_1576034825_f1a0d7f5\", \"created_utc\": \"2019-12-11T03:27:06.888165Z\", \"properties\": {\"_azureml.ComputeTargetType\": \"amlcompute\", \"ContentSnapshotId\": \"28221d30-3646-4866-8a0b-b72a45db5a79\", \"azureml.git.repository_uri\": \"git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git\", \"mlflow.source.git.repoURL\": \"git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git\", \"azureml.git.branch\": \"jacob/tf2\", \"mlflow.source.git.branch\": \"jacob/tf2\", \"azureml.git.commit\": \"2f569e903fc7b1786838f2103794ef97a27a2322\", \"mlflow.source.git.commit\": \"2f569e903fc7b1786838f2103794ef97a27a2322\", \"azureml.git.dirty\": \"False\", \"AzureML.DerivedImageName\": \"azureml/azureml_9a25422eab79dd1d3af5961bb44d5575\", \"ProcessInfoFile\": \"azureml-logs/process_info.json\", \"ProcessStatusFile\": \"azureml-logs/process_status.json\"}, \"tags\": {\"_aml_system_ComputeTargetStatus\": \"{\\\"AllocationState\\\":\\\"steady\\\",\\\"PreparingNodeCount\\\":1,\\\"RunningNodeCount\\\":0,\\\"CurrentNodeCount\\\":1}\"}, \"script_name\": null, \"arguments\": null, \"end_time_utc\": \"2019-12-11T03:34:04.329302Z\", \"status\": \"Completed\", \"log_files\": {\"azureml-logs/55_azureml-execution-tvmps_f35180ac786b52e35cbdccf2dc5aabbd384850ca149b63f99fdcf927e8a7a099_d.txt\": \"https://aml23782003686.blob.core.windows.net/azureml/ExperimentRun/dcid.cnn_1576034825_f1a0d7f5/azureml-logs/55_azureml-execution-tvmps_f35180ac786b52e35cbdccf2dc5aabbd384850ca149b63f99fdcf927e8a7a099_d.txt?sv=2019-02-02&sr=b&sig=iMKyB2Rzdqs4H3mEOPchcBGQ0y3cZ333CQjeaMPNr3c%3D&st=2019-12-11T03%3A24%3A14Z&se=2019-12-11T11%3A34%3A14Z&sp=r\", \"azureml-logs/65_job_prep-tvmps_f35180ac786b52e35cbdccf2dc5aabbd384850ca149b63f99fdcf927e8a7a099_d.txt\": \"https://aml23782003686.blob.core.windows.net/azureml/ExperimentRun/dcid.cnn_1576034825_f1a0d7f5/azureml-logs/65_job_prep-tvmps_f35180ac786b52e35cbdccf2dc5aabbd384850ca149b63f99fdcf927e8a7a099_d.txt?sv=2019-02-02&sr=b&sig=RoT2g8G7h2nEp%2FIaZE66oS6SvvA6hDBeGw%2BQMq5mdwk%3D&st=2019-12-11T03%3A24%3A15Z&se=2019-12-11T11%3A34%3A15Z&sp=r\", \"azureml-logs/70_driver_log.txt\": \"https://aml23782003686.blob.core.windows.net/azureml/ExperimentRun/dcid.cnn_1576034825_f1a0d7f5/azureml-logs/70_driver_log.txt?sv=2019-02-02&sr=b&sig=2MVV5sIOz0WKZjf9XIGeun%2BYJ34G3o2h%2FUPgiuLPsdw%3D&st=2019-12-11T03%3A24%3A15Z&se=2019-12-11T11%3A34%3A15Z&sp=r\", \"azureml-logs/75_job_post-tvmps_f35180ac786b52e35cbdccf2dc5aabbd384850ca149b63f99fdcf927e8a7a099_d.txt\": \"https://aml23782003686.blob.core.windows.net/azureml/ExperimentRun/dcid.cnn_1576034825_f1a0d7f5/azureml-logs/75_job_post-tvmps_f35180ac786b52e35cbdccf2dc5aabbd384850ca149b63f99fdcf927e8a7a099_d.txt?sv=2019-02-02&sr=b&sig=T0lbHYh%2BpAmDxFvP2Plrl4k7h2%2FakyVokhRWRSAB91c%3D&st=2019-12-11T03%3A24%3A15Z&se=2019-12-11T11%3A34%3A15Z&sp=r\", \"azureml-logs/process_info.json\": \"https://aml23782003686.blob.core.windows.net/azureml/ExperimentRun/dcid.cnn_1576034825_f1a0d7f5/azureml-logs/process_info.json?sv=2019-02-02&sr=b&sig=aVvn92pRFX13vs7MUoAdeRWm00r1kn9%2FS7L7RYZEDOY%3D&st=2019-12-11T03%3A24%3A15Z&se=2019-12-11T11%3A34%3A15Z&sp=r\", \"azureml-logs/process_status.json\": \"https://aml23782003686.blob.core.windows.net/azureml/ExperimentRun/dcid.cnn_1576034825_f1a0d7f5/azureml-logs/process_status.json?sv=2019-02-02&sr=b&sig=%2F3NwsKIJ4X865xwjPXTi89iUrBeJPItbar6R4teWHLQ%3D&st=2019-12-11T03%3A24%3A15Z&se=2019-12-11T11%3A34%3A15Z&sp=r\", \"logs/azureml/136_azureml.log\": \"https://aml23782003686.blob.core.windows.net/azureml/ExperimentRun/dcid.cnn_1576034825_f1a0d7f5/logs/azureml/136_azureml.log?sv=2019-02-02&sr=b&sig=T8MPlQr24RmmehrKoykhmNxnOUyrP4YfK52zb9O0QKw%3D&st=2019-12-11T03%3A24%3A14Z&se=2019-12-11T11%3A34%3A14Z&sp=r\", \"logs/azureml/azureml.log\": \"https://aml23782003686.blob.core.windows.net/azureml/ExperimentRun/dcid.cnn_1576034825_f1a0d7f5/logs/azureml/azureml.log?sv=2019-02-02&sr=b&sig=IF01SxfdN2t5Lrmt2l%2B4Sfhk3KJGgmzDpDpCpjiTn4c%3D&st=2019-12-11T03%3A24%3A15Z&se=2019-12-11T11%3A34%3A15Z&sp=r\"}, \"log_groups\": [[\"azureml-logs/process_info.json\", \"azureml-logs/process_status.json\", \"logs/azureml/azureml.log\"], [\"azureml-logs/55_azureml-execution-tvmps_f35180ac786b52e35cbdccf2dc5aabbd384850ca149b63f99fdcf927e8a7a099_d.txt\"], [\"azureml-logs/65_job_prep-tvmps_f35180ac786b52e35cbdccf2dc5aabbd384850ca149b63f99fdcf927e8a7a099_d.txt\"], [\"azureml-logs/70_driver_log.txt\"], [\"azureml-logs/75_job_post-tvmps_f35180ac786b52e35cbdccf2dc5aabbd384850ca149b63f99fdcf927e8a7a099_d.txt\"], [\"logs/azureml/136_azureml.log\"]], \"run_duration\": \"0:06:57\"}, \"child_runs\": [], \"children_metrics\": {}, \"run_metrics\": [{\"name\": \"Loss\", \"run_id\": \"cnn_1576034825_f1a0d7f5\", \"categories\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], \"series\": [{\"data\": [0.013505496894741916, 0.005491749510720438, 0.005226260294792852, 0.005162392597918985, 0.005060522292578749, 0.0051179951146085945, 0.005134332971165339, 0.005085030978962463, 0.005099362424143114, 0.005027222022359642, 0.005071478483926507, 0.005055981670719877, 0.005059191733506301, 0.005058864032981121, 0.005070991393479208]}]}, {\"name\": \"validationLoss\", \"run_id\": \"cnn_1576034825_f1a0d7f5\", \"categories\": [0], \"series\": [{\"data\": [0.0025542569243245656]}]}, {\"name\": \"testMAPE\", \"run_id\": \"cnn_1576034825_f1a0d7f5\", \"categories\": [0], \"series\": [{\"data\": [0.05312445242839181]}]}], \"run_logs\": \"2019-12-11 03:32:49,777|azureml|DEBUG|Inputs:: kwargs: {'OutputCollection': True, 'snapshotProject': True, 'only_in_process_features': True, 'skip_track_logs_dir': True}, track_folders: None, deny_list: None, directories_to_watch: []\\n2019-12-11 03:32:49,778|azureml.history._tracking.PythonWorkingDirectory|DEBUG|Execution target type: batchai\\n2019-12-11 03:32:49,784|azureml.history._tracking.PythonWorkingDirectory|DEBUG|Failed to import pyspark with error: No module named 'pyspark'\\n2019-12-11 03:32:49,784|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|Pinning working directory for filesystems: ['pyfs']\\n2019-12-11 03:32:50,104|azureml._base_sdk_common.user_agent|DEBUG|Fetching client info from /root/.azureml/clientinfo.json\\n2019-12-11 03:32:50,105|azureml._base_sdk_common.user_agent|DEBUG|Error loading client info: [Errno 2] No such file or directory: '/root/.azureml/clientinfo.json'\\n2019-12-11 03:32:50,516|azureml.core._experiment_method|DEBUG|Trying to register submit_function search, on method \\n2019-12-11 03:32:50,517|azureml.core._experiment_method|DEBUG|Registered submit_function search, on method \\n2019-12-11 03:32:50,517|azureml.core._experiment_method|DEBUG|Trying to register submit_function search, on method \\n2019-12-11 03:32:50,517|azureml.core._experiment_method|DEBUG|Registered submit_function search, on method \\n2019-12-11 03:32:50,517|azureml.core.run|DEBUG|Adding new factory for run source hyperdrive\\n2019-12-11 03:32:51,409|azureml.core.run|DEBUG|Adding new factory for run source azureml.PipelineRun\\n2019-12-11 03:32:51,417|azureml.core.run|DEBUG|Adding new factory for run source azureml.ReusedStepRun\\n2019-12-11 03:32:51,423|azureml.core.run|DEBUG|Adding new factory for run source azureml.StepRun\\n2019-12-11 03:32:51,429|azureml.core.run|DEBUG|Adding new factory for run source azureml.scriptrun\\n2019-12-11 03:32:51,430|azureml.core.authentication.TokenRefresherDaemon|DEBUG|Starting daemon and triggering first instance\\n2019-12-11 03:32:51,438|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:51,439|azureml._restclient.clientbase|INFO|Created a worker pool for first use\\n2019-12-11 03:32:51,439|azureml.core.authentication|DEBUG|Time to expire 1814054.560842 seconds\\n2019-12-11 03:32:51,439|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:51,439|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:51,439|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:51,439|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:51,439|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:51,439|azureml._base_sdk_common.service_discovery|DEBUG|Constructing mms service url in from history url environment variable None, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:51,439|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:51,440|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:51,440|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:51,472|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:51,478|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:51,487|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:51,493|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:51,500|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:51,506|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:51,506|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.RunClient.get-async:False|DEBUG|[START]\\n2019-12-11 03:32:51,507|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:32:51,507|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5'\\n2019-12-11 03:32:51,507|msrest.http_logger|DEBUG|Request method: 'GET'\\n2019-12-11 03:32:51,507|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:32:51,508|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:32:51,508|msrest.http_logger|DEBUG| 'Content-Type': 'application/json; charset=utf-8'\\n2019-12-11 03:32:51,508|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '37ef3eeb-2310-4b6e-a8a3-e44dc48191e0'\\n2019-12-11 03:32:51,508|msrest.http_logger|DEBUG| 'request-id': '37ef3eeb-2310-4b6e-a8a3-e44dc48191e0'\\n2019-12-11 03:32:51,508|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79'\\n2019-12-11 03:32:51,508|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:32:51,508|msrest.http_logger|DEBUG|None\\n2019-12-11 03:32:51,508|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:32:51,508|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:32:51,508|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:32:51,508|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:32:51,576|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:32:51,577|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:32:51,577|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:32:51 GMT'\\n2019-12-11 03:32:51,577|msrest.http_logger|DEBUG| 'Content-Type': 'application/json; charset=utf-8'\\n2019-12-11 03:32:51,577|msrest.http_logger|DEBUG| 'Transfer-Encoding': 'chunked'\\n2019-12-11 03:32:51,577|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:32:51,577|msrest.http_logger|DEBUG| 'Vary': 'Accept-Encoding'\\n2019-12-11 03:32:51,577|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:32:51,577|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '37ef3eeb-2310-4b6e-a8a3-e44dc48191e0'\\n2019-12-11 03:32:51,577|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:32:51,577|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:32:51,578|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:32:51,578|msrest.http_logger|DEBUG| 'Content-Encoding': 'gzip'\\n2019-12-11 03:32:51,578|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:32:51,578|msrest.http_logger|DEBUG|{\\n \\\"runNumber\\\": 18,\\n \\\"rootRunId\\\": \\\"cnn_1576034825_f1a0d7f5\\\",\\n \\\"experimentId\\\": \\\"d9359e6f-96e7-4708-9d89-afeb3cf2dac4\\\",\\n \\\"createdUtc\\\": \\\"2019-12-11T03:27:06.888165+00:00\\\",\\n \\\"createdBy\\\": {\\n \\\"userObjectId\\\": \\\"d6cb2a0d-7951-42fb-8d8d-69fea3e351e6\\\",\\n \\\"userPuId\\\": \\\"10037FFE88EE1881\\\",\\n \\\"userIdp\\\": null,\\n \\\"userAltSecId\\\": null,\\n \\\"userIss\\\": \\\"https://sts.windows.net/72f988bf-86f1-41af-91ab-2d7cd011db47/\\\",\\n \\\"userTenantId\\\": \\\"72f988bf-86f1-41af-91ab-2d7cd011db47\\\",\\n \\\"userName\\\": \\\"Jacob Spoelstra\\\"\\n },\\n \\\"userId\\\": \\\"d6cb2a0d-7951-42fb-8d8d-69fea3e351e6\\\",\\n \\\"token\\\": null,\\n \\\"tokenExpiryTimeUtc\\\": null,\\n \\\"error\\\": null,\\n \\\"warnings\\\": null,\\n \\\"revision\\\": 10,\\n \\\"runId\\\": \\\"cnn_1576034825_f1a0d7f5\\\",\\n \\\"parentRunId\\\": null,\\n \\\"status\\\": \\\"Running\\\",\\n \\\"startTimeUtc\\\": \\\"2019-12-11T03:30:41.1408333+00:00\\\",\\n \\\"endTimeUtc\\\": null,\\n \\\"heartbeatEnabled\\\": false,\\n \\\"options\\\": {\\n \\\"generateDataContainerIdIfNotSpecified\\\": true\\n },\\n \\\"name\\\": null,\\n \\\"dataContainerId\\\": \\\"dcid.cnn_1576034825_f1a0d7f5\\\",\\n \\\"description\\\": null,\\n \\\"hidden\\\": false,\\n \\\"runType\\\": \\\"azureml.scriptrun\\\",\\n \\\"properties\\\": {\\n \\\"_azureml.ComputeTargetType\\\": \\\"amlcompute\\\",\\n \\\"ContentSnapshotId\\\": \\\"28221d30-3646-4866-8a0b-b72a45db5a79\\\",\\n \\\"azureml.git.repository_uri\\\": \\\"git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git\\\",\\n \\\"mlflow.source.git.repoURL\\\": \\\"git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git\\\",\\n \\\"azureml.git.branch\\\": \\\"jacob/tf2\\\",\\n \\\"mlflow.source.git.branch\\\": \\\"jacob/tf2\\\",\\n \\\"azureml.git.commit\\\": \\\"2f569e903fc7b1786838f2103794ef97a27a2322\\\",\\n \\\"mlflow.source.git.commit\\\": \\\"2f569e903fc7b1786838f2103794ef97a27a2322\\\",\\n \\\"azureml.git.dirty\\\": \\\"False\\\",\\n \\\"AzureML.DerivedImageName\\\": \\\"azureml/azureml_9a25422eab79dd1d3af5961bb44d5575\\\",\\n \\\"ProcessInfoFile\\\": \\\"azureml-logs/process_info.json\\\",\\n \\\"ProcessStatusFile\\\": \\\"azureml-logs/process_status.json\\\"\\n },\\n \\\"scriptName\\\": \\\"CNN.py\\\",\\n \\\"target\\\": \\\"gpucluster\\\",\\n \\\"tags\\\": {\\n \\\"_aml_system_ComputeTargetStatus\\\": \\\"{\\\\\\\"AllocationState\\\\\\\":\\\\\\\"steady\\\\\\\",\\\\\\\"PreparingNodeCount\\\\\\\":1,\\\\\\\"RunningNodeCount\\\\\\\":0,\\\\\\\"CurrentNodeCount\\\\\\\":1}\\\"\\n },\\n \\\"inputDatasets\\\": [],\\n \\\"runDefinition\\\": null,\\n \\\"createdFrom\\\": null,\\n \\\"cancelUri\\\": \\\"https://southcentralus.experiments.azureml.net/execution/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runId/cnn_1576034825_f1a0d7f5/cancel\\\",\\n \\\"completeUri\\\": null,\\n \\\"diagnosticsUri\\\": \\\"https://southcentralus.experiments.azureml.net/execution/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runId/cnn_1576034825_f1a0d7f5/diagnostics\\\",\\n \\\"computeRequest\\\": {\\n \\\"nodeCount\\\": 1\\n },\\n \\\"retainForLifetimeOfWorkspace\\\": false\\n}\\n2019-12-11 03:32:51,585|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.RunClient.get-async:False|DEBUG|[STOP]\\n2019-12-11 03:32:51,586|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5|DEBUG|Constructing run from dto. type: azureml.scriptrun, source: None, props: {'_azureml.ComputeTargetType': 'amlcompute', 'ContentSnapshotId': '28221d30-3646-4866-8a0b-b72a45db5a79', 'azureml.git.repository_uri': 'git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git', 'mlflow.source.git.repoURL': 'git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git', 'azureml.git.branch': 'jacob/tf2', 'mlflow.source.git.branch': 'jacob/tf2', 'azureml.git.commit': '2f569e903fc7b1786838f2103794ef97a27a2322', 'mlflow.source.git.commit': '2f569e903fc7b1786838f2103794ef97a27a2322', 'azureml.git.dirty': 'False', 'AzureML.DerivedImageName': 'azureml/azureml_9a25422eab79dd1d3af5961bb44d5575', 'ProcessInfoFile': 'azureml-logs/process_info.json', 'ProcessStatusFile': 'azureml-logs/process_status.json'}\\n2019-12-11 03:32:51,586|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunContextManager|DEBUG|Valid logs dir, setting up content loader\\n2019-12-11 03:32:51,587|azureml|WARNING|Could not import azureml.mlflow or azureml.contrib.mlflow mlflow APIs will not run against AzureML services. Add azureml-mlflow as a conda dependency for the run if this behavior is desired\\n2019-12-11 03:32:51,587|azureml.WorkerPool|DEBUG|[START]\\n2019-12-11 03:32:51,587|azureml.SendRunKillSignal|DEBUG|[START]\\n2019-12-11 03:32:51,587|azureml.RunStatusContext|DEBUG|[START]\\n2019-12-11 03:32:51,587|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunContextManager.RunStatusContext|DEBUG|[START]\\n2019-12-11 03:32:51,587|azureml.WorkingDirectoryCM|DEBUG|[START]\\n2019-12-11 03:32:51,587|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|[START]\\n2019-12-11 03:32:51,587|azureml.history._tracking.PythonWorkingDirectory|INFO|Current working dir: /mnt/batch/tasks/shared/LS_root/jobs/aml2/azureml/cnn_1576034825_f1a0d7f5/mounts/workspaceblobstore/azureml/cnn_1576034825_f1a0d7f5\\n2019-12-11 03:32:51,587|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|Calling pyfs\\n2019-12-11 03:32:51,588|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|Storing working dir for pyfs as /mnt/batch/tasks/shared/LS_root/jobs/aml2/azureml/cnn_1576034825_f1a0d7f5/mounts/workspaceblobstore/azureml/cnn_1576034825_f1a0d7f5\\n2019-12-11 03:32:53,953|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:53,953|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:53,953|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:53,954|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:53,954|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:53,954|azureml._base_sdk_common.service_discovery|DEBUG|Constructing mms service url in from history url environment variable None, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:53,954|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:53,954|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:53,955|azureml._base_sdk_common.service_discovery|DEBUG|Found history service url in environment variable AZUREML_SERVICE_ENDPOINT, history service url: https://southcentralus.experiments.azureml.net.\\n2019-12-11 03:32:53,961|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:53,962|azureml._run_impl.run_history_facade|DEBUG|Created a static thread pool for RunHistoryFacade class\\n2019-12-11 03:32:53,968|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:53,974|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:53,981|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:53,987|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:32:53,988|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.RunClient.get-async:False|DEBUG|[START]\\n2019-12-11 03:32:53,988|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:32:53,988|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5'\\n2019-12-11 03:32:53,989|msrest.http_logger|DEBUG|Request method: 'GET'\\n2019-12-11 03:32:53,989|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:32:53,989|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:32:53,989|msrest.http_logger|DEBUG| 'Content-Type': 'application/json; charset=utf-8'\\n2019-12-11 03:32:53,989|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '08217582-a0f4-46ce-96bf-c2cce285f6fd'\\n2019-12-11 03:32:53,989|msrest.http_logger|DEBUG| 'request-id': '08217582-a0f4-46ce-96bf-c2cce285f6fd'\\n2019-12-11 03:32:53,989|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79'\\n2019-12-11 03:32:53,990|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:32:53,990|msrest.http_logger|DEBUG|None\\n2019-12-11 03:32:53,990|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:32:53,990|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:32:53,990|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:32:53,990|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:32:54,057|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:32:54,058|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:32:54,058|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:32:54 GMT'\\n2019-12-11 03:32:54,058|msrest.http_logger|DEBUG| 'Content-Type': 'application/json; charset=utf-8'\\n2019-12-11 03:32:54,058|msrest.http_logger|DEBUG| 'Transfer-Encoding': 'chunked'\\n2019-12-11 03:32:54,058|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:32:54,058|msrest.http_logger|DEBUG| 'Vary': 'Accept-Encoding'\\n2019-12-11 03:32:54,058|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:32:54,058|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '08217582-a0f4-46ce-96bf-c2cce285f6fd'\\n2019-12-11 03:32:54,058|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:32:54,058|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:32:54,059|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:32:54,059|msrest.http_logger|DEBUG| 'Content-Encoding': 'gzip'\\n2019-12-11 03:32:54,059|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:32:54,059|msrest.http_logger|DEBUG|{\\n \\\"runNumber\\\": 18,\\n \\\"rootRunId\\\": \\\"cnn_1576034825_f1a0d7f5\\\",\\n \\\"experimentId\\\": \\\"d9359e6f-96e7-4708-9d89-afeb3cf2dac4\\\",\\n \\\"createdUtc\\\": \\\"2019-12-11T03:27:06.888165+00:00\\\",\\n \\\"createdBy\\\": {\\n \\\"userObjectId\\\": \\\"d6cb2a0d-7951-42fb-8d8d-69fea3e351e6\\\",\\n \\\"userPuId\\\": \\\"10037FFE88EE1881\\\",\\n \\\"userIdp\\\": null,\\n \\\"userAltSecId\\\": null,\\n \\\"userIss\\\": \\\"https://sts.windows.net/72f988bf-86f1-41af-91ab-2d7cd011db47/\\\",\\n \\\"userTenantId\\\": \\\"72f988bf-86f1-41af-91ab-2d7cd011db47\\\",\\n \\\"userName\\\": \\\"Jacob Spoelstra\\\"\\n },\\n \\\"userId\\\": \\\"d6cb2a0d-7951-42fb-8d8d-69fea3e351e6\\\",\\n \\\"token\\\": null,\\n \\\"tokenExpiryTimeUtc\\\": null,\\n \\\"error\\\": null,\\n \\\"warnings\\\": null,\\n \\\"revision\\\": 10,\\n \\\"runId\\\": \\\"cnn_1576034825_f1a0d7f5\\\",\\n \\\"parentRunId\\\": null,\\n \\\"status\\\": \\\"Running\\\",\\n \\\"startTimeUtc\\\": \\\"2019-12-11T03:30:41.1408333+00:00\\\",\\n \\\"endTimeUtc\\\": null,\\n \\\"heartbeatEnabled\\\": false,\\n \\\"options\\\": {\\n \\\"generateDataContainerIdIfNotSpecified\\\": true\\n },\\n \\\"name\\\": null,\\n \\\"dataContainerId\\\": \\\"dcid.cnn_1576034825_f1a0d7f5\\\",\\n \\\"description\\\": null,\\n \\\"hidden\\\": false,\\n \\\"runType\\\": \\\"azureml.scriptrun\\\",\\n \\\"properties\\\": {\\n \\\"_azureml.ComputeTargetType\\\": \\\"amlcompute\\\",\\n \\\"ContentSnapshotId\\\": \\\"28221d30-3646-4866-8a0b-b72a45db5a79\\\",\\n \\\"azureml.git.repository_uri\\\": \\\"git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git\\\",\\n \\\"mlflow.source.git.repoURL\\\": \\\"git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git\\\",\\n \\\"azureml.git.branch\\\": \\\"jacob/tf2\\\",\\n \\\"mlflow.source.git.branch\\\": \\\"jacob/tf2\\\",\\n \\\"azureml.git.commit\\\": \\\"2f569e903fc7b1786838f2103794ef97a27a2322\\\",\\n \\\"mlflow.source.git.commit\\\": \\\"2f569e903fc7b1786838f2103794ef97a27a2322\\\",\\n \\\"azureml.git.dirty\\\": \\\"False\\\",\\n \\\"AzureML.DerivedImageName\\\": \\\"azureml/azureml_9a25422eab79dd1d3af5961bb44d5575\\\",\\n \\\"ProcessInfoFile\\\": \\\"azureml-logs/process_info.json\\\",\\n \\\"ProcessStatusFile\\\": \\\"azureml-logs/process_status.json\\\"\\n },\\n \\\"scriptName\\\": \\\"CNN.py\\\",\\n \\\"target\\\": \\\"gpucluster\\\",\\n \\\"tags\\\": {\\n \\\"_aml_system_ComputeTargetStatus\\\": \\\"{\\\\\\\"AllocationState\\\\\\\":\\\\\\\"steady\\\\\\\",\\\\\\\"PreparingNodeCount\\\\\\\":1,\\\\\\\"RunningNodeCount\\\\\\\":0,\\\\\\\"CurrentNodeCount\\\\\\\":1}\\\"\\n },\\n \\\"inputDatasets\\\": [],\\n \\\"runDefinition\\\": null,\\n \\\"createdFrom\\\": null,\\n \\\"cancelUri\\\": \\\"https://southcentralus.experiments.azureml.net/execution/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runId/cnn_1576034825_f1a0d7f5/cancel\\\",\\n \\\"completeUri\\\": null,\\n \\\"diagnosticsUri\\\": \\\"https://southcentralus.experiments.azureml.net/execution/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runId/cnn_1576034825_f1a0d7f5/diagnostics\\\",\\n \\\"computeRequest\\\": {\\n \\\"nodeCount\\\": 1\\n },\\n \\\"retainForLifetimeOfWorkspace\\\": false\\n}\\n2019-12-11 03:32:54,063|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.RunClient.get-async:False|DEBUG|[STOP]\\n2019-12-11 03:32:54,063|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5|DEBUG|Constructing run from dto. type: azureml.scriptrun, source: None, props: {'_azureml.ComputeTargetType': 'amlcompute', 'ContentSnapshotId': '28221d30-3646-4866-8a0b-b72a45db5a79', 'azureml.git.repository_uri': 'git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git', 'mlflow.source.git.repoURL': 'git@github.com:jspoelstra/DeepLearningForTimeSeriesForecasting.git', 'azureml.git.branch': 'jacob/tf2', 'mlflow.source.git.branch': 'jacob/tf2', 'azureml.git.commit': '2f569e903fc7b1786838f2103794ef97a27a2322', 'mlflow.source.git.commit': '2f569e903fc7b1786838f2103794ef97a27a2322', 'azureml.git.dirty': 'False', 'AzureML.DerivedImageName': 'azureml/azureml_9a25422eab79dd1d3af5961bb44d5575', 'ProcessInfoFile': 'azureml-logs/process_info.json', 'ProcessStatusFile': 'azureml-logs/process_status.json'}\\n2019-12-11 03:32:54,064|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunContextManager|DEBUG|Valid logs dir, setting up content loader\\n2019-12-11 03:33:12,522|azureml.core._metrics|DEBUG|Converted key Loss of value 0.013505496894741916 to 0.013505496894741916.\\n\\n2019-12-11 03:33:12,522|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient|DEBUG|Overrides: Max batch size: 50, batch cushion: 5, Interval: 1.\\n2019-12-11 03:33:12,523|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.PostMetricsBatchDaemon|DEBUG|Starting daemon and triggering first instance\\n2019-12-11 03:33:12,523|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient|DEBUG|Used for use_batch=True.\\n2019-12-11 03:33:13,523|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:13,524|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:13,524|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:13,524|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:13,524|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:13,524|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:13,525|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:13,525|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:13,525|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.0__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:13,526|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:13,527|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:13,527|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 0__log_batch to queue of approximate size: 0\\n2019-12-11 03:33:13,527|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:33:13,527|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:13,528|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:13,528|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:13,528|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:13,528|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:13,529|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:13,529|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:13,529|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:13,529|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:13,528|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:13,533|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:13,533|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:13,538|msrest.http_logger|DEBUG| 'x-ms-client-request-id': 'a1df7018-fded-4edd-bac2-06b1812abd3f'\\n2019-12-11 03:33:13,539|msrest.http_logger|DEBUG| 'request-id': 'a1df7018-fded-4edd-bac2-06b1812abd3f'\\n2019-12-11 03:33:13,543|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:13,543|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:13,544|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:13,544|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"0f255152-6e00-4c12-abda-0ad6513c467c\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:12.522794Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.013505496894741916}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:13,545|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:13,545|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:13,545|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:13,545|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:13,679|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:13,679|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:13,680|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:13 GMT'\\n2019-12-11 03:33:13,680|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:13,680|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:13,680|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:13,680|msrest.http_logger|DEBUG| 'x-ms-client-request-id': 'a1df7018-fded-4edd-bac2-06b1812abd3f'\\n2019-12-11 03:33:13,680|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:13,680|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:13,680|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:13,680|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:13,681|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:13,682|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:14,417|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005491749510720438 to 0.005491749510720438.\\n\\n2019-12-11 03:33:14,524|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:14,524|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:14,524|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:14,524|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:14,525|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:14,525|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:14,525|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.1__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:14,526|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:14,526|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:14,526|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 1__log_batch to queue of approximate size: 1\\n2019-12-11 03:33:14,527|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:14,528|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:14,528|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:33:14,528|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:14,529|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:14,529|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:14,529|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:14,529|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:14,529|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:14,529|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:14,530|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:14,530|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:14,530|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:14,530|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:14,530|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '13acb944-a259-473a-bc4c-888eed2df8a2'\\n2019-12-11 03:33:14,530|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:14,531|msrest.http_logger|DEBUG| 'request-id': '13acb944-a259-473a-bc4c-888eed2df8a2'\\n2019-12-11 03:33:14,531|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:14,531|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:14,531|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:14,532|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"ced38e13-af47-4fbd-88f6-f515336c7174\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:14.418075Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005491749510720438}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:14,532|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:14,532|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:14,532|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:14,532|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:14,685|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:14,686|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:14,686|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:14 GMT'\\n2019-12-11 03:33:14,686|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:14,686|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:14,686|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:14,686|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '13acb944-a259-473a-bc4c-888eed2df8a2'\\n2019-12-11 03:33:14,686|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:14,686|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:14,686|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:14,687|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:14,687|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:14,688|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:16,302|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005226260294792852 to 0.005226260294792852.\\n\\n2019-12-11 03:33:16,524|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:16,524|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:16,525|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:16,525|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:16,525|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:16,525|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:16,526|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:16,526|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.2__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:16,526|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:16,527|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:16,527|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 2__log_batch to queue of approximate size: 2\\n2019-12-11 03:33:16,528|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:16,528|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:16,528|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:33:16,529|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:16,529|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:16,529|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:16,529|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:16,530|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:16,530|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:16,530|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:16,530|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:16,530|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:16,530|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:16,530|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:16,531|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '0d2cabce-60cd-419a-9be3-22597104403d'\\n2019-12-11 03:33:16,531|msrest.http_logger|DEBUG| 'request-id': '0d2cabce-60cd-419a-9be3-22597104403d'\\n2019-12-11 03:33:16,531|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:16,531|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:16,531|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:16,531|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"c75857bb-1201-435e-81c2-470cf15217e8\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:16.303139Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005226260294792852}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:16,531|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:16,532|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:16,532|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:16,532|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:16,680|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:16,680|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:16,680|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:16 GMT'\\n2019-12-11 03:33:16,681|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:16,681|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:16,681|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:16,681|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '0d2cabce-60cd-419a-9be3-22597104403d'\\n2019-12-11 03:33:16,681|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:16,681|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:16,681|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:16,681|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:16,681|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:16,682|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:18,207|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005162392597918985 to 0.005162392597918985.\\n\\n2019-12-11 03:33:18,525|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:18,525|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:18,525|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:18,525|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:18,525|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:18,526|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:18,527|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:18,527|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:18,527|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.3__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:18,527|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:18,528|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:18,528|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 3__log_batch to queue of approximate size: 3\\n2019-12-11 03:33:18,528|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:18,529|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:18,529|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:18,529|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:18,530|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:18,529|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:18,530|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:18,530|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:18,530|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:18,530|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:18,530|msrest.http_logger|DEBUG| 'x-ms-client-request-id': 'a4de6776-ee6e-4313-99d5-dfd4ecbb6476'\\n2019-12-11 03:33:18,530|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:18,531|msrest.http_logger|DEBUG| 'request-id': 'a4de6776-ee6e-4313-99d5-dfd4ecbb6476'\\n2019-12-11 03:33:18,531|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:18,531|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:18,531|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:18,531|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:18,531|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"76a35a24-29c6-432a-9a69-e3232d35c614\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:18.207815Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005162392597918985}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:18,532|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:18,532|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:18,532|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:18,532|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:18,666|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:18,666|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:18,666|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:18 GMT'\\n2019-12-11 03:33:18,666|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:18,666|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:18,666|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:18,667|msrest.http_logger|DEBUG| 'x-ms-client-request-id': 'a4de6776-ee6e-4313-99d5-dfd4ecbb6476'\\n2019-12-11 03:33:18,667|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:18,667|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:18,667|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:18,667|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:18,668|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:18,669|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:20,103|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005060522292578749 to 0.005060522292578749.\\n\\n2019-12-11 03:33:20,525|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:20,526|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:20,526|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:20,526|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:20,526|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:20,527|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:20,527|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:20,527|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:20,527|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.4__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:20,528|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:20,528|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:20,529|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 4__log_batch to queue of approximate size: 4\\n2019-12-11 03:33:20,529|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:20,529|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:20,530|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:20,530|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:20,530|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:20,530|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:20,530|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:20,530|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:20,531|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:20,531|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:20,531|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '4365221f-8e0c-49f7-baa5-e173f75fb271'\\n2019-12-11 03:33:20,531|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:20,531|msrest.http_logger|DEBUG| 'request-id': '4365221f-8e0c-49f7-baa5-e173f75fb271'\\n2019-12-11 03:33:20,531|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:20,531|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:20,532|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:20,532|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:20,532|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"e3267d89-077d-4889-bdfa-5df5966f7afa\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:20.104116Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005060522292578749}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:20,532|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:20,532|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:20,533|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:20,533|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:20,672|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:20,673|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:20,673|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:20 GMT'\\n2019-12-11 03:33:20,673|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:20,673|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:20,673|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:20,673|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '4365221f-8e0c-49f7-baa5-e173f75fb271'\\n2019-12-11 03:33:20,673|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:20,674|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:20,674|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:20,674|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:20,674|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:20,675|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:21,431|azureml.core.authentication|DEBUG|Time to expire 1814024.568969 seconds\\n2019-12-11 03:33:22,037|azureml.core._metrics|DEBUG|Converted key Loss of value 0.0051179951146085945 to 0.0051179951146085945.\\n\\n2019-12-11 03:33:22,526|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:22,526|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:22,527|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:22,527|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:22,527|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:22,527|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:22,528|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:22,528|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.5__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:22,528|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:22,530|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:22,530|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:22,529|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:22,529|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 5__log_batch to queue of approximate size: 5\\n2019-12-11 03:33:22,530|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:22,530|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:33:22,530|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:22,531|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:22,531|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:22,531|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:22,531|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:22,531|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:22,531|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:22,531|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:22,532|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:22,532|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:22,533|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '0b7e7512-a6d7-4c3a-b8d4-df63b27cd40e'\\n2019-12-11 03:33:22,533|msrest.http_logger|DEBUG| 'request-id': '0b7e7512-a6d7-4c3a-b8d4-df63b27cd40e'\\n2019-12-11 03:33:22,533|msrest.http_logger|DEBUG| 'Content-Length': '345'\\n2019-12-11 03:33:22,533|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:22,533|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:22,533|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"a959510c-24da-4eca-8740-31a343736f3c\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:22.037926Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.0051179951146085945}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:22,533|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:22,533|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:22,533|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:22,533|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:22,666|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:22,666|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:22,666|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:22 GMT'\\n2019-12-11 03:33:22,667|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:22,667|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:22,667|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:22,667|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '0b7e7512-a6d7-4c3a-b8d4-df63b27cd40e'\\n2019-12-11 03:33:22,667|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:22,667|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:22,667|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:22,667|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:22,667|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:22,669|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:23,961|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005134332971165339 to 0.005134332971165339.\\n\\n2019-12-11 03:33:24,527|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:24,527|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:24,527|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:24,527|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:24,527|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:24,527|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:24,528|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:24,528|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:24,529|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.6__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:24,530|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:24,530|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:24,530|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 6__log_batch to queue of approximate size: 6\\n2019-12-11 03:33:24,530|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:33:24,531|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:24,531|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:24,531|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:24,531|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:24,532|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:24,532|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:24,532|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:24,532|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:24,532|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:24,532|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:24,533|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '7af23bb7-e080-458e-9faf-cb1bf2d65f38'\\n2019-12-11 03:33:24,533|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:24,533|msrest.http_logger|DEBUG| 'request-id': '7af23bb7-e080-458e-9faf-cb1bf2d65f38'\\n2019-12-11 03:33:24,533|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:24,533|msrest.http_logger|DEBUG| 'Content-Length': '343'\\n2019-12-11 03:33:24,533|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:24,533|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:24,534|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"f9351a40-79cb-4b61-bb77-198bbb7c3ddc\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:23.96193Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005134332971165339}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:24,534|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:24,534|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:24,534|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:24,534|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:24,707|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:24,707|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:24,707|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:24 GMT'\\n2019-12-11 03:33:24,708|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:24,708|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:24,708|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:24,708|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '7af23bb7-e080-458e-9faf-cb1bf2d65f38'\\n2019-12-11 03:33:24,708|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:24,708|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:24,708|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:24,708|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:24,708|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:24,710|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:25,845|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005085030978962463 to 0.005085030978962463.\\n\\n2019-12-11 03:33:26,527|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:26,528|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:26,528|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:26,528|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:26,529|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:26,529|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:26,529|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:26,529|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:26,528|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:26,529|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:26,530|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:26,530|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.7__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:26,531|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:26,532|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 7__log_batch to queue of approximate size: 7\\n2019-12-11 03:33:26,532|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:26,533|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:26,533|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:26,533|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:26,533|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:26,533|msrest.http_logger|DEBUG| 'x-ms-client-request-id': 'ca8cda7a-0784-4c3d-8e60-d377521eca0e'\\n2019-12-11 03:33:26,533|msrest.http_logger|DEBUG| 'request-id': 'ca8cda7a-0784-4c3d-8e60-d377521eca0e'\\n2019-12-11 03:33:26,533|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:26,533|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:26,533|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:26,534|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"27738a1d-71d8-4941-abeb-8c7cacaf173f\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:25.845773Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005085030978962463}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:26,534|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:26,534|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:26,534|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:26,534|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:26,665|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:26,666|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:26,666|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:26 GMT'\\n2019-12-11 03:33:26,666|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:26,666|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:26,666|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:26,666|msrest.http_logger|DEBUG| 'x-ms-client-request-id': 'ca8cda7a-0784-4c3d-8e60-d377521eca0e'\\n2019-12-11 03:33:26,666|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:26,667|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:26,667|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:26,667|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:26,667|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:26,669|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:26,780|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:26,780|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:26,780|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:26,781|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Waiting on task: 0__handle_batch.\\n1 tasks left. Current duration of flush 0.0007410049438476562 seconds.\\n\\n2019-12-11 03:33:26,781|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:27,743|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005099362424143114 to 0.005099362424143114.\\n\\n2019-12-11 03:33:28,528|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:28,528|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:28,528|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:28,528|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:28,528|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:28,529|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:28,529|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:28,529|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:28,530|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.8__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:28,531|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:28,531|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:28,531|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 8__log_batch to queue of approximate size: 8\\n2019-12-11 03:33:28,531|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:33:28,532|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:28,532|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:28,532|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:28,532|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:28,532|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:28,533|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:28,533|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:28,533|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:28,533|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:28,533|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:28,534|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:28,534|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:28,534|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '4c9c9a78-cbce-4650-bb02-c833afa1bcb0'\\n2019-12-11 03:33:28,534|msrest.http_logger|DEBUG| 'request-id': '4c9c9a78-cbce-4650-bb02-c833afa1bcb0'\\n2019-12-11 03:33:28,534|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:28,534|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:28,534|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:28,535|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"cf752462-023d-4487-9e3c-64b1ffaab4a4\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:27.743514Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005099362424143114}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:28,535|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:28,535|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:28,535|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:28,535|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:28,681|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:28,682|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:28,682|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:28 GMT'\\n2019-12-11 03:33:28,682|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:28,682|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:28,682|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:28,682|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '4c9c9a78-cbce-4650-bb02-c833afa1bcb0'\\n2019-12-11 03:33:28,682|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:28,682|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:28,682|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:28,682|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:28,683|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:28,684|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:29,717|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005027222022359642 to 0.005027222022359642.\\n\\n2019-12-11 03:33:30,529|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:30,529|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:30,529|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:30,529|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:30,530|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:30,530|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:30,531|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:30,531|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:30,531|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.9__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:30,532|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:30,532|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:30,533|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:30,533|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:30,533|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 9__log_batch to queue of approximate size: 9\\n2019-12-11 03:33:30,533|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:30,533|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:30,534|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:30,534|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:30,534|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:30,534|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:30,534|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:30,534|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:30,534|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:30,535|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:30,534|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '15bc60cd-68be-4d6c-824e-62775286b134'\\n2019-12-11 03:33:30,535|msrest.http_logger|DEBUG| 'request-id': '15bc60cd-68be-4d6c-824e-62775286b134'\\n2019-12-11 03:33:30,536|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:30,536|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:30,536|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:30,536|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"019c9362-f5a2-4ec4-b13e-0d9ac73171ce\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:29.717615Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005027222022359642}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:30,536|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:30,536|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:30,536|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:30,536|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:30,690|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:30,690|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:30,690|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:30 GMT'\\n2019-12-11 03:33:30,691|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:30,691|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:30,691|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:30,691|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '15bc60cd-68be-4d6c-824e-62775286b134'\\n2019-12-11 03:33:30,691|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:30,691|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:30,691|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:30,691|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:30,692|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:30,693|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:31,619|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005071478483926507 to 0.005071478483926507.\\n\\n2019-12-11 03:33:32,529|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:32,529|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:32,530|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:32,530|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:32,530|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:32,530|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:32,531|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:32,531|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.10__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:32,531|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:32,532|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:32,532|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 10__log_batch to queue of approximate size: 10\\n2019-12-11 03:33:32,532|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:32,533|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:33:32,533|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:32,534|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:32,534|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:32,534|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:32,534|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:32,534|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:32,534|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:32,535|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:32,535|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:32,535|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:32,536|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:32,536|msrest.http_logger|DEBUG| 'x-ms-client-request-id': 'a3a953aa-c7ea-4eca-81d8-4a54feba8c2f'\\n2019-12-11 03:33:32,536|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:32,536|msrest.http_logger|DEBUG| 'request-id': 'a3a953aa-c7ea-4eca-81d8-4a54feba8c2f'\\n2019-12-11 03:33:32,536|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:32,536|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:32,536|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:32,537|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"a4dcc9ec-d583-422a-bb19-7fa735737036\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:31.619814Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005071478483926507}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:32,537|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:32,537|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:32,537|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:32,537|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:32,677|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:32,677|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:32,677|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:32 GMT'\\n2019-12-11 03:33:32,677|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:32,678|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:32,678|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:32,678|msrest.http_logger|DEBUG| 'x-ms-client-request-id': 'a3a953aa-c7ea-4eca-81d8-4a54feba8c2f'\\n2019-12-11 03:33:32,678|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:32,678|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:32,678|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:32,679|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:32,679|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:32,680|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:33,496|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005055981670719877 to 0.005055981670719877.\\n\\n2019-12-11 03:33:33,529|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:33,529|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:33,530|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:33,530|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:33,530|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:33,530|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:33,531|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:33,531|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:33,532|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:33,532|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.11__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:33,533|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:33,533|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:33,533|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 11__log_batch to queue of approximate size: 11\\n2019-12-11 03:33:33,533|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:33,533|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:33,534|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:33,534|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:33,534|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:33,534|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:33,534|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:33,534|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:33,534|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '945707c6-f722-48fc-8d86-b3784fe83fab'\\n2019-12-11 03:33:33,535|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:33,535|msrest.http_logger|DEBUG| 'request-id': '945707c6-f722-48fc-8d86-b3784fe83fab'\\n2019-12-11 03:33:33,536|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:33,535|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:33,536|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:33,536|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:33,536|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:33,536|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"baa30acf-ae41-435e-9391-59290a2b8ad6\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:33.497113Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005055981670719877}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:33,536|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:33,536|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:33,536|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:33,537|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:33,684|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:33,684|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:33,684|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:33 GMT'\\n2019-12-11 03:33:33,684|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:33,684|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:33,685|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:33,685|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '945707c6-f722-48fc-8d86-b3784fe83fab'\\n2019-12-11 03:33:33,685|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:33,685|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:33,685|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:33,685|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:33,686|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:33,687|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:35,402|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005059191733506301 to 0.005059191733506301.\\n\\n2019-12-11 03:33:35,530|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:35,530|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:35,531|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:35,531|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:35,531|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:35,531|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:35,532|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:35,532|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:35,532|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.12__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:35,533|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:35,534|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:33:35,534|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 12__log_batch to queue of approximate size: 12\\n2019-12-11 03:33:35,533|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:35,534|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:35,534|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:35,535|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:35,535|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:35,535|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:35,535|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:35,535|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:35,535|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:35,535|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:35,535|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:35,536|msrest.http_logger|DEBUG| 'x-ms-client-request-id': 'dffe7e54-5543-4c6c-aa71-871b79d700d2'\\n2019-12-11 03:33:35,536|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:35,536|msrest.http_logger|DEBUG| 'request-id': 'dffe7e54-5543-4c6c-aa71-871b79d700d2'\\n2019-12-11 03:33:35,536|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:35,537|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:35,537|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:35,537|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:35,537|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"44a15f73-6099-4117-9129-9c534bff950e\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:35.402267Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005059191733506301}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:35,537|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:35,537|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:35,537|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:35,538|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:37,315|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005070991393479208 to 0.005070991393479208.\\n\\n2019-12-11 03:33:37,531|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:37,531|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:37,531|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 1.\\n2019-12-11 03:33:37,532|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:37,532|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:37,532|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:37,532|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:37,532|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:37,533|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.13__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:37,533|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:37,534|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:37,534|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 13__log_batch to queue of approximate size: 13\\n2019-12-11 03:33:37,535|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:37,535|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:37,535|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:37,535|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:37,536|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:37,535|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:37,536|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:37,536|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:37,536|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:37,537|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:37,537|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:37,538|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '04d87655-3f4b-4479-84ee-dec233c8f8b6'\\n2019-12-11 03:33:37,538|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:37,538|msrest.http_logger|DEBUG| 'request-id': '04d87655-3f4b-4479-84ee-dec233c8f8b6'\\n2019-12-11 03:33:37,538|msrest.http_logger|DEBUG| 'Content-Length': '344'\\n2019-12-11 03:33:37,538|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:37,538|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:37,538|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"9d64f262-e4f2-4311-ab97-e74dc2b4baed\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:37.316248Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005070991393479208}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:37,538|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:37,538|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:37,539|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:37,539|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:38,476|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:38,476|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:38,477|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:38 GMT'\\n2019-12-11 03:33:38,477|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:38,477|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:38,477|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:38,478|msrest.http_logger|DEBUG| 'x-ms-client-request-id': 'dffe7e54-5543-4c6c-aa71-871b79d700d2'\\n2019-12-11 03:33:38,478|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:38,478|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:38,478|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:38,478|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:38,478|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:38,479|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:39,222|azureml.core._metrics|DEBUG|Converted key Loss of value 0.005058864032981121 to 0.005058864032981121.\\n\\n2019-12-11 03:33:39,454|azureml.core._metrics|DEBUG|Converted key validationLoss of value 0.0025542569243245656 to 0.0025542569243245656.\\n\\n2019-12-11 03:33:39,454|azureml.core._metrics|DEBUG|Converted key testMAPE of value 0.05312445242839181 to 0.05312445242839181.\\n\\n2019-12-11 03:33:39,531|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Start]\\n2019-12-11 03:33:39,531|azureml.BatchTaskQueueAdd_1_Batches.WorkerPool|DEBUG|submitting future: _handle_batch\\n2019-12-11 03:33:39,532|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Batch size 3.\\n2019-12-11 03:33:39,532|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:39,532|azureml._restclient.clientbase.WorkerPool|DEBUG|submitting future: _log_batch\\n2019-12-11 03:33:39,532|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|Adding task 0__handle_batch to queue of approximate size: 0\\n2019-12-11 03:33:39,533|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[START]\\n2019-12-11 03:33:39,533|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|[Stop] - waiting default timeout\\n2019-12-11 03:33:39,533|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.14__log_batch|DEBUG|Using basic handler - no exception handling\\n2019-12-11 03:33:39,534|msrest.service_client|DEBUG|Accept header absent and forced to application/json\\n2019-12-11 03:33:39,534|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[START]\\n2019-12-11 03:33:39,535|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Adding task 14__log_batch to queue of approximate size: 14\\n2019-12-11 03:33:39,535|msrest.universal_http.requests|DEBUG|Configuring retry: max_retries=3, backoff_factor=0.8, max_backoff=90\\n2019-12-11 03:33:39,535|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Overriding default flush timeout from None to 120\\n2019-12-11 03:33:39,535|msrest.http_logger|DEBUG|Request URL: 'https://southcentralus.experiments.azureml.net/history/v1.0/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576034825_f1a0d7f5/batch/metrics'\\n2019-12-11 03:33:39,536|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|Waiting 120 seconds on tasks: [AsyncTask(0__handle_batch)].\\n2019-12-11 03:33:39,536|msrest.http_logger|DEBUG|Request method: 'POST'\\n2019-12-11 03:33:39,536|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,536|msrest.http_logger|DEBUG|Request headers:\\n2019-12-11 03:33:39,536|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|Awaiter is BatchTaskQueueAdd_1_Batches\\n2019-12-11 03:33:39,536|msrest.http_logger|DEBUG| 'Accept': 'application/json'\\n2019-12-11 03:33:39,536|azureml.BatchTaskQueueAdd_1_Batches.0__handle_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,536|msrest.http_logger|DEBUG| 'Content-Type': 'application/json-patch+json; charset=utf-8'\\n2019-12-11 03:33:39,537|azureml.BatchTaskQueueAdd_1_Batches|DEBUG|\\n2019-12-11 03:33:39,537|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '8ebef24e-d3a3-4908-82f7-959cf4c94ce9'\\n2019-12-11 03:33:39,537|azureml.BatchTaskQueueAdd_1_Batches.WaitFlushSource:BatchTaskQueueAdd_1_Batches|DEBUG|[STOP]\\n2019-12-11 03:33:39,537|msrest.http_logger|DEBUG| 'request-id': '8ebef24e-d3a3-4908-82f7-959cf4c94ce9'\\n2019-12-11 03:33:39,537|msrest.http_logger|DEBUG| 'Content-Length': '1064'\\n2019-12-11 03:33:39,537|msrest.http_logger|DEBUG| 'User-Agent': 'python/3.6.2 (Linux-4.15.0-1057-azure-x86_64-with-debian-stretch-sid) msrest/0.6.10 azureml._restclient/core.1.0.79 sdk_run'\\n2019-12-11 03:33:39,537|msrest.http_logger|DEBUG|Request body:\\n2019-12-11 03:33:39,537|msrest.http_logger|DEBUG|{\\\"values\\\": [{\\\"metricId\\\": \\\"f8cfb11a-7b64-44de-9043-bc18d84c33d2\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:39.222458Z\\\", \\\"name\\\": \\\"Loss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"Loss\\\": 0.005058864032981121}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"Loss\\\", \\\"name\\\": \\\"Loss\\\", \\\"type\\\": \\\"float\\\"}]}}, {\\\"metricId\\\": \\\"f0783094-a356-4361-b04c-2d259c22fe5c\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:39.454736Z\\\", \\\"name\\\": \\\"validationLoss\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"validationLoss\\\": 0.0025542569243245656}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"validationLoss\\\", \\\"name\\\": \\\"validationLoss\\\", \\\"type\\\": \\\"float\\\"}]}}, {\\\"metricId\\\": \\\"dd2694a6-8373-4c39-b42b-b0f49210ec95\\\", \\\"metricType\\\": \\\"azureml.v1.scalar\\\", \\\"createdUtc\\\": \\\"2019-12-11T03:33:39.454988Z\\\", \\\"name\\\": \\\"testMAPE\\\", \\\"description\\\": \\\"\\\", \\\"numCells\\\": 1, \\\"cells\\\": [{\\\"testMAPE\\\": 0.05312445242839181}], \\\"schema\\\": {\\\"numProperties\\\": 1, \\\"properties\\\": [{\\\"propertyId\\\": \\\"testMAPE\\\", \\\"name\\\": \\\"testMAPE\\\", \\\"type\\\": \\\"float\\\"}]}}]}\\n2019-12-11 03:33:39,538|msrest.universal_http|DEBUG|Configuring redirects: allow=True, max=30\\n2019-12-11 03:33:39,538|msrest.universal_http|DEBUG|Configuring request: timeout=100, verify=True, cert=None\\n2019-12-11 03:33:39,538|msrest.universal_http|DEBUG|Configuring proxies: ''\\n2019-12-11 03:33:39,538|msrest.universal_http|DEBUG|Evaluate proxies against ENV settings: True\\n2019-12-11 03:33:39,643|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|Calling pyfs\\n2019-12-11 03:33:39,643|azureml.history._tracking.PythonWorkingDirectory|INFO|Current working dir: /mnt/batch/tasks/shared/LS_root/jobs/aml2/azureml/cnn_1576034825_f1a0d7f5/mounts/workspaceblobstore/azureml/cnn_1576034825_f1a0d7f5\\n2019-12-11 03:33:39,643|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|Reverting working dir from /mnt/batch/tasks/shared/LS_root/jobs/aml2/azureml/cnn_1576034825_f1a0d7f5/mounts/workspaceblobstore/azureml/cnn_1576034825_f1a0d7f5 to /mnt/batch/tasks/shared/LS_root/jobs/aml2/azureml/cnn_1576034825_f1a0d7f5/mounts/workspaceblobstore/azureml/cnn_1576034825_f1a0d7f5\\n2019-12-11 03:33:39,643|azureml.history._tracking.PythonWorkingDirectory|INFO|Working dir is already updated /mnt/batch/tasks/shared/LS_root/jobs/aml2/azureml/cnn_1576034825_f1a0d7f5/mounts/workspaceblobstore/azureml/cnn_1576034825_f1a0d7f5\\n2019-12-11 03:33:39,643|azureml.history._tracking.PythonWorkingDirectory.workingdir|DEBUG|[STOP]\\n2019-12-11 03:33:39,643|azureml.WorkingDirectoryCM|DEBUG|[STOP]\\n2019-12-11 03:33:39,643|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5|INFO|complete is not setting status for submitted runs.\\n2019-12-11 03:33:39,644|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[START]\\n2019-12-11 03:33:39,644|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient|DEBUG|Overrides: Max batch size: 50, batch cushion: 5, Interval: 1.\\n2019-12-11 03:33:39,644|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.PostMetricsBatchDaemon|DEBUG|Starting daemon and triggering first instance\\n2019-12-11 03:33:39,644|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient|DEBUG|Used for use_batch=True.\\n2019-12-11 03:33:39,644|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[START]\\n2019-12-11 03:33:39,644|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|flush timeout 300 is different from task queue timeout 120, using flush timeout\\n2019-12-11 03:33:39,644|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|Waiting 300 seconds on tasks: [].\\n2019-12-11 03:33:39,644|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|\\n2019-12-11 03:33:39,645|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[STOP]\\n2019-12-11 03:33:39,645|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[STOP]\\n2019-12-11 03:33:39,645|azureml.RunStatusContext|DEBUG|[STOP]\\n2019-12-11 03:33:39,645|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[START]\\n2019-12-11 03:33:39,645|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[START]\\n2019-12-11 03:33:39,645|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|flush timeout 300.0 is different from task queue timeout 120, using flush timeout\\n2019-12-11 03:33:39,645|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|Waiting 300.0 seconds on tasks: [].\\n2019-12-11 03:33:39,645|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|\\n2019-12-11 03:33:39,645|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[STOP]\\n2019-12-11 03:33:39,645|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[STOP]\\n2019-12-11 03:33:39,646|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[START]\\n2019-12-11 03:33:39,646|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[START]\\n2019-12-11 03:33:39,646|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|flush timeout 300.0 is different from task queue timeout 120, using flush timeout\\n2019-12-11 03:33:39,646|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|Waiting 300.0 seconds on tasks: [AsyncTask(0__log_batch), AsyncTask(1__log_batch), AsyncTask(2__log_batch), AsyncTask(3__log_batch), AsyncTask(4__log_batch), AsyncTask(5__log_batch), AsyncTask(6__log_batch), AsyncTask(7__log_batch), AsyncTask(8__log_batch), AsyncTask(9__log_batch), AsyncTask(10__log_batch), AsyncTask(11__log_batch), AsyncTask(12__log_batch), AsyncTask(13__log_batch), AsyncTask(14__log_batch)].\\n2019-12-11 03:33:39,646|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.0__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,646|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.0__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,646|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.0__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,646|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.1__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,646|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.1__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,646|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.1__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.2__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.2__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.2__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.3__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.3__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.3__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.4__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.4__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.4__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.5__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.5__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,647|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.5__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.6__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.6__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.6__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.7__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.7__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.7__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.8__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.8__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.8__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.9__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.9__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,648|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.9__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,649|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.10__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,649|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.10__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,649|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.10__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,649|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.11__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,649|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.11__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,649|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.11__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,649|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.12__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,649|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.12__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,649|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.12__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,664|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:39,664|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:39,664|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:39 GMT'\\n2019-12-11 03:33:39,664|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:39,664|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:39,664|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:39,664|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '8ebef24e-d3a3-4908-82f7-959cf4c94ce9'\\n2019-12-11 03:33:39,664|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:39,665|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:39,665|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:39,665|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:39,665|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:39,666|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:39,781|msrest.http_logger|DEBUG|Response status: 200\\n2019-12-11 03:33:39,781|msrest.http_logger|DEBUG|Response headers:\\n2019-12-11 03:33:39,781|msrest.http_logger|DEBUG| 'Date': 'Wed, 11 Dec 2019 03:33:39 GMT'\\n2019-12-11 03:33:39,781|msrest.http_logger|DEBUG| 'Content-Length': '0'\\n2019-12-11 03:33:39,781|msrest.http_logger|DEBUG| 'Connection': 'keep-alive'\\n2019-12-11 03:33:39,782|msrest.http_logger|DEBUG| 'Request-Context': 'appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d'\\n2019-12-11 03:33:39,782|msrest.http_logger|DEBUG| 'x-ms-client-request-id': '04d87655-3f4b-4479-84ee-dec233c8f8b6'\\n2019-12-11 03:33:39,782|msrest.http_logger|DEBUG| 'x-ms-client-session-id': ''\\n2019-12-11 03:33:39,782|msrest.http_logger|DEBUG| 'Strict-Transport-Security': 'max-age=15724800; includeSubDomains; preload'\\n2019-12-11 03:33:39,782|msrest.http_logger|DEBUG| 'X-Content-Type-Options': 'nosniff'\\n2019-12-11 03:33:39,782|msrest.http_logger|DEBUG|Response content:\\n2019-12-11 03:33:39,782|msrest.http_logger|DEBUG|\\n2019-12-11 03:33:39,783|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.post_batch-async:False|DEBUG|[STOP]\\n2019-12-11 03:33:39,900|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.13__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,900|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.13__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,900|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.13__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,900|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.14__log_batch.WaitingTask|DEBUG|[START]\\n2019-12-11 03:33:39,900|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.14__log_batch.WaitingTask|DEBUG|Awaiter is PostMetricsBatch\\n2019-12-11 03:33:39,900|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.14__log_batch.WaitingTask|DEBUG|[STOP]\\n2019-12-11 03:33:39,900|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch|DEBUG|Waiting on task: 13__log_batch.\\nWaiting on task: 14__log_batch.\\n2 tasks left. Current duration of flush 0.003556966781616211 seconds.\\n\\n2019-12-11 03:33:39,900|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.PostMetricsBatch.WaitFlushSource:MetricsClient|DEBUG|[STOP]\\n2019-12-11 03:33:39,900|azureml._SubmittedRun#cnn_1576034825_f1a0d7f5.RunHistoryFacade.MetricsClient.FlushingMetricsClient|DEBUG|[STOP]\\n2019-12-11 03:33:39,901|azureml.SendRunKillSignal|DEBUG|[STOP]\\n2019-12-11 03:33:39,901|azureml.HistoryTrackingWorkerPool.WorkerPoolShutdown|DEBUG|[START]\\n2019-12-11 03:33:39,901|azureml.HistoryTrackingWorkerPool.WorkerPoolShutdown|DEBUG|[STOP]\\n2019-12-11 03:33:39,901|azureml.WorkerPool|DEBUG|[STOP]\\n\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"NOTSET\", \"sdk_version\": \"1.0.76\"}, \"loading\": false}" + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -447,16 +449,23 @@ { "data": { "text/plain": [ - "{'Loss': [0.009110108019041547,\n", - " 0.005354772156065162,\n", - " 0.005015456444958626,\n", - " 0.0047539635646614165,\n", - " 0.004554342382865237,\n", - " 0.004436220557913219,\n", - " 0.004371501290375846,\n", - " 0.0043796824610396745],\n", - " 'validationLoss': 0.002579645800869912,\n", - " 'testMAPE': 0.04557668535775167}" + "{'Loss': [0.013505496894741916,\n", + " 0.005491749510720438,\n", + " 0.005226260294792852,\n", + " 0.005162392597918985,\n", + " 0.005060522292578749,\n", + " 0.0051179951146085945,\n", + " 0.005134332971165339,\n", + " 0.005085030978962463,\n", + " 0.005099362424143114,\n", + " 0.005027222022359642,\n", + " 0.005071478483926507,\n", + " 0.005055981670719877,\n", + " 0.005059191733506301,\n", + " 0.005070991393479208,\n", + " 0.005058864032981121],\n", + " 'validationLoss': 0.0025542569243245656,\n", + " 'testMAPE': 0.05312445242839181}" ] }, "execution_count": 14, @@ -506,7 +515,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "framework_version is not specified, defaulting to version 1.13.\n" + "WARNING - framework_version is not specified, defaulting to version 1.13.\n" ] } ], @@ -599,12 +608,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ab227246b78b4726b09994afafaf5025", + "model_id": "b45e4ea41c984117a0482b58a562b4d3", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO'…" + "_HyperDriveWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'NOTSE…" ] }, "metadata": {}, @@ -612,70 +621,7 @@ }, { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', '…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', '…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', '…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', '…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "376d6f944fc44852a40f01bf04509a56", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "_UserRunWidget(widget_settings={'childWidgetDisplay': 'popup', 'send_telemetry': False, 'log_level': 'INFO', '…" - ] + "application/aml.mini.widget.v1": "{\"status\": \"Completed\", \"workbench_run_details_uri\": \"https://ml.azure.com/experiments/cnn/runs/cnn_1576035422508121?wsid=/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourcegroups/AML2/workspaces/AML2\", \"run_id\": \"cnn_1576035422508121\", \"run_properties\": {\"run_id\": \"cnn_1576035422508121\", \"created_utc\": \"2019-12-11T03:37:02.584945Z\", \"properties\": {\"primary_metric_config\": \"{\\\"name\\\": \\\"Loss\\\", \\\"goal\\\": \\\"minimize\\\"}\", \"resume_from\": \"null\", \"runTemplate\": \"HyperDrive\", \"azureml.runsource\": \"hyperdrive\", \"platform\": \"AML\", \"baggage\": \"eyJvaWQiOiAiZDZjYjJhMGQtNzk1MS00MmZiLThkOGQtNjlmZWEzZTM1MWU2IiwgInRpZCI6ICI3MmY5ODhiZi04NmYxLTQxYWYtOTFhYi0yZDdjZDAxMWRiNDciLCAidW5hbWUiOiAiMDRiMDc3OTUtOGRkYi00NjFhLWJiZWUtMDJmOWUxYmY3YjQ2In0\", \"ContentSnapshotId\": \"28221d30-3646-4866-8a0b-b72a45db5a79\", \"score\": \"0.0014921563051374907\", \"best_child_run_id\": \"cnn_1576035422508121_74\", \"best_metric_status\": \"Succeeded\"}, \"tags\": {\"max_concurrent_jobs\": \"4\", \"max_total_jobs\": \"300\", \"max_duration_minutes\": \"180\", \"policy_config\": \"{\\\"name\\\": \\\"BANDIT\\\", \\\"properties\\\": {\\\"evaluation_interval\\\": 1, \\\"delay_evaluation\\\": 5, \\\"slack_factor\\\": 0.1}}\", \"generator_config\": \"{\\\"name\\\": \\\"RANDOM\\\", \\\"parameter_space\\\": {\\\"--latent-dim-1\\\": [\\\"choice\\\", [[5, 10, 15]]], \\\"--latent-dim-2\\\": [\\\"choice\\\", [[0, 5, 10]]], \\\"--kernel-size\\\": [\\\"choice\\\", [[3]]], \\\"--batch-size\\\": [\\\"choice\\\", [[16, 32]]], \\\"--T\\\": [\\\"choice\\\", [[72, 168, 336]]], \\\"--learning-rate\\\": [\\\"choice\\\", [[0.01, 0.001, 0.0001]]], \\\"--alpha\\\": [\\\"choice\\\", [[0.1, 0.001, 0]]]}}\", \"primary_metric_config\": \"{\\\"name\\\": \\\"Loss\\\", \\\"goal\\\": \\\"minimize\\\"}\", \"platform_config\": \"{\\\"ServiceAddress\\\": \\\"https://southcentralus.experiments.azureml.net\\\", \\\"ServiceArmScope\\\": \\\"subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn\\\", \\\"SubscriptionId\\\": \\\"6fa1b60b-c4be-4966-a446-261a3ad62d42\\\", \\\"ResourceGroupName\\\": \\\"AML2\\\", \\\"WorkspaceName\\\": \\\"AML2\\\", \\\"ExperimentName\\\": \\\"cnn\\\", \\\"Definition\\\": {\\\"Overrides\\\": {\\\"script\\\": \\\"CNN.py\\\", \\\"arguments\\\": [\\\"--datadir\\\", \\\"$AZUREML_DATAREFERENCE_bc05cd0424b44823ae2861964d34c82e\\\", \\\"--scriptdir\\\", \\\"$AZUREML_DATAREFERENCE_8f1bb2deb4dd4fe9a157ea3319691d2b\\\"], \\\"target\\\": \\\"gpucluster\\\", \\\"framework\\\": \\\"Python\\\", \\\"communicator\\\": \\\"None\\\", \\\"maxRunDurationSeconds\\\": null, \\\"nodeCount\\\": 1, \\\"environment\\\": {\\\"name\\\": null, \\\"version\\\": null, \\\"environmentVariables\\\": {\\\"EXAMPLE_ENV_VAR\\\": \\\"EXAMPLE_VALUE\\\"}, \\\"python\\\": {\\\"userManagedDependencies\\\": false, \\\"interpreterPath\\\": \\\"python\\\", \\\"condaDependenciesFile\\\": null, \\\"baseCondaEnvironment\\\": null, \\\"condaDependencies\\\": {\\\"name\\\": \\\"project_environment\\\", \\\"dependencies\\\": [\\\"python=3.6.2\\\", {\\\"pip\\\": [\\\"keras\\\", \\\"matplotlib\\\", \\\"scikit-learn\\\", \\\"xlrd\\\", \\\"azureml-sdk\\\", \\\"azureml-defaults\\\", \\\"tensorflow-gpu==1.13.1\\\", \\\"horovod==0.16.1\\\"]}, \\\"pandas\\\", \\\"numpy\\\"], \\\"channels\\\": [\\\"conda-forge\\\"]}}, \\\"docker\\\": {\\\"enabled\\\": true, \\\"baseImage\\\": \\\"mcr.microsoft.com/azureml/base-gpu:intelmpi2018.3-cuda10.0-cudnn7-ubuntu16.04\\\", \\\"baseDockerfile\\\": null, \\\"sharedVolumes\\\": true, \\\"shmSize\\\": \\\"2g\\\", \\\"arguments\\\": [], \\\"baseImageRegistry\\\": {\\\"address\\\": null, \\\"username\\\": null, \\\"password\\\": null}}, \\\"spark\\\": {\\\"repositories\\\": [], \\\"packages\\\": [], \\\"precachePackages\\\": false}, \\\"databricks\\\": {\\\"mavenLibraries\\\": [], \\\"pypiLibraries\\\": [], \\\"rcranLibraries\\\": [], \\\"jarLibraries\\\": [], \\\"eggLibraries\\\": []}, \\\"inferencingStackVersion\\\": null}, \\\"history\\\": {\\\"outputCollection\\\": true, \\\"snapshotProject\\\": true, \\\"directoriesToWatch\\\": [\\\"logs\\\"]}, \\\"spark\\\": {\\\"configuration\\\": {\\\"spark.app.name\\\": \\\"Azure ML Experiment\\\", \\\"spark.yarn.maxAppAttempts\\\": 1}}, \\\"hdi\\\": {\\\"yarnDeployMode\\\": \\\"cluster\\\"}, \\\"tensorflow\\\": {\\\"workerCount\\\": 1, \\\"parameterServerCount\\\": 1}, \\\"mpi\\\": {\\\"processCountPerNode\\\": 1}, \\\"dataReferences\\\": {\\\"bc05cd0424b44823ae2861964d34c82e\\\": {\\\"dataStoreName\\\": \\\"workspacefilestore\\\", \\\"pathOnDataStore\\\": \\\"energy\\\", \\\"mode\\\": \\\"mount\\\", \\\"overwrite\\\": false, \\\"pathOnCompute\\\": null}, \\\"8f1bb2deb4dd4fe9a157ea3319691d2b\\\": {\\\"dataStoreName\\\": \\\"workspacefilestore\\\", \\\"pathOnDataStore\\\": \\\"common\\\", \\\"mode\\\": \\\"mount\\\", \\\"overwrite\\\": false, \\\"pathOnCompute\\\": null}}, \\\"data\\\": {}, \\\"sourceDirectoryDataStore\\\": null, \\\"amlcompute\\\": {\\\"vmSize\\\": null, \\\"vmPriority\\\": null, \\\"retainCluster\\\": false, \\\"name\\\": null, \\\"clusterMaxNodeCount\\\": 1}}, \\\"TargetDetails\\\": null, \\\"SnapshotId\\\": \\\"28221d30-3646-4866-8a0b-b72a45db5a79\\\", \\\"TelemetryValues\\\": {\\\"amlClientType\\\": \\\"azureml-sdk-train\\\", \\\"amlClientModule\\\": \\\"azureml.train.hyperdrive._search\\\", \\\"amlClientFunction\\\": \\\"search\\\", \\\"tenantId\\\": \\\"72f988bf-86f1-41af-91ab-2d7cd011db47\\\", \\\"amlClientRequestId\\\": \\\"27cd986e-a31f-47f7-bf6e-793186e03eeb\\\", \\\"amlClientSessionId\\\": \\\"01d9d47a-dfb2-4f24-9ee1-a81504f41d92\\\", \\\"subscriptionId\\\": \\\"6fa1b60b-c4be-4966-a446-261a3ad62d42\\\", \\\"estimator\\\": \\\"TensorFlow\\\", \\\"samplingMethod\\\": \\\"RANDOM\\\", \\\"terminationPolicy\\\": \\\"Bandit\\\", \\\"primaryMetricGoal\\\": \\\"minimize\\\", \\\"maxTotalRuns\\\": 300, \\\"maxConcurrentRuns\\\": 4, \\\"maxDurationMinutes\\\": 180, \\\"computeTarget\\\": \\\"AmlCompute\\\", \\\"vmSize\\\": null}}}\", \"resume_child_runs\": \"null\", \"all_jobs_generated\": \"true\", \"cancellation_requested\": \"true\", \"progress_metadata_evaluation_timestamp\": \"\\\"2019-12-11T06:37:06.619856\\\"\", \"progress_metadata_digest\": \"\\\"2706d0e4bb19e54fcea074bdcb9a41de7507c1af50070d623e84aee723ed53b7\\\"\", \"progress_metadata_active_timestamp\": \"\\\"2019-12-11T06:37:06.619856\\\"\", \"cnn_1576035422508121_0\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_1\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_2\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_3\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"environment_preparation_status\": \"PREPARED\", \"prepare_run_id\": \"cnn_1576035422508121_preparation\", \"cnn_1576035422508121_4\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_0_cancelled\": \"true\", \"cnn_1576035422508121_5\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_6\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_7\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_8\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_9\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_10\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_8_cancelled\": \"true\", \"cnn_1576035422508121_11\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_12\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_13\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_14\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_15\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_16\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_12_cancelled\": \"true\", \"cnn_1576035422508121_17\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_18\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_15_cancelled\": \"true\", \"cnn_1576035422508121_16_cancelled\": \"true\", \"cnn_1576035422508121_19\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_20\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_21\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_22\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_23\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_19_cancelled\": \"true\", \"cnn_1576035422508121_24\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_25\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_26\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_23_cancelled\": \"true\", \"cnn_1576035422508121_27\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_24_cancelled\": \"true\", \"cnn_1576035422508121_28\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_29\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_27_cancelled\": \"true\", \"cnn_1576035422508121_30\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_31\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_32\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_33\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_34\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_35\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_36\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_37\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_38\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_39\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_40\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_41\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_38_cancelled\": \"true\", \"cnn_1576035422508121_42\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_43\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_44\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_40_cancelled\": \"true\", \"cnn_1576035422508121_45\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_42_cancelled\": \"true\", \"cnn_1576035422508121_46\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_47\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_48\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_49\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_50\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_47_cancelled\": \"true\", \"cnn_1576035422508121_51\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_52\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_53\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_50_cancelled\": \"true\", \"cnn_1576035422508121_54\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_55\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_56\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_53_cancelled\": \"true\", \"cnn_1576035422508121_57\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_54_cancelled\": \"true\", \"cnn_1576035422508121_58\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_59\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_60\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_61\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_62\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_63\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_60_cancelled\": \"true\", \"cnn_1576035422508121_64\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_65\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_63_cancelled\": \"true\", \"cnn_1576035422508121_66\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_62_cancelled\": \"true\", \"cnn_1576035422508121_67\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_68\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_69\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_70\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_71\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_68_cancelled\": \"true\", \"cnn_1576035422508121_72\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_73\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_70_cancelled\": \"true\", \"cnn_1576035422508121_74\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_75\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_76\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_73_cancelled\": \"true\", \"cnn_1576035422508121_77\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_74_cancelled\": \"true\", \"cnn_1576035422508121_78\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_79\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_80\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_81\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_82\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_83\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_84\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_81_cancelled\": \"true\", \"cnn_1576035422508121_85\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_86\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_83_cancelled\": \"true\", \"cnn_1576035422508121_87\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_88\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_89\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_90\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_87_cancelled\": \"true\", \"cnn_1576035422508121_91\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_92\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_93\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_90_cancelled\": \"true\", \"cnn_1576035422508121_94\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_91_cancelled\": \"true\", \"cnn_1576035422508121_95\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_92_cancelled\": \"true\", \"cnn_1576035422508121_96\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_93_cancelled\": \"true\", \"cnn_1576035422508121_94_cancelled\": \"true\", \"cnn_1576035422508121_97\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_98\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_99\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_100\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_97_cancelled\": \"true\", \"cnn_1576035422508121_99_cancelled\": \"true\", \"cnn_1576035422508121_101\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_102\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_103\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_98_cancelled\": \"true\", \"cnn_1576035422508121_104\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_105\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_106\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_107\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_108\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_109\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_106_cancelled\": \"true\", \"cnn_1576035422508121_110\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_111\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_112\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_109_cancelled\": \"true\", \"cnn_1576035422508121_111_cancelled\": \"true\", \"cnn_1576035422508121_113\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_114\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_110_cancelled\": \"true\", \"cnn_1576035422508121_115\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_116\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_113_cancelled\": \"true\", \"cnn_1576035422508121_117\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_118\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_115_cancelled\": \"true\", \"cnn_1576035422508121_119\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_116_cancelled\": \"true\", \"cnn_1576035422508121_120\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_117_cancelled\": \"true\", \"cnn_1576035422508121_118_cancelled\": \"true\", \"cnn_1576035422508121_121\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_122\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_119_cancelled\": \"true\", \"cnn_1576035422508121_123\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_120_cancelled\": \"true\", \"cnn_1576035422508121_124\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_125\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_122_cancelled\": \"true\", \"cnn_1576035422508121_123_cancelled\": \"true\", \"cnn_1576035422508121_126\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_127\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_128\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_125_cancelled\": \"true\", \"cnn_1576035422508121_129\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_130\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_131\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_128_cancelled\": \"true\", \"cnn_1576035422508121_129_cancelled\": \"true\", \"cnn_1576035422508121_132\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_133\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_130_cancelled\": \"true\", \"cnn_1576035422508121_134\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_131_cancelled\": \"true\", \"cnn_1576035422508121_135\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_132_cancelled\": \"true\", \"cnn_1576035422508121_133_cancelled\": \"true\", \"cnn_1576035422508121_136\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_137\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_138\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_139\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_136_cancelled\": \"true\", \"cnn_1576035422508121_140\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_141\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_137_cancelled\": \"true\", \"cnn_1576035422508121_142\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_143\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_144\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_145\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_146\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_147\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_148\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_149\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_150\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_151\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_147_cancelled\": \"true\", \"cnn_1576035422508121_152\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_149_cancelled\": \"true\", \"cnn_1576035422508121_153\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_154\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_155\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_152_cancelled\": \"true\", \"cnn_1576035422508121_156\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_157\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_155_cancelled\": \"true\", \"cnn_1576035422508121_158\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_159\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_160\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_161\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_158_cancelled\": \"true\", \"cnn_1576035422508121_162\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_163\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_164\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_165\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_166\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_167\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_168\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_165_cancelled\": \"true\", \"cnn_1576035422508121_169\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_167_cancelled\": \"true\", \"cnn_1576035422508121_170\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_168_cancelled\": \"true\", \"cnn_1576035422508121_171\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_172\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_173\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_174\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_175\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_176\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_177\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_174_cancelled\": \"true\", \"cnn_1576035422508121_178\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_179\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_180\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_177_cancelled\": \"true\", \"cnn_1576035422508121_181\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_178_cancelled\": \"true\", \"cnn_1576035422508121_182\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_183\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_184\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_185\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_186\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_187\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_188\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_189\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_190\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_191\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_192\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_189_cancelled\": \"true\", \"cnn_1576035422508121_193\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_194\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_195\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_196\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_197\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_198\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_194_cancelled\": \"true\", \"cnn_1576035422508121_199\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_200\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_196_cancelled\": \"true\", \"cnn_1576035422508121_201\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_198_cancelled\": \"true\", \"cnn_1576035422508121_202\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_203\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_199_cancelled\": \"true\", \"cnn_1576035422508121_204\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_201_cancelled\": \"true\", \"cnn_1576035422508121_205\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_202_cancelled\": \"true\", \"cnn_1576035422508121_203_cancelled\": \"true\", \"cnn_1576035422508121_206\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_207\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_208\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_205_cancelled\": \"true\", \"cnn_1576035422508121_209\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_210\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_211\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_212\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_213\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_214\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_210_cancelled\": \"true\", \"cnn_1576035422508121_215\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_216\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_217\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_218\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_219\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_216_cancelled\": \"true\", \"cnn_1576035422508121_220\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_221\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_222\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_219_cancelled\": \"true\", \"cnn_1576035422508121_223\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_222_cancelled\": \"true\", \"cnn_1576035422508121_224\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_225\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_226\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_227\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_228\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_229\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_230\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_231\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_232\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_229_cancelled\": \"true\", \"cnn_1576035422508121_233\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_234\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_230_cancelled\": \"true\", \"cnn_1576035422508121_235\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_236\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_237\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_238\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_239\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_240\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_241\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_242\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_243\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_244\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_245\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_246\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_247\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_248\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_249\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_246_cancelled\": \"true\", \"cnn_1576035422508121_250\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_251\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_248_cancelled\": \"true\", \"cnn_1576035422508121_252\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_253\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_254\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_251_cancelled\": \"true\", \"cnn_1576035422508121_253_cancelled\": \"true\", \"cnn_1576035422508121_255\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_256\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_252_cancelled\": \"true\", \"cnn_1576035422508121_254_cancelled\": \"true\", \"cnn_1576035422508121_257\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_258\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_259\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_260\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_257_cancelled\": \"true\", \"cnn_1576035422508121_261\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_262\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_259_cancelled\": \"true\", \"cnn_1576035422508121_263\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_260_cancelled\": \"true\", \"cnn_1576035422508121_264\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_262_cancelled\": \"true\", \"cnn_1576035422508121_265\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_261_cancelled\": \"true\", \"cnn_1576035422508121_266\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_267\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_268\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_269\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_270\": \"{\\\"--T\\\": 72, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_271\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_268_cancelled\": \"true\", \"cnn_1576035422508121_272\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 10, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_273\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_274\": \"{\\\"--T\\\": 336, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_275\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 10, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_272_cancelled\": \"true\", \"cnn_1576035422508121_273_cancelled\": \"true\", \"cnn_1576035422508121_276\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_277\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.001, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 5, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.0001}\", \"cnn_1576035422508121_274_cancelled\": \"true\", \"cnn_1576035422508121_278\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0, \\\"--batch-size\\\": 32, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 5, \\\"--learning-rate\\\": 0.01}\", \"cnn_1576035422508121_275_cancelled\": \"true\", \"cnn_1576035422508121_279\": \"{\\\"--T\\\": 168, \\\"--alpha\\\": 0.1, \\\"--batch-size\\\": 16, \\\"--kernel-size\\\": 3, \\\"--latent-dim-1\\\": 15, \\\"--latent-dim-2\\\": 0, \\\"--learning-rate\\\": 0.001}\", \"cnn_1576035422508121_276_cancelled\": \"true\", \"cnn_1576035422508121_277_cancelled\": \"true\", \"cnn_1576035422508121_278_cancelled\": \"true\", \"cnn_1576035422508121_279_cancelled\": \"true\"}, \"end_time_utc\": \"2019-12-11T06:37:39.098666Z\", \"status\": \"Completed\", \"log_files\": {\"azureml-logs/hyperdrive.txt\": \"https://aml23782003686.blob.core.windows.net/azureml/ExperimentRun/dcid.cnn_1576035422508121/azureml-logs/hyperdrive.txt?sv=2019-02-02&sr=b&sig=dtrlo62ndKkLpiI%2BRb2GkZp5d%2FJnwP6YrAhIj%2BX1bhU%3D&st=2019-12-11T14%3A17%3A55Z&se=2019-12-11T22%3A27%3A55Z&sp=r\"}, \"log_groups\": [[\"azureml-logs/hyperdrive.txt\"]], \"run_duration\": \"3:00:36\", \"hyper_parameters\": {\"--latent-dim-1\": [\"choice\", [[5, 10, 15]]], \"--latent-dim-2\": [\"choice\", [[0, 5, 10]]], \"--kernel-size\": [\"choice\", [[3]]], \"--batch-size\": [\"choice\", [[16, 32]]], \"--T\": [\"choice\", [[72, 168, 336]]], \"--learning-rate\": [\"choice\", [[0.01, 0.001, 0.0001]]], \"--alpha\": [\"choice\", [[0.1, 0.001, 0]]]}}, \"child_runs\": [{\"run_id\": \"cnn_1576035422508121_0\", \"run_number\": 21, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:48:15.111248Z\", \"end_time\": \"2019-12-11T03:52:27.19032Z\", \"created_time\": \"2019-12-11T03:37:34.411876Z\", \"created_time_dt\": \"2019-12-11T03:37:34.411876Z\", \"duration\": \"0:14:52\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.22413187966067272}, {\"run_id\": \"cnn_1576035422508121_3\", \"run_number\": 22, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:48:15.690317Z\", \"end_time\": \"2019-12-11T03:53:11.307134Z\", \"created_time\": \"2019-12-11T03:37:34.477105Z\", \"created_time_dt\": \"2019-12-11T03:37:34.477105Z\", \"duration\": \"0:15:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0029059401590500783}, {\"run_id\": \"cnn_1576035422508121_2\", \"run_number\": 23, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:48:15.104771Z\", \"end_time\": \"2019-12-11T03:51:51.544661Z\", \"created_time\": \"2019-12-11T03:37:34.623725Z\", \"created_time_dt\": \"2019-12-11T03:37:34.623725Z\", \"duration\": \"0:14:16\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007106514356456776}, {\"run_id\": \"cnn_1576035422508121_1\", \"run_number\": 24, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:48:44.807779Z\", \"end_time\": \"2019-12-11T03:52:29.239431Z\", \"created_time\": \"2019-12-11T03:37:34.572079Z\", \"created_time_dt\": \"2019-12-11T03:37:34.572079Z\", \"duration\": \"0:14:54\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.05817225834878085}, {\"run_id\": \"cnn_1576035422508121_4\", \"run_number\": 25, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:52:25.536153Z\", \"end_time\": \"2019-12-11T03:53:31.303517Z\", \"created_time\": \"2019-12-11T03:52:14.922108Z\", \"created_time_dt\": \"2019-12-11T03:52:14.922108Z\", \"duration\": \"0:01:16\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.020610862465104154}, {\"run_id\": \"cnn_1576035422508121_6\", \"run_number\": 26, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:52:56.338003Z\", \"end_time\": \"2019-12-11T03:56:41.649847Z\", \"created_time\": \"2019-12-11T03:52:45.586818Z\", \"created_time_dt\": \"2019-12-11T03:52:45.586818Z\", \"duration\": \"0:03:56\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0017414035854717736}, {\"run_id\": \"cnn_1576035422508121_5\", \"run_number\": 27, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:52:56.782873Z\", \"end_time\": \"2019-12-11T03:54:16.026265Z\", \"created_time\": \"2019-12-11T03:52:45.606706Z\", \"created_time_dt\": \"2019-12-11T03:52:45.606706Z\", \"duration\": \"0:01:30\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.005289679083347334}, {\"run_id\": \"cnn_1576035422508121_7\", \"run_number\": 28, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:53:56.971175Z\", \"end_time\": \"2019-12-11T03:55:15.7228Z\", \"created_time\": \"2019-12-11T03:53:46.546122Z\", \"created_time_dt\": \"2019-12-11T03:53:46.546122Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.006431415790800701}, {\"run_id\": \"cnn_1576035422508121_8\", \"run_number\": 29, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:53:57.830212Z\", \"end_time\": \"2019-12-11T03:56:27.153379Z\", \"created_time\": \"2019-12-11T03:53:46.591207Z\", \"created_time_dt\": \"2019-12-11T03:53:46.591207Z\", \"duration\": \"0:02:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0028719150971581752}, {\"run_id\": \"cnn_1576035422508121_9\", \"run_number\": 30, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:54:58.677479Z\", \"end_time\": \"2019-12-11T03:56:31.372967Z\", \"created_time\": \"2019-12-11T03:54:47.953114Z\", \"created_time_dt\": \"2019-12-11T03:54:47.953114Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.02356652807815455}, {\"run_id\": \"cnn_1576035422508121_10\", \"run_number\": 31, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:55:59.423504Z\", \"end_time\": \"2019-12-11T03:57:11.549136Z\", \"created_time\": \"2019-12-11T03:55:48.898479Z\", \"created_time_dt\": \"2019-12-11T03:55:48.898479Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.0073065431433869815}, {\"run_id\": \"cnn_1576035422508121_12\", \"run_number\": 32, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:57:15.922885Z\", \"end_time\": \"2019-12-11T03:59:44.078211Z\", \"created_time\": \"2019-12-11T03:56:50.118219Z\", \"created_time_dt\": \"2019-12-11T03:56:50.118219Z\", \"duration\": \"0:02:53\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.006536608317091732}, {\"run_id\": \"cnn_1576035422508121_11\", \"run_number\": 33, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:57:00.795219Z\", \"end_time\": \"2019-12-11T03:58:48.469298Z\", \"created_time\": \"2019-12-11T03:56:50.233436Z\", \"created_time_dt\": \"2019-12-11T03:56:50.233436Z\", \"duration\": \"0:01:58\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.005342740123637193}, {\"run_id\": \"cnn_1576035422508121_13\", \"run_number\": 34, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:57:31.543454Z\", \"end_time\": \"2019-12-11T03:58:50.62532Z\", \"created_time\": \"2019-12-11T03:57:21.093051Z\", \"created_time_dt\": \"2019-12-11T03:57:21.093051Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.10847739190967944}, {\"run_id\": \"cnn_1576035422508121_14\", \"run_number\": 35, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:58:03.327907Z\", \"end_time\": \"2019-12-11T03:59:28.963005Z\", \"created_time\": \"2019-12-11T03:57:52.852848Z\", \"created_time_dt\": \"2019-12-11T03:57:52.852848Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.061761667736072286}, {\"run_id\": \"cnn_1576035422508121_16\", \"run_number\": 36, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:59:34.959894Z\", \"end_time\": \"2019-12-11T04:01:35.062529Z\", \"created_time\": \"2019-12-11T03:59:24.373054Z\", \"created_time_dt\": \"2019-12-11T03:59:24.373054Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.024843308993877367}, {\"run_id\": \"cnn_1576035422508121_15\", \"run_number\": 37, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T03:59:34.870445Z\", \"end_time\": \"2019-12-11T04:01:34.902978Z\", \"created_time\": \"2019-12-11T03:59:24.453351Z\", \"created_time_dt\": \"2019-12-11T03:59:24.453351Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.005878983058165305}, {\"run_id\": \"cnn_1576035422508121_17\", \"run_number\": 38, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:00:05.496742Z\", \"end_time\": \"2019-12-11T04:02:00.586133Z\", \"created_time\": \"2019-12-11T03:59:55.185234Z\", \"created_time_dt\": \"2019-12-11T03:59:55.185234Z\", \"duration\": \"0:02:05\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0020779614683672626}, {\"run_id\": \"cnn_1576035422508121_18\", \"run_number\": 39, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:00:36.663182Z\", \"end_time\": \"2019-12-11T04:01:55.652007Z\", \"created_time\": \"2019-12-11T04:00:25.911514Z\", \"created_time_dt\": \"2019-12-11T04:00:25.911514Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.004808689557126866}, {\"run_id\": \"cnn_1576035422508121_20\", \"run_number\": 40, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:02:08.001882Z\", \"end_time\": \"2019-12-11T04:03:34.399744Z\", \"created_time\": \"2019-12-11T04:01:57.615363Z\", \"created_time_dt\": \"2019-12-11T04:01:57.615363Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.22647066125551957}, {\"run_id\": \"cnn_1576035422508121_19\", \"run_number\": 41, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:02:08.161895Z\", \"end_time\": \"2019-12-11T04:04:04.672498Z\", \"created_time\": \"2019-12-11T04:01:57.705166Z\", \"created_time_dt\": \"2019-12-11T04:01:57.705166Z\", \"duration\": \"0:02:06\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.002738739366904434}, {\"run_id\": \"cnn_1576035422508121_21\", \"run_number\": 42, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:02:38.930971Z\", \"end_time\": \"2019-12-11T04:03:51.042579Z\", \"created_time\": \"2019-12-11T04:02:28.340228Z\", \"created_time_dt\": \"2019-12-11T04:02:28.340228Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.22916587034201763}, {\"run_id\": \"cnn_1576035422508121_22\", \"run_number\": 43, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:02:38.721396Z\", \"end_time\": \"2019-12-11T04:04:04.408109Z\", \"created_time\": \"2019-12-11T04:02:28.397703Z\", \"created_time_dt\": \"2019-12-11T04:02:28.397703Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.05937609517515041}, {\"run_id\": \"cnn_1576035422508121_23\", \"run_number\": 44, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:04:11.46156Z\", \"end_time\": \"2019-12-11T04:06:11.519874Z\", \"created_time\": \"2019-12-11T04:04:00.62166Z\", \"created_time_dt\": \"2019-12-11T04:04:00.62166Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007537388185405509}, {\"run_id\": \"cnn_1576035422508121_25\", \"run_number\": 45, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:04:41.892417Z\", \"end_time\": \"2019-12-11T04:06:53.23443Z\", \"created_time\": \"2019-12-11T04:04:31.468939Z\", \"created_time_dt\": \"2019-12-11T04:04:31.468939Z\", \"duration\": \"0:02:21\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.15506146720431604}, {\"run_id\": \"cnn_1576035422508121_26\", \"run_number\": 46, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:04:41.729614Z\", \"end_time\": \"2019-12-11T04:08:20.12823Z\", \"created_time\": \"2019-12-11T04:04:31.492433Z\", \"created_time_dt\": \"2019-12-11T04:04:31.492433Z\", \"duration\": \"0:03:48\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.001527197303406981}, {\"run_id\": \"cnn_1576035422508121_24\", \"run_number\": 47, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:04:42.275328Z\", \"end_time\": \"2019-12-11T04:06:42.658909Z\", \"created_time\": \"2019-12-11T04:04:31.516817Z\", \"created_time_dt\": \"2019-12-11T04:04:31.516817Z\", \"duration\": \"0:02:11\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0067629750459602365}, {\"run_id\": \"cnn_1576035422508121_27\", \"run_number\": 48, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:06:44.596443Z\", \"end_time\": \"2019-12-11T04:08:45.465426Z\", \"created_time\": \"2019-12-11T04:06:34.082861Z\", \"created_time_dt\": \"2019-12-11T04:06:34.082861Z\", \"duration\": \"0:02:11\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.004547393438490248}, {\"run_id\": \"cnn_1576035422508121_28\", \"run_number\": 49, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:07:15.196775Z\", \"end_time\": \"2019-12-11T04:08:55.205309Z\", \"created_time\": \"2019-12-11T04:07:04.979022Z\", \"created_time_dt\": \"2019-12-11T04:07:04.979022Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.05695115713140514}, {\"run_id\": \"cnn_1576035422508121_29\", \"run_number\": 50, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:07:46.900372Z\", \"end_time\": \"2019-12-11T04:09:12.36172Z\", \"created_time\": \"2019-12-11T04:07:35.716363Z\", \"created_time_dt\": \"2019-12-11T04:07:35.716363Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.32797380582403857}, {\"run_id\": \"cnn_1576035422508121_30\", \"run_number\": 51, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:09:17.781361Z\", \"end_time\": \"2019-12-11T04:10:29.784307Z\", \"created_time\": \"2019-12-11T04:09:07.439031Z\", \"created_time_dt\": \"2019-12-11T04:09:07.439031Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.022458058754319415}, {\"run_id\": \"cnn_1576035422508121_31\", \"run_number\": 52, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:09:17.676983Z\", \"end_time\": \"2019-12-11T04:10:43.20295Z\", \"created_time\": \"2019-12-11T04:09:07.477457Z\", \"created_time_dt\": \"2019-12-11T04:09:07.477457Z\", \"duration\": \"0:01:35\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.024077694944222735}, {\"run_id\": \"cnn_1576035422508121_32\", \"run_number\": 53, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:09:49.237694Z\", \"end_time\": \"2019-12-11T04:11:08.485405Z\", \"created_time\": \"2019-12-11T04:09:38.104403Z\", \"created_time_dt\": \"2019-12-11T04:09:38.104403Z\", \"duration\": \"0:01:30\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.058173411413095934}, {\"run_id\": \"cnn_1576035422508121_33\", \"run_number\": 54, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:09:50.156445Z\", \"end_time\": \"2019-12-11T04:11:30.201008Z\", \"created_time\": \"2019-12-11T04:09:39.882515Z\", \"created_time_dt\": \"2019-12-11T04:09:39.882515Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.040936152733208206}, {\"run_id\": \"cnn_1576035422508121_34\", \"run_number\": 55, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:11:21.753583Z\", \"end_time\": \"2019-12-11T04:13:01.676046Z\", \"created_time\": \"2019-12-11T04:11:11.253872Z\", \"created_time_dt\": \"2019-12-11T04:11:11.253872Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.05819567123201647}, {\"run_id\": \"cnn_1576035422508121_35\", \"run_number\": 56, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:11:21.647092Z\", \"end_time\": \"2019-12-11T04:13:16.992458Z\", \"created_time\": \"2019-12-11T04:11:11.308593Z\", \"created_time_dt\": \"2019-12-11T04:11:11.308593Z\", \"duration\": \"0:02:05\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.03500164560198385}, {\"run_id\": \"cnn_1576035422508121_36\", \"run_number\": 57, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:11:57.482315Z\", \"end_time\": \"2019-12-11T04:13:29.7026Z\", \"created_time\": \"2019-12-11T04:11:42.140138Z\", \"created_time_dt\": \"2019-12-11T04:11:42.140138Z\", \"duration\": \"0:01:47\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.1148115495177183}, {\"run_id\": \"cnn_1576035422508121_37\", \"run_number\": 58, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:12:24.342937Z\", \"end_time\": \"2019-12-11T04:13:43.569335Z\", \"created_time\": \"2019-12-11T04:12:12.97371Z\", \"created_time_dt\": \"2019-12-11T04:12:12.97371Z\", \"duration\": \"0:01:30\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007171524431799889}, {\"run_id\": \"cnn_1576035422508121_39\", \"run_number\": 59, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:13:55.433344Z\", \"end_time\": \"2019-12-11T04:15:35.143555Z\", \"created_time\": \"2019-12-11T04:13:44.436012Z\", \"created_time_dt\": \"2019-12-11T04:13:44.436012Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.059980308375039544}, {\"run_id\": \"cnn_1576035422508121_38\", \"run_number\": 60, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:13:55.39241Z\", \"end_time\": \"2019-12-11T04:15:55.687512Z\", \"created_time\": \"2019-12-11T04:13:45.087444Z\", \"created_time_dt\": \"2019-12-11T04:13:45.087444Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.005721853474891772}, {\"run_id\": \"cnn_1576035422508121_41\", \"run_number\": 61, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:14:26.412077Z\", \"end_time\": \"2019-12-11T04:15:51.856629Z\", \"created_time\": \"2019-12-11T04:14:16.32081Z\", \"created_time_dt\": \"2019-12-11T04:14:16.32081Z\", \"duration\": \"0:01:35\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0017268186240333076}, {\"run_id\": \"cnn_1576035422508121_40\", \"run_number\": 62, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:14:26.852475Z\", \"end_time\": \"2019-12-11T04:16:56.361501Z\", \"created_time\": \"2019-12-11T04:14:16.58631Z\", \"created_time_dt\": \"2019-12-11T04:14:16.58631Z\", \"duration\": \"0:02:39\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.005555269872303349}, {\"run_id\": \"cnn_1576035422508121_42\", \"run_number\": 63, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:16:29.44348Z\", \"end_time\": \"2019-12-11T04:18:29.555942Z\", \"created_time\": \"2019-12-11T04:16:18.816407Z\", \"created_time_dt\": \"2019-12-11T04:16:18.816407Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.001811288416960176}, {\"run_id\": \"cnn_1576035422508121_44\", \"run_number\": 64, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:17:00.653003Z\", \"end_time\": \"2019-12-11T04:18:19.645242Z\", \"created_time\": \"2019-12-11T04:16:50.469294Z\", \"created_time_dt\": \"2019-12-11T04:16:50.469294Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.016580273673171327}, {\"run_id\": \"cnn_1576035422508121_43\", \"run_number\": 65, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:17:00.912762Z\", \"end_time\": \"2019-12-11T04:18:13.007593Z\", \"created_time\": \"2019-12-11T04:16:50.492605Z\", \"created_time_dt\": \"2019-12-11T04:16:50.492605Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.0056045679237317035}, {\"run_id\": \"cnn_1576035422508121_45\", \"run_number\": 66, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:17:32.371929Z\", \"end_time\": \"2019-12-11T04:18:44.517414Z\", \"created_time\": \"2019-12-11T04:17:21.229767Z\", \"created_time_dt\": \"2019-12-11T04:17:21.229767Z\", \"duration\": \"0:01:23\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.007028574753630603}, {\"run_id\": \"cnn_1576035422508121_46\", \"run_number\": 67, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:19:03.373581Z\", \"end_time\": \"2019-12-11T04:20:29.17812Z\", \"created_time\": \"2019-12-11T04:18:52.940648Z\", \"created_time_dt\": \"2019-12-11T04:18:52.940648Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001}, {\"run_id\": \"cnn_1576035422508121_47\", \"run_number\": 68, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:19:03.478438Z\", \"end_time\": \"2019-12-11T04:21:03.519921Z\", \"created_time\": \"2019-12-11T04:18:52.955704Z\", \"created_time_dt\": \"2019-12-11T04:18:52.955704Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.11564231035406608}, {\"run_id\": \"cnn_1576035422508121_48\", \"run_number\": 69, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:19:07.783099Z\", \"end_time\": \"2019-12-11T04:20:50.675437Z\", \"created_time\": \"2019-12-11T04:18:52.957302Z\", \"created_time_dt\": \"2019-12-11T04:18:52.957302Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.3342519811581855}, {\"run_id\": \"cnn_1576035422508121_49\", \"run_number\": 70, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:19:34.855931Z\", \"end_time\": \"2019-12-11T04:21:22.149898Z\", \"created_time\": \"2019-12-11T04:19:24.647321Z\", \"created_time_dt\": \"2019-12-11T04:19:24.647321Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.15396478664895596}, {\"run_id\": \"cnn_1576035422508121_50\", \"run_number\": 71, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:21:07.390922Z\", \"end_time\": \"2019-12-11T04:22:34.7163Z\", \"created_time\": \"2019-12-11T04:20:56.588807Z\", \"created_time_dt\": \"2019-12-11T04:20:56.588807Z\", \"duration\": \"0:01:38\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.00448313701089664}, {\"run_id\": \"cnn_1576035422508121_51\", \"run_number\": 72, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:21:38.316307Z\", \"end_time\": \"2019-12-11T04:23:04.353603Z\", \"created_time\": \"2019-12-11T04:21:27.244947Z\", \"created_time_dt\": \"2019-12-11T04:21:27.244947Z\", \"duration\": \"0:01:37\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.020048756338967976}, {\"run_id\": \"cnn_1576035422508121_53\", \"run_number\": 73, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:22:08.378067Z\", \"end_time\": \"2019-12-11T04:24:08.628443Z\", \"created_time\": \"2019-12-11T04:21:58.017081Z\", \"created_time_dt\": \"2019-12-11T04:21:58.017081Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0020659926777994693}, {\"run_id\": \"cnn_1576035422508121_52\", \"run_number\": 74, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:22:08.326992Z\", \"end_time\": \"2019-12-11T04:23:20.22784Z\", \"created_time\": \"2019-12-11T04:21:58.276181Z\", \"created_time_dt\": \"2019-12-11T04:21:58.276181Z\", \"duration\": \"0:01:21\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0736879717554942}, {\"run_id\": \"cnn_1576035422508121_54\", \"run_number\": 75, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:23:41.321806Z\", \"end_time\": \"2019-12-11T04:25:11.988777Z\", \"created_time\": \"2019-12-11T04:23:30.683256Z\", \"created_time_dt\": \"2019-12-11T04:23:30.683256Z\", \"duration\": \"0:01:41\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0600825878528202}, {\"run_id\": \"cnn_1576035422508121_55\", \"run_number\": 76, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:24:12.617993Z\", \"end_time\": \"2019-12-11T04:25:49.763021Z\", \"created_time\": \"2019-12-11T04:24:01.934439Z\", \"created_time_dt\": \"2019-12-11T04:24:01.934439Z\", \"duration\": \"0:01:47\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.00519257232554387}, {\"run_id\": \"cnn_1576035422508121_56\", \"run_number\": 77, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:24:12.548234Z\", \"end_time\": \"2019-12-11T04:25:24.698815Z\", \"created_time\": \"2019-12-11T04:24:01.945277Z\", \"created_time_dt\": \"2019-12-11T04:24:01.945277Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0028064894968546417}, {\"run_id\": \"cnn_1576035422508121_57\", \"run_number\": 78, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:24:43.616581Z\", \"end_time\": \"2019-12-11T04:27:21.68921Z\", \"created_time\": \"2019-12-11T04:24:32.881448Z\", \"created_time_dt\": \"2019-12-11T04:24:32.881448Z\", \"duration\": \"0:02:48\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.001646545816153777}, {\"run_id\": \"cnn_1576035422508121_58\", \"run_number\": 79, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:25:45.800227Z\", \"end_time\": \"2019-12-11T04:27:18.107117Z\", \"created_time\": \"2019-12-11T04:25:35.447782Z\", \"created_time_dt\": \"2019-12-11T04:25:35.447782Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.03078053675727252}, {\"run_id\": \"cnn_1576035422508121_59\", \"run_number\": 80, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:25:45.790426Z\", \"end_time\": \"2019-12-11T04:27:04.436979Z\", \"created_time\": \"2019-12-11T04:25:35.457749Z\", \"created_time_dt\": \"2019-12-11T04:25:35.457749Z\", \"duration\": \"0:01:28\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.004883573212567681}, {\"run_id\": \"cnn_1576035422508121_60\", \"run_number\": 81, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:26:16.629633Z\", \"end_time\": \"2019-12-11T04:27:43.729725Z\", \"created_time\": \"2019-12-11T04:26:06.406469Z\", \"created_time_dt\": \"2019-12-11T04:26:06.406469Z\", \"duration\": \"0:01:37\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0521919116306293}, {\"run_id\": \"cnn_1576035422508121_61\", \"run_number\": 82, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:27:49.672237Z\", \"end_time\": \"2019-12-11T04:29:15.497914Z\", \"created_time\": \"2019-12-11T04:27:38.67678Z\", \"created_time_dt\": \"2019-12-11T04:27:38.67678Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01}, {\"run_id\": \"cnn_1576035422508121_62\", \"run_number\": 83, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:27:49.758062Z\", \"end_time\": \"2019-12-11T04:30:18.85762Z\", \"created_time\": \"2019-12-11T04:27:38.750822Z\", \"created_time_dt\": \"2019-12-11T04:27:38.750822Z\", \"duration\": \"0:02:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007335491237741804}, {\"run_id\": \"cnn_1576035422508121_63\", \"run_number\": 84, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:27:53.906317Z\", \"end_time\": \"2019-12-11T04:29:49.482076Z\", \"created_time\": \"2019-12-11T04:27:38.77228Z\", \"created_time_dt\": \"2019-12-11T04:27:38.77228Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.15530867274076948}, {\"run_id\": \"cnn_1576035422508121_64\", \"run_number\": 85, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:28:25.135314Z\", \"end_time\": \"2019-12-11T04:30:23.808766Z\", \"created_time\": \"2019-12-11T04:28:10.504497Z\", \"created_time_dt\": \"2019-12-11T04:28:10.504497Z\", \"duration\": \"0:02:13\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0016155656466410383}, {\"run_id\": \"cnn_1576035422508121_65\", \"run_number\": 86, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:29:52.936952Z\", \"end_time\": \"2019-12-11T04:31:32.834344Z\", \"created_time\": \"2019-12-11T04:29:42.453058Z\", \"created_time_dt\": \"2019-12-11T04:29:42.453058Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007142919216848105}, {\"run_id\": \"cnn_1576035422508121_66\", \"run_number\": 87, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:30:24.039937Z\", \"end_time\": \"2019-12-11T04:31:56.62069Z\", \"created_time\": \"2019-12-11T04:30:13.657806Z\", \"created_time_dt\": \"2019-12-11T04:30:13.657806Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.2326143695035144}, {\"run_id\": \"cnn_1576035422508121_68\", \"run_number\": 88, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:30:59.797397Z\", \"end_time\": \"2019-12-11T04:32:56.132076Z\", \"created_time\": \"2019-12-11T04:30:44.419651Z\", \"created_time_dt\": \"2019-12-11T04:30:44.419651Z\", \"duration\": \"0:02:11\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.005936361565191362}, {\"run_id\": \"cnn_1576035422508121_67\", \"run_number\": 89, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:30:59.497535Z\", \"end_time\": \"2019-12-11T04:32:13.807514Z\", \"created_time\": \"2019-12-11T04:30:44.513364Z\", \"created_time_dt\": \"2019-12-11T04:30:44.513364Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.005781642908455964}, {\"run_id\": \"cnn_1576035422508121_70\", \"run_number\": 90, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:32:27.449703Z\", \"end_time\": \"2019-12-11T04:34:22.886429Z\", \"created_time\": \"2019-12-11T04:32:16.735835Z\", \"created_time_dt\": \"2019-12-11T04:32:16.735835Z\", \"duration\": \"0:02:06\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.002330840195451436}, {\"run_id\": \"cnn_1576035422508121_69\", \"run_number\": 91, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:32:27.343004Z\", \"end_time\": \"2019-12-11T04:34:00.302328Z\", \"created_time\": \"2019-12-11T04:32:16.887455Z\", \"created_time_dt\": \"2019-12-11T04:32:16.887455Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007383124391799118}, {\"run_id\": \"cnn_1576035422508121_71\", \"run_number\": 92, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:32:58.673175Z\", \"end_time\": \"2019-12-11T04:34:31.320912Z\", \"created_time\": \"2019-12-11T04:32:48.077231Z\", \"created_time_dt\": \"2019-12-11T04:32:48.077231Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007347825920781899}, {\"run_id\": \"cnn_1576035422508121_72\", \"run_number\": 93, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:33:29.381508Z\", \"end_time\": \"2019-12-11T04:34:41.577015Z\", \"created_time\": \"2019-12-11T04:33:18.941578Z\", \"created_time_dt\": \"2019-12-11T04:33:18.941578Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.02239200498258821}, {\"run_id\": \"cnn_1576035422508121_73\", \"run_number\": 94, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:34:31.688341Z\", \"end_time\": \"2019-12-11T04:36:33.018887Z\", \"created_time\": \"2019-12-11T04:34:20.615059Z\", \"created_time_dt\": \"2019-12-11T04:34:20.615059Z\", \"duration\": \"0:02:12\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.001955593157174411}, {\"run_id\": \"cnn_1576035422508121_75\", \"run_number\": 95, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:35:01.435409Z\", \"end_time\": \"2019-12-11T04:36:26.821388Z\", \"created_time\": \"2019-12-11T04:34:51.340588Z\", \"created_time_dt\": \"2019-12-11T04:34:51.340588Z\", \"duration\": \"0:01:35\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.004535114380728043}, {\"run_id\": \"cnn_1576035422508121_74\", \"run_number\": 96, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:35:01.894934Z\", \"end_time\": \"2019-12-11T04:37:02.101861Z\", \"created_time\": \"2019-12-11T04:34:51.37799Z\", \"created_time_dt\": \"2019-12-11T04:34:51.37799Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0014921563051374907}, {\"run_id\": \"cnn_1576035422508121_76\", \"run_number\": 97, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:35:34.156426Z\", \"end_time\": \"2019-12-11T04:37:06.674878Z\", \"created_time\": \"2019-12-11T04:35:23.465213Z\", \"created_time_dt\": \"2019-12-11T04:35:23.465213Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.003079935575048017}, {\"run_id\": \"cnn_1576035422508121_77\", \"run_number\": 98, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:37:06.979557Z\", \"end_time\": \"2019-12-11T04:38:46.608981Z\", \"created_time\": \"2019-12-11T04:36:56.606401Z\", \"created_time_dt\": \"2019-12-11T04:36:56.606401Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.022761660627009205}, {\"run_id\": \"cnn_1576035422508121_78\", \"run_number\": 99, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:37:37.695604Z\", \"end_time\": \"2019-12-11T04:39:57.408171Z\", \"created_time\": \"2019-12-11T04:37:27.459502Z\", \"created_time_dt\": \"2019-12-11T04:37:27.459502Z\", \"duration\": \"0:02:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.001497901016791068}, {\"run_id\": \"cnn_1576035422508121_80\", \"run_number\": 100, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:38:08.548893Z\", \"end_time\": \"2019-12-11T04:40:03.453099Z\", \"created_time\": \"2019-12-11T04:37:58.288846Z\", \"created_time_dt\": \"2019-12-11T04:37:58.288846Z\", \"duration\": \"0:02:05\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.11641764644296415}, {\"run_id\": \"cnn_1576035422508121_79\", \"run_number\": 101, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:38:08.625283Z\", \"end_time\": \"2019-12-11T04:39:27.631341Z\", \"created_time\": \"2019-12-11T04:37:58.355649Z\", \"created_time_dt\": \"2019-12-11T04:37:58.355649Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.023474579264809725}, {\"run_id\": \"cnn_1576035422508121_81\", \"run_number\": 102, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:39:40.39081Z\", \"end_time\": \"2019-12-11T04:41:10.796977Z\", \"created_time\": \"2019-12-11T04:39:29.898628Z\", \"created_time_dt\": \"2019-12-11T04:39:29.898628Z\", \"duration\": \"0:01:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0018969921083682986}, {\"run_id\": \"cnn_1576035422508121_82\", \"run_number\": 103, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:40:15.315421Z\", \"end_time\": \"2019-12-11T04:41:43.637806Z\", \"created_time\": \"2019-12-11T04:40:00.76438Z\", \"created_time_dt\": \"2019-12-11T04:40:00.76438Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.006967784217045131}, {\"run_id\": \"cnn_1576035422508121_83\", \"run_number\": 104, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:40:41.963083Z\", \"end_time\": \"2019-12-11T04:42:12.372038Z\", \"created_time\": \"2019-12-11T04:40:31.528346Z\", \"created_time_dt\": \"2019-12-11T04:40:31.528346Z\", \"duration\": \"0:01:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.004555898075851705}, {\"run_id\": \"cnn_1576035422508121_84\", \"run_number\": 105, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:41:14.020164Z\", \"end_time\": \"2019-12-11T04:42:53.825629Z\", \"created_time\": \"2019-12-11T04:41:03.877219Z\", \"created_time_dt\": \"2019-12-11T04:41:03.877219Z\", \"duration\": \"0:01:49\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.00761953046204774}, {\"run_id\": \"cnn_1576035422508121_85\", \"run_number\": 106, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:41:45.143275Z\", \"end_time\": \"2019-12-11T04:43:03.82124Z\", \"created_time\": \"2019-12-11T04:41:34.826616Z\", \"created_time_dt\": \"2019-12-11T04:41:34.826616Z\", \"duration\": \"0:01:28\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.047755043659940065}, {\"run_id\": \"cnn_1576035422508121_86\", \"run_number\": 107, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:42:16.363046Z\", \"end_time\": \"2019-12-11T04:44:03.808203Z\", \"created_time\": \"2019-12-11T04:42:05.948068Z\", \"created_time_dt\": \"2019-12-11T04:42:05.948068Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.040946687393872236}, {\"run_id\": \"cnn_1576035422508121_87\", \"run_number\": 108, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:42:47.530199Z\", \"end_time\": \"2019-12-11T04:44:19.273984Z\", \"created_time\": \"2019-12-11T04:42:36.763751Z\", \"created_time_dt\": \"2019-12-11T04:42:36.763751Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.006851420427921854}, {\"run_id\": \"cnn_1576035422508121_88\", \"run_number\": 109, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:43:18.077902Z\", \"end_time\": \"2019-12-11T04:44:30.143973Z\", \"created_time\": \"2019-12-11T04:43:07.73492Z\", \"created_time_dt\": \"2019-12-11T04:43:07.73492Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.062301642068352245}, {\"run_id\": \"cnn_1576035422508121_89\", \"run_number\": 110, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:43:49.862239Z\", \"end_time\": \"2019-12-11T04:45:22.352851Z\", \"created_time\": \"2019-12-11T04:43:39.48923Z\", \"created_time_dt\": \"2019-12-11T04:43:39.48923Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.06214200360284737}, {\"run_id\": \"cnn_1576035422508121_90\", \"run_number\": 111, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:44:21.355362Z\", \"end_time\": \"2019-12-11T04:45:48.656146Z\", \"created_time\": \"2019-12-11T04:44:10.76973Z\", \"created_time_dt\": \"2019-12-11T04:44:10.76973Z\", \"duration\": \"0:01:37\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.005850789132024163}, {\"run_id\": \"cnn_1576035422508121_92\", \"run_number\": 112, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:44:52.120803Z\", \"end_time\": \"2019-12-11T04:46:52.188445Z\", \"created_time\": \"2019-12-11T04:44:41.567529Z\", \"created_time_dt\": \"2019-12-11T04:44:41.567529Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0027836619750393967}, {\"run_id\": \"cnn_1576035422508121_91\", \"run_number\": 113, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:44:52.607683Z\", \"end_time\": \"2019-12-11T04:46:19.995721Z\", \"created_time\": \"2019-12-11T04:44:41.601168Z\", \"created_time_dt\": \"2019-12-11T04:44:41.601168Z\", \"duration\": \"0:01:38\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.002886556377179966}, {\"run_id\": \"cnn_1576035422508121_93\", \"run_number\": 114, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:45:54.189493Z\", \"end_time\": \"2019-12-11T04:48:25.174457Z\", \"created_time\": \"2019-12-11T04:45:43.046584Z\", \"created_time_dt\": \"2019-12-11T04:45:43.046584Z\", \"duration\": \"0:02:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.00737012180515877}, {\"run_id\": \"cnn_1576035422508121_94\", \"run_number\": 115, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:46:24.370786Z\", \"end_time\": \"2019-12-11T04:48:24.548746Z\", \"created_time\": \"2019-12-11T04:46:14.244925Z\", \"created_time_dt\": \"2019-12-11T04:46:14.244925Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.02123085768913044}, {\"run_id\": \"cnn_1576035422508121_95\", \"run_number\": 116, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:46:55.612319Z\", \"end_time\": \"2019-12-11T04:48:07.698131Z\", \"created_time\": \"2019-12-11T04:46:45.306338Z\", \"created_time_dt\": \"2019-12-11T04:46:45.306338Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.033218957704612126}, {\"run_id\": \"cnn_1576035422508121_96\", \"run_number\": 117, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:47:27.782882Z\", \"end_time\": \"2019-12-11T04:48:40.105452Z\", \"created_time\": \"2019-12-11T04:47:17.289026Z\", \"created_time_dt\": \"2019-12-11T04:47:17.289026Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.058161514991913806}, {\"run_id\": \"cnn_1576035422508121_98\", \"run_number\": 118, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:49:04.944168Z\", \"end_time\": \"2019-12-11T04:51:29.981676Z\", \"created_time\": \"2019-12-11T04:48:49.978832Z\", \"created_time_dt\": \"2019-12-11T04:48:49.978832Z\", \"duration\": \"0:02:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.05239481322129709}, {\"run_id\": \"cnn_1576035422508121_97\", \"run_number\": 119, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:49:00.631129Z\", \"end_time\": \"2019-12-11T04:51:00.853296Z\", \"created_time\": \"2019-12-11T04:48:50.017144Z\", \"created_time_dt\": \"2019-12-11T04:48:50.017144Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.002328968983315816}, {\"run_id\": \"cnn_1576035422508121_99\", \"run_number\": 120, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:49:00.682814Z\", \"end_time\": \"2019-12-11T04:51:00.78599Z\", \"created_time\": \"2019-12-11T04:48:50.183773Z\", \"created_time_dt\": \"2019-12-11T04:48:50.183773Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.006486634587010012}, {\"run_id\": \"cnn_1576035422508121_100\", \"run_number\": 121, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:49:31.460086Z\", \"end_time\": \"2019-12-11T04:51:03.954801Z\", \"created_time\": \"2019-12-11T04:49:21.020244Z\", \"created_time_dt\": \"2019-12-11T04:49:21.020244Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.006988799399263467}, {\"run_id\": \"cnn_1576035422508121_101\", \"run_number\": 122, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:51:34.574771Z\", \"end_time\": \"2019-12-11T04:52:53.473769Z\", \"created_time\": \"2019-12-11T04:51:24.245583Z\", \"created_time_dt\": \"2019-12-11T04:51:24.245583Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001}, {\"run_id\": \"cnn_1576035422508121_103\", \"run_number\": 123, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:51:34.546111Z\", \"end_time\": \"2019-12-11T04:53:14.259184Z\", \"created_time\": \"2019-12-11T04:51:24.314635Z\", \"created_time_dt\": \"2019-12-11T04:51:24.314635Z\", \"duration\": \"0:01:49\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.07389346084598157}, {\"run_id\": \"cnn_1576035422508121_102\", \"run_number\": 124, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:51:34.580155Z\", \"end_time\": \"2019-12-11T04:53:54.527822Z\", \"created_time\": \"2019-12-11T04:51:24.363097Z\", \"created_time_dt\": \"2019-12-11T04:51:24.363097Z\", \"duration\": \"0:02:30\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007536933376816077}, {\"run_id\": \"cnn_1576035422508121_104\", \"run_number\": 125, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:52:10.060879Z\", \"end_time\": \"2019-12-11T04:53:45.389793Z\", \"created_time\": \"2019-12-11T04:51:55.271987Z\", \"created_time_dt\": \"2019-12-11T04:51:55.271987Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0728744949072908}, {\"run_id\": \"cnn_1576035422508121_105\", \"run_number\": 126, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:53:38.448146Z\", \"end_time\": \"2019-12-11T04:55:11.336939Z\", \"created_time\": \"2019-12-11T04:53:27.38327Z\", \"created_time_dt\": \"2019-12-11T04:53:27.38327Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.07409506540357143}, {\"run_id\": \"cnn_1576035422508121_106\", \"run_number\": 127, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:54:09.27528Z\", \"end_time\": \"2019-12-11T04:56:09.246758Z\", \"created_time\": \"2019-12-11T04:53:58.894413Z\", \"created_time_dt\": \"2019-12-11T04:53:58.894413Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0027160836141038856}, {\"run_id\": \"cnn_1576035422508121_108\", \"run_number\": 128, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:54:40.32904Z\", \"end_time\": \"2019-12-11T04:56:13.12842Z\", \"created_time\": \"2019-12-11T04:54:29.879098Z\", \"created_time_dt\": \"2019-12-11T04:54:29.879098Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.050989530866667195}, {\"run_id\": \"cnn_1576035422508121_107\", \"run_number\": 129, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:54:40.916117Z\", \"end_time\": \"2019-12-11T04:56:06.636087Z\", \"created_time\": \"2019-12-11T04:54:30.001837Z\", \"created_time_dt\": \"2019-12-11T04:54:30.001837Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.005527675198949129}, {\"run_id\": \"cnn_1576035422508121_109\", \"run_number\": 130, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:56:12.793033Z\", \"end_time\": \"2019-12-11T04:57:46.857121Z\", \"created_time\": \"2019-12-11T04:56:02.070114Z\", \"created_time_dt\": \"2019-12-11T04:56:02.070114Z\", \"duration\": \"0:01:44\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0027850685169387717}, {\"run_id\": \"cnn_1576035422508121_111\", \"run_number\": 131, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:56:43.321652Z\", \"end_time\": \"2019-12-11T04:57:44.77959Z\", \"created_time\": \"2019-12-11T04:56:32.944896Z\", \"created_time_dt\": \"2019-12-11T04:56:32.944896Z\", \"duration\": \"0:01:11\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0031271427739685215}, {\"run_id\": \"cnn_1576035422508121_110\", \"run_number\": 132, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:56:48.030379Z\", \"end_time\": \"2019-12-11T04:58:18.659865Z\", \"created_time\": \"2019-12-11T04:56:37.853469Z\", \"created_time_dt\": \"2019-12-11T04:56:37.853469Z\", \"duration\": \"0:01:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.002174990297306968}, {\"run_id\": \"cnn_1576035422508121_112\", \"run_number\": 133, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:57:19.077003Z\", \"end_time\": \"2019-12-11T04:58:31.172304Z\", \"created_time\": \"2019-12-11T04:57:08.72536Z\", \"created_time_dt\": \"2019-12-11T04:57:08.72536Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.019116538972536838}, {\"run_id\": \"cnn_1576035422508121_113\", \"run_number\": 134, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:58:22.266169Z\", \"end_time\": \"2019-12-11T04:59:49.355962Z\", \"created_time\": \"2019-12-11T04:58:12.026783Z\", \"created_time_dt\": \"2019-12-11T04:58:12.026783Z\", \"duration\": \"0:01:37\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0057463796162591785}, {\"run_id\": \"cnn_1576035422508121_114\", \"run_number\": 135, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:58:22.543839Z\", \"end_time\": \"2019-12-11T05:00:02.408583Z\", \"created_time\": \"2019-12-11T04:58:12.062991Z\", \"created_time_dt\": \"2019-12-11T04:58:12.062991Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.007319097105330926}, {\"run_id\": \"cnn_1576035422508121_115\", \"run_number\": 136, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:58:53.203136Z\", \"end_time\": \"2019-12-11T05:00:53.306062Z\", \"created_time\": \"2019-12-11T04:58:42.800513Z\", \"created_time_dt\": \"2019-12-11T04:58:42.800513Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.006465596963153954}, {\"run_id\": \"cnn_1576035422508121_116\", \"run_number\": 137, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T04:58:53.31287Z\", \"end_time\": \"2019-12-11T05:01:22.896839Z\", \"created_time\": \"2019-12-11T04:58:42.896459Z\", \"created_time_dt\": \"2019-12-11T04:58:42.896459Z\", \"duration\": \"0:02:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.03247675533597945}, {\"run_id\": \"cnn_1576035422508121_118\", \"run_number\": 138, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:00:25.623098Z\", \"end_time\": \"2019-12-11T05:01:51.290603Z\", \"created_time\": \"2019-12-11T05:00:15.16103Z\", \"created_time_dt\": \"2019-12-11T05:00:15.16103Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.004122214508639576}, {\"run_id\": \"cnn_1576035422508121_117\", \"run_number\": 139, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:00:25.532361Z\", \"end_time\": \"2019-12-11T05:01:56.229202Z\", \"created_time\": \"2019-12-11T05:00:15.187491Z\", \"created_time_dt\": \"2019-12-11T05:00:15.187491Z\", \"duration\": \"0:01:41\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.02016178781143776}, {\"run_id\": \"cnn_1576035422508121_119\", \"run_number\": 140, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:01:27.560158Z\", \"end_time\": \"2019-12-11T05:02:58.051156Z\", \"created_time\": \"2019-12-11T05:01:17.125835Z\", \"created_time_dt\": \"2019-12-11T05:01:17.125835Z\", \"duration\": \"0:01:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.004819371107591107}, {\"run_id\": \"cnn_1576035422508121_120\", \"run_number\": 141, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:01:58.684245Z\", \"end_time\": \"2019-12-11T05:03:58.780619Z\", \"created_time\": \"2019-12-11T05:01:48.548647Z\", \"created_time_dt\": \"2019-12-11T05:01:48.548647Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.05210291513293579}, {\"run_id\": \"cnn_1576035422508121_121\", \"run_number\": 142, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:02:30.071084Z\", \"end_time\": \"2019-12-11T05:03:42.771828Z\", \"created_time\": \"2019-12-11T05:02:19.436092Z\", \"created_time_dt\": \"2019-12-11T05:02:19.436092Z\", \"duration\": \"0:01:23\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01}, {\"run_id\": \"cnn_1576035422508121_122\", \"run_number\": 143, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:02:30.012488Z\", \"end_time\": \"2019-12-11T05:04:59.588128Z\", \"created_time\": \"2019-12-11T05:02:19.495801Z\", \"created_time_dt\": \"2019-12-11T05:02:19.495801Z\", \"duration\": \"0:02:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.05749912596984914}, {\"run_id\": \"cnn_1576035422508121_123\", \"run_number\": 144, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:03:31.694813Z\", \"end_time\": \"2019-12-11T05:05:02.281298Z\", \"created_time\": \"2019-12-11T05:03:21.294887Z\", \"created_time_dt\": \"2019-12-11T05:03:21.294887Z\", \"duration\": \"0:01:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.040946675821793725}, {\"run_id\": \"cnn_1576035422508121_124\", \"run_number\": 145, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:04:33.563075Z\", \"end_time\": \"2019-12-11T05:05:46.135153Z\", \"created_time\": \"2019-12-11T05:04:23.145045Z\", \"created_time_dt\": \"2019-12-11T05:04:23.145045Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001}, {\"run_id\": \"cnn_1576035422508121_125\", \"run_number\": 146, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:04:33.636473Z\", \"end_time\": \"2019-12-11T05:06:33.848289Z\", \"created_time\": \"2019-12-11T05:04:23.174963Z\", \"created_time_dt\": \"2019-12-11T05:04:23.174963Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.07293972862572135}, {\"run_id\": \"cnn_1576035422508121_127\", \"run_number\": 147, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:05:35.149917Z\", \"end_time\": \"2019-12-11T05:06:53.735344Z\", \"created_time\": \"2019-12-11T05:05:24.717618Z\", \"created_time_dt\": \"2019-12-11T05:05:24.717618Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.02246595649272443}, {\"run_id\": \"cnn_1576035422508121_126\", \"run_number\": 148, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:05:35.97465Z\", \"end_time\": \"2019-12-11T05:07:23.23075Z\", \"created_time\": \"2019-12-11T05:05:24.836576Z\", \"created_time_dt\": \"2019-12-11T05:05:24.836576Z\", \"duration\": \"0:01:58\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.005148247534554578}, {\"run_id\": \"cnn_1576035422508121_128\", \"run_number\": 149, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:06:38.487122Z\", \"end_time\": \"2019-12-11T05:08:38.562987Z\", \"created_time\": \"2019-12-11T05:06:28.107941Z\", \"created_time_dt\": \"2019-12-11T05:06:28.107941Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.05999990603614398}, {\"run_id\": \"cnn_1576035422508121_129\", \"run_number\": 150, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:07:09.343446Z\", \"end_time\": \"2019-12-11T05:08:39.790054Z\", \"created_time\": \"2019-12-11T05:06:58.995358Z\", \"created_time_dt\": \"2019-12-11T05:06:58.995358Z\", \"duration\": \"0:01:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0032212303586517787}, {\"run_id\": \"cnn_1576035422508121_130\", \"run_number\": 151, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:07:40.691718Z\", \"end_time\": \"2019-12-11T05:09:09.417058Z\", \"created_time\": \"2019-12-11T05:07:29.961819Z\", \"created_time_dt\": \"2019-12-11T05:07:29.961819Z\", \"duration\": \"0:01:39\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.005095630727028677}, {\"run_id\": \"cnn_1576035422508121_131\", \"run_number\": 152, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:08:10.977767Z\", \"end_time\": \"2019-12-11T05:09:41.524406Z\", \"created_time\": \"2019-12-11T05:08:00.782177Z\", \"created_time_dt\": \"2019-12-11T05:08:00.782177Z\", \"duration\": \"0:01:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.04102848713130967}, {\"run_id\": \"cnn_1576035422508121_133\", \"run_number\": 153, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:09:13.682333Z\", \"end_time\": \"2019-12-11T05:11:14.241274Z\", \"created_time\": \"2019-12-11T05:09:03.154031Z\", \"created_time_dt\": \"2019-12-11T05:09:03.154031Z\", \"duration\": \"0:02:11\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.005627939403536982}, {\"run_id\": \"cnn_1576035422508121_132\", \"run_number\": 154, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:09:13.736087Z\", \"end_time\": \"2019-12-11T05:11:13.819767Z\", \"created_time\": \"2019-12-11T05:09:03.296361Z\", \"created_time_dt\": \"2019-12-11T05:09:03.296361Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.047809981358399645}, {\"run_id\": \"cnn_1576035422508121_134\", \"run_number\": 155, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:09:45.13776Z\", \"end_time\": \"2019-12-11T05:11:10.609228Z\", \"created_time\": \"2019-12-11T05:09:35.003022Z\", \"created_time_dt\": \"2019-12-11T05:09:35.003022Z\", \"duration\": \"0:01:35\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.20556085652412462}, {\"run_id\": \"cnn_1576035422508121_135\", \"run_number\": 156, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:10:46.969652Z\", \"end_time\": \"2019-12-11T05:12:41.904305Z\", \"created_time\": \"2019-12-11T05:10:36.547195Z\", \"created_time_dt\": \"2019-12-11T05:10:36.547195Z\", \"duration\": \"0:02:05\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0017043049748055436}, {\"run_id\": \"cnn_1576035422508121_136\", \"run_number\": 157, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:11:48.924326Z\", \"end_time\": \"2019-12-11T05:13:48.984603Z\", \"created_time\": \"2019-12-11T05:11:38.505432Z\", \"created_time_dt\": \"2019-12-11T05:11:38.505432Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.00613529505075418}, {\"run_id\": \"cnn_1576035422508121_138\", \"run_number\": 158, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:12:19.639558Z\", \"end_time\": \"2019-12-11T05:13:59.446096Z\", \"created_time\": \"2019-12-11T05:12:09.340314Z\", \"created_time_dt\": \"2019-12-11T05:12:09.340314Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.07410803536793799}, {\"run_id\": \"cnn_1576035422508121_137\", \"run_number\": 159, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:12:19.536331Z\", \"end_time\": \"2019-12-11T05:14:14.558012Z\", \"created_time\": \"2019-12-11T05:12:09.356694Z\", \"created_time_dt\": \"2019-12-11T05:12:09.356694Z\", \"duration\": \"0:02:05\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0018395874517682382}, {\"run_id\": \"cnn_1576035422508121_139\", \"run_number\": 160, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:13:52.592272Z\", \"end_time\": \"2019-12-11T05:15:25.015707Z\", \"created_time\": \"2019-12-11T05:13:41.641569Z\", \"created_time_dt\": \"2019-12-11T05:13:41.641569Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007089736047942984}, {\"run_id\": \"cnn_1576035422508121_142\", \"run_number\": 161, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:14:53.83144Z\", \"end_time\": \"2019-12-11T05:16:06.246062Z\", \"created_time\": \"2019-12-11T05:14:43.471242Z\", \"created_time_dt\": \"2019-12-11T05:14:43.471242Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001}, {\"run_id\": \"cnn_1576035422508121_140\", \"run_number\": 162, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:14:53.889128Z\", \"end_time\": \"2019-12-11T05:16:06.189603Z\", \"created_time\": \"2019-12-11T05:14:43.511996Z\", \"created_time_dt\": \"2019-12-11T05:14:43.511996Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.007170816212619382}, {\"run_id\": \"cnn_1576035422508121_141\", \"run_number\": 163, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:14:54.037796Z\", \"end_time\": \"2019-12-11T05:16:41.347835Z\", \"created_time\": \"2019-12-11T05:14:43.535876Z\", \"created_time_dt\": \"2019-12-11T05:14:43.535876Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.060023087066599645}, {\"run_id\": \"cnn_1576035422508121_143\", \"run_number\": 164, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:15:55.163103Z\", \"end_time\": \"2019-12-11T05:17:13.727242Z\", \"created_time\": \"2019-12-11T05:15:44.871703Z\", \"created_time_dt\": \"2019-12-11T05:15:44.871703Z\", \"duration\": \"0:01:28\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.22373544442996518}, {\"run_id\": \"cnn_1576035422508121_145\", \"run_number\": 165, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:16:25.968329Z\", \"end_time\": \"2019-12-11T05:17:58.47696Z\", \"created_time\": \"2019-12-11T05:16:15.679822Z\", \"created_time_dt\": \"2019-12-11T05:16:15.679822Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.00886806398838928}, {\"run_id\": \"cnn_1576035422508121_144\", \"run_number\": 166, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:16:25.839188Z\", \"end_time\": \"2019-12-11T05:17:44.534952Z\", \"created_time\": \"2019-12-11T05:16:15.727042Z\", \"created_time_dt\": \"2019-12-11T05:16:15.727042Z\", \"duration\": \"0:01:28\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.006961299402258681}, {\"run_id\": \"cnn_1576035422508121_146\", \"run_number\": 167, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:16:58.180828Z\", \"end_time\": \"2019-12-11T05:18:23.438116Z\", \"created_time\": \"2019-12-11T05:16:47.712985Z\", \"created_time_dt\": \"2019-12-11T05:16:47.712985Z\", \"duration\": \"0:01:35\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0016478991165532857}, {\"run_id\": \"cnn_1576035422508121_148\", \"run_number\": 168, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:17:59.469491Z\", \"end_time\": \"2019-12-11T05:19:11.569974Z\", \"created_time\": \"2019-12-11T05:17:49.163422Z\", \"created_time_dt\": \"2019-12-11T05:17:49.163422Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.006436058941020129}, {\"run_id\": \"cnn_1576035422508121_147\", \"run_number\": 169, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:17:59.619087Z\", \"end_time\": \"2019-12-11T05:20:14.628115Z\", \"created_time\": \"2019-12-11T05:17:49.176446Z\", \"created_time_dt\": \"2019-12-11T05:17:49.176446Z\", \"duration\": \"0:02:25\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.006015634919581108}, {\"run_id\": \"cnn_1576035422508121_149\", \"run_number\": 170, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:18:30.413534Z\", \"end_time\": \"2019-12-11T05:20:30.406636Z\", \"created_time\": \"2019-12-11T05:18:20.244361Z\", \"created_time_dt\": \"2019-12-11T05:18:20.244361Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0055673253102979615}, {\"run_id\": \"cnn_1576035422508121_150\", \"run_number\": 171, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:19:01.735532Z\", \"end_time\": \"2019-12-11T05:20:49.212893Z\", \"created_time\": \"2019-12-11T05:18:51.199099Z\", \"created_time_dt\": \"2019-12-11T05:18:51.199099Z\", \"duration\": \"0:01:58\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.03103081823005821}, {\"run_id\": \"cnn_1576035422508121_151\", \"run_number\": 172, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:19:32.559113Z\", \"end_time\": \"2019-12-11T05:20:58.16597Z\", \"created_time\": \"2019-12-11T05:19:22.166168Z\", \"created_time_dt\": \"2019-12-11T05:19:22.166168Z\", \"duration\": \"0:01:35\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.05649181320375083}, {\"run_id\": \"cnn_1576035422508121_152\", \"run_number\": 173, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:20:39.937286Z\", \"end_time\": \"2019-12-11T05:22:06.072557Z\", \"created_time\": \"2019-12-11T05:20:24.934045Z\", \"created_time_dt\": \"2019-12-11T05:20:24.934045Z\", \"duration\": \"0:01:41\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.002126991913329401}, {\"run_id\": \"cnn_1576035422508121_153\", \"run_number\": 174, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:21:05.970628Z\", \"end_time\": \"2019-12-11T05:22:24.703269Z\", \"created_time\": \"2019-12-11T05:20:55.887896Z\", \"created_time_dt\": \"2019-12-11T05:20:55.887896Z\", \"duration\": \"0:01:28\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0739042383068324}, {\"run_id\": \"cnn_1576035422508121_154\", \"run_number\": 175, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:21:37.648014Z\", \"end_time\": \"2019-12-11T05:23:40.903936Z\", \"created_time\": \"2019-12-11T05:21:27.097925Z\", \"created_time_dt\": \"2019-12-11T05:21:27.097925Z\", \"duration\": \"0:02:13\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.07289642681217721}, {\"run_id\": \"cnn_1576035422508121_155\", \"run_number\": 176, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:21:37.48489Z\", \"end_time\": \"2019-12-11T05:23:37.6051Z\", \"created_time\": \"2019-12-11T05:21:27.109062Z\", \"created_time_dt\": \"2019-12-11T05:21:27.109062Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0029952168249101534}, {\"run_id\": \"cnn_1576035422508121_156\", \"run_number\": 177, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:22:39.622973Z\", \"end_time\": \"2019-12-11T05:23:51.496097Z\", \"created_time\": \"2019-12-11T05:22:29.04227Z\", \"created_time_dt\": \"2019-12-11T05:22:29.04227Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007361856441061399}, {\"run_id\": \"cnn_1576035422508121_157\", \"run_number\": 178, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:23:10.483203Z\", \"end_time\": \"2019-12-11T05:24:29.178282Z\", \"created_time\": \"2019-12-11T05:23:00.02951Z\", \"created_time_dt\": \"2019-12-11T05:23:00.02951Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.1152883650525642}, {\"run_id\": \"cnn_1576035422508121_160\", \"run_number\": 179, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:24:12.783918Z\", \"end_time\": \"2019-12-11T05:25:59.975145Z\", \"created_time\": \"2019-12-11T05:24:02.244057Z\", \"created_time_dt\": \"2019-12-11T05:24:02.244057Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.018245631696892448}, {\"run_id\": \"cnn_1576035422508121_158\", \"run_number\": 180, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:24:13.013426Z\", \"end_time\": \"2019-12-11T05:26:13.180054Z\", \"created_time\": \"2019-12-11T05:24:02.258172Z\", \"created_time_dt\": \"2019-12-11T05:24:02.258172Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0018798126665922333}, {\"run_id\": \"cnn_1576035422508121_159\", \"run_number\": 181, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:24:12.818647Z\", \"end_time\": \"2019-12-11T05:26:15.953479Z\", \"created_time\": \"2019-12-11T05:24:02.250157Z\", \"created_time_dt\": \"2019-12-11T05:24:02.250157Z\", \"duration\": \"0:02:13\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007337688606528911}, {\"run_id\": \"cnn_1576035422508121_161\", \"run_number\": 182, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:25:14.534376Z\", \"end_time\": \"2019-12-11T05:27:01.935707Z\", \"created_time\": \"2019-12-11T05:25:03.974822Z\", \"created_time_dt\": \"2019-12-11T05:25:03.974822Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.048746973447536944}, {\"run_id\": \"cnn_1576035422508121_162\", \"run_number\": 183, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:26:51.74547Z\", \"end_time\": \"2019-12-11T05:28:27.142691Z\", \"created_time\": \"2019-12-11T05:26:36.81595Z\", \"created_time_dt\": \"2019-12-11T05:26:36.81595Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.004857694112147825}, {\"run_id\": \"cnn_1576035422508121_164\", \"run_number\": 184, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:26:51.769061Z\", \"end_time\": \"2019-12-11T05:27:59.424764Z\", \"created_time\": \"2019-12-11T05:26:36.832487Z\", \"created_time_dt\": \"2019-12-11T05:26:36.832487Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0621190648925235}, {\"run_id\": \"cnn_1576035422508121_163\", \"run_number\": 185, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:26:51.909094Z\", \"end_time\": \"2019-12-11T05:28:06.504734Z\", \"created_time\": \"2019-12-11T05:26:36.85431Z\", \"created_time_dt\": \"2019-12-11T05:26:36.85431Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001}, {\"run_id\": \"cnn_1576035422508121_165\", \"run_number\": 186, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:27:48.629044Z\", \"end_time\": \"2019-12-11T05:29:48.897261Z\", \"created_time\": \"2019-12-11T05:27:38.07611Z\", \"created_time_dt\": \"2019-12-11T05:27:38.07611Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.056449554634164414}, {\"run_id\": \"cnn_1576035422508121_167\", \"run_number\": 187, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:28:49.897139Z\", \"end_time\": \"2019-12-11T05:30:50.110351Z\", \"created_time\": \"2019-12-11T05:28:39.53491Z\", \"created_time_dt\": \"2019-12-11T05:28:39.53491Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.005234973874115784}, {\"run_id\": \"cnn_1576035422508121_166\", \"run_number\": 188, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:28:49.867488Z\", \"end_time\": \"2019-12-11T05:31:01.103912Z\", \"created_time\": \"2019-12-11T05:28:39.551185Z\", \"created_time_dt\": \"2019-12-11T05:28:39.551185Z\", \"duration\": \"0:02:21\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.003156564795002247}, {\"run_id\": \"cnn_1576035422508121_168\", \"run_number\": 189, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:29:21.102685Z\", \"end_time\": \"2019-12-11T05:31:21.312762Z\", \"created_time\": \"2019-12-11T05:29:10.491211Z\", \"created_time_dt\": \"2019-12-11T05:29:10.491211Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.001969541415377886}, {\"run_id\": \"cnn_1576035422508121_169\", \"run_number\": 190, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:30:22.80476Z\", \"end_time\": \"2019-12-11T05:31:48.546177Z\", \"created_time\": \"2019-12-11T05:30:12.342326Z\", \"created_time_dt\": \"2019-12-11T05:30:12.342326Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.059784411036713746}, {\"run_id\": \"cnn_1576035422508121_170\", \"run_number\": 191, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:31:26.31889Z\", \"end_time\": \"2019-12-11T05:32:38.353329Z\", \"created_time\": \"2019-12-11T05:31:15.86655Z\", \"created_time_dt\": \"2019-12-11T05:31:15.86655Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.22264110533621007}, {\"run_id\": \"cnn_1576035422508121_172\", \"run_number\": 192, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:31:56.961772Z\", \"end_time\": \"2019-12-11T05:33:52.264134Z\", \"created_time\": \"2019-12-11T05:31:46.76911Z\", \"created_time_dt\": \"2019-12-11T05:31:46.76911Z\", \"duration\": \"0:02:05\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.06224243860030601}, {\"run_id\": \"cnn_1576035422508121_171\", \"run_number\": 193, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:31:57.647428Z\", \"end_time\": \"2019-12-11T05:34:01.44556Z\", \"created_time\": \"2019-12-11T05:31:47.048659Z\", \"created_time_dt\": \"2019-12-11T05:31:47.048659Z\", \"duration\": \"0:02:14\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.004737953442852887}, {\"run_id\": \"cnn_1576035422508121_173\", \"run_number\": 194, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:32:28.424916Z\", \"end_time\": \"2019-12-11T05:34:31.550348Z\", \"created_time\": \"2019-12-11T05:32:18.129408Z\", \"created_time_dt\": \"2019-12-11T05:32:18.129408Z\", \"duration\": \"0:02:13\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007346217012309931}, {\"run_id\": \"cnn_1576035422508121_174\", \"run_number\": 195, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:33:30.377602Z\", \"end_time\": \"2019-12-11T05:35:45.67163Z\", \"created_time\": \"2019-12-11T05:33:19.762979Z\", \"created_time_dt\": \"2019-12-11T05:33:19.762979Z\", \"duration\": \"0:02:25\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0030417262315517303}, {\"run_id\": \"cnn_1576035422508121_175\", \"run_number\": 196, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:34:31.593254Z\", \"end_time\": \"2019-12-11T05:36:03.960906Z\", \"created_time\": \"2019-12-11T05:34:21.254259Z\", \"created_time_dt\": \"2019-12-11T05:34:21.254259Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0729206776049051}, {\"run_id\": \"cnn_1576035422508121_176\", \"run_number\": 197, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:34:31.634651Z\", \"end_time\": \"2019-12-11T05:36:26.74031Z\", \"created_time\": \"2019-12-11T05:34:21.280097Z\", \"created_time_dt\": \"2019-12-11T05:34:21.280097Z\", \"duration\": \"0:02:05\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0016641365048132158}, {\"run_id\": \"cnn_1576035422508121_177\", \"run_number\": 198, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:35:02.784371Z\", \"end_time\": \"2019-12-11T05:37:03.040819Z\", \"created_time\": \"2019-12-11T05:34:52.287408Z\", \"created_time_dt\": \"2019-12-11T05:34:52.287408Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.005317719610682364}, {\"run_id\": \"cnn_1576035422508121_179\", \"run_number\": 199, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:36:35.77825Z\", \"end_time\": \"2019-12-11T05:38:08.394586Z\", \"created_time\": \"2019-12-11T05:36:25.397955Z\", \"created_time_dt\": \"2019-12-11T05:36:25.397955Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.15636326994570982}, {\"run_id\": \"cnn_1576035422508121_178\", \"run_number\": 200, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:36:35.720666Z\", \"end_time\": \"2019-12-11T05:38:36.03731Z\", \"created_time\": \"2019-12-11T05:36:25.4558Z\", \"created_time_dt\": \"2019-12-11T05:36:25.4558Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0030274794999121057}, {\"run_id\": \"cnn_1576035422508121_180\", \"run_number\": 201, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:37:07.127782Z\", \"end_time\": \"2019-12-11T05:38:25.768517Z\", \"created_time\": \"2019-12-11T05:36:56.803084Z\", \"created_time_dt\": \"2019-12-11T05:36:56.803084Z\", \"duration\": \"0:01:28\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.05598762355527637}, {\"run_id\": \"cnn_1576035422508121_181\", \"run_number\": 202, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:37:38.408913Z\", \"end_time\": \"2019-12-11T05:38:57.064607Z\", \"created_time\": \"2019-12-11T05:37:27.811616Z\", \"created_time_dt\": \"2019-12-11T05:37:27.811616Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.2077188635562704}, {\"run_id\": \"cnn_1576035422508121_182\", \"run_number\": 203, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:39:15.482628Z\", \"end_time\": \"2019-12-11T05:40:26.964742Z\", \"created_time\": \"2019-12-11T05:39:00.098085Z\", \"created_time_dt\": \"2019-12-11T05:39:00.098085Z\", \"duration\": \"0:01:26\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01}, {\"run_id\": \"cnn_1576035422508121_184\", \"run_number\": 204, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:39:10.762753Z\", \"end_time\": \"2019-12-11T05:40:43.582122Z\", \"created_time\": \"2019-12-11T05:39:00.157189Z\", \"created_time_dt\": \"2019-12-11T05:39:00.157189Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007271907025542771}, {\"run_id\": \"cnn_1576035422508121_183\", \"run_number\": 205, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:39:10.345498Z\", \"end_time\": \"2019-12-11T05:41:13.30405Z\", \"created_time\": \"2019-12-11T05:39:00.223505Z\", \"created_time_dt\": \"2019-12-11T05:39:00.223505Z\", \"duration\": \"0:02:13\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0030099846787144586}, {\"run_id\": \"cnn_1576035422508121_185\", \"run_number\": 206, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:39:42.411215Z\", \"end_time\": \"2019-12-11T05:41:29.916018Z\", \"created_time\": \"2019-12-11T05:39:31.933468Z\", \"created_time_dt\": \"2019-12-11T05:39:31.933468Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.002350359636339595}, {\"run_id\": \"cnn_1576035422508121_186\", \"run_number\": 207, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:41:14.408637Z\", \"end_time\": \"2019-12-11T05:42:33.279761Z\", \"created_time\": \"2019-12-11T05:41:03.941052Z\", \"created_time_dt\": \"2019-12-11T05:41:03.941052Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.11560058613239728}, {\"run_id\": \"cnn_1576035422508121_187\", \"run_number\": 208, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:41:45.309424Z\", \"end_time\": \"2019-12-11T05:43:17.929726Z\", \"created_time\": \"2019-12-11T05:41:35.12531Z\", \"created_time_dt\": \"2019-12-11T05:41:35.12531Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.008030137101512567}, {\"run_id\": \"cnn_1576035422508121_189\", \"run_number\": 209, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:42:16.46588Z\", \"end_time\": \"2019-12-11T05:44:49.992223Z\", \"created_time\": \"2019-12-11T05:42:06.163123Z\", \"created_time_dt\": \"2019-12-11T05:42:06.163123Z\", \"duration\": \"0:02:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.00530158337310446}, {\"run_id\": \"cnn_1576035422508121_188\", \"run_number\": 210, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:42:16.971156Z\", \"end_time\": \"2019-12-11T05:44:04.271666Z\", \"created_time\": \"2019-12-11T05:42:06.173137Z\", \"created_time_dt\": \"2019-12-11T05:42:06.173137Z\", \"duration\": \"0:01:58\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0019108238242012653}, {\"run_id\": \"cnn_1576035422508121_190\", \"run_number\": 211, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:43:18.177988Z\", \"end_time\": \"2019-12-11T05:44:50.733871Z\", \"created_time\": \"2019-12-11T05:43:07.662748Z\", \"created_time_dt\": \"2019-12-11T05:43:07.662748Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0030320507628377306}, {\"run_id\": \"cnn_1576035422508121_191\", \"run_number\": 212, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:44:20.008242Z\", \"end_time\": \"2019-12-11T05:45:25.878791Z\", \"created_time\": \"2019-12-11T05:44:09.540343Z\", \"created_time_dt\": \"2019-12-11T05:44:09.540343Z\", \"duration\": \"0:01:16\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0026966536367079015}, {\"run_id\": \"cnn_1576035422508121_192\", \"run_number\": 213, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:44:51.24561Z\", \"end_time\": \"2019-12-11T05:46:23.819929Z\", \"created_time\": \"2019-12-11T05:44:40.945044Z\", \"created_time_dt\": \"2019-12-11T05:44:40.945044Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0023448347342281783}, {\"run_id\": \"cnn_1576035422508121_194\", \"run_number\": 214, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:45:57.273567Z\", \"end_time\": \"2019-12-11T05:48:21.663188Z\", \"created_time\": \"2019-12-11T05:45:42.356712Z\", \"created_time_dt\": \"2019-12-11T05:45:42.356712Z\", \"duration\": \"0:02:39\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.028750095594940267}, {\"run_id\": \"cnn_1576035422508121_193\", \"run_number\": 215, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:45:57.268013Z\", \"end_time\": \"2019-12-11T05:47:40.143176Z\", \"created_time\": \"2019-12-11T05:45:42.438682Z\", \"created_time_dt\": \"2019-12-11T05:45:42.438682Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.0046589534273998754}, {\"run_id\": \"cnn_1576035422508121_195\", \"run_number\": 216, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:46:23.904549Z\", \"end_time\": \"2019-12-11T05:48:11.09245Z\", \"created_time\": \"2019-12-11T05:46:13.716636Z\", \"created_time_dt\": \"2019-12-11T05:46:13.716636Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.004845674466416047}, {\"run_id\": \"cnn_1576035422508121_196\", \"run_number\": 217, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:47:26.399318Z\", \"end_time\": \"2019-12-11T05:49:26.945985Z\", \"created_time\": \"2019-12-11T05:47:15.684743Z\", \"created_time_dt\": \"2019-12-11T05:47:15.684743Z\", \"duration\": \"0:02:11\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.023821670553693636}, {\"run_id\": \"cnn_1576035422508121_197\", \"run_number\": 218, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:47:56.739668Z\", \"end_time\": \"2019-12-11T05:49:08.961046Z\", \"created_time\": \"2019-12-11T05:47:46.601647Z\", \"created_time_dt\": \"2019-12-11T05:47:46.601647Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.00869666941554745}, {\"run_id\": \"cnn_1576035422508121_198\", \"run_number\": 219, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:48:28.901767Z\", \"end_time\": \"2019-12-11T05:49:59.441797Z\", \"created_time\": \"2019-12-11T05:48:18.191012Z\", \"created_time_dt\": \"2019-12-11T05:48:18.191012Z\", \"duration\": \"0:01:41\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.002385774247814472}, {\"run_id\": \"cnn_1576035422508121_199\", \"run_number\": 220, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:48:59.453294Z\", \"end_time\": \"2019-12-11T05:51:03.630333Z\", \"created_time\": \"2019-12-11T05:48:49.264141Z\", \"created_time_dt\": \"2019-12-11T05:48:49.264141Z\", \"duration\": \"0:02:14\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0019560782659801533}, {\"run_id\": \"cnn_1576035422508121_200\", \"run_number\": 221, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:49:31.30809Z\", \"end_time\": \"2019-12-11T05:50:49.859312Z\", \"created_time\": \"2019-12-11T05:49:20.900166Z\", \"created_time_dt\": \"2019-12-11T05:49:20.900166Z\", \"duration\": \"0:01:28\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0051127586628842425}, {\"run_id\": \"cnn_1576035422508121_201\", \"run_number\": 222, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:50:03.42246Z\", \"end_time\": \"2019-12-11T05:51:33.869228Z\", \"created_time\": \"2019-12-11T05:49:53.090519Z\", \"created_time_dt\": \"2019-12-11T05:49:53.090519Z\", \"duration\": \"0:01:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.05957448892037738}, {\"run_id\": \"cnn_1576035422508121_202\", \"run_number\": 223, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:50:34.721464Z\", \"end_time\": \"2019-12-11T05:52:35.02476Z\", \"created_time\": \"2019-12-11T05:50:24.184906Z\", \"created_time_dt\": \"2019-12-11T05:50:24.184906Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.005144864267809982}, {\"run_id\": \"cnn_1576035422508121_203\", \"run_number\": 224, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:51:05.904075Z\", \"end_time\": \"2019-12-11T05:52:36.413102Z\", \"created_time\": \"2019-12-11T05:50:55.566358Z\", \"created_time_dt\": \"2019-12-11T05:50:55.566358Z\", \"duration\": \"0:01:40\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0022097761814799557}, {\"run_id\": \"cnn_1576035422508121_204\", \"run_number\": 225, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:51:37.665703Z\", \"end_time\": \"2019-12-11T05:53:25.789764Z\", \"created_time\": \"2019-12-11T05:51:27.010621Z\", \"created_time_dt\": \"2019-12-11T05:51:27.010621Z\", \"duration\": \"0:01:58\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0073114777567348885}, {\"run_id\": \"cnn_1576035422508121_205\", \"run_number\": 226, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:52:08.239321Z\", \"end_time\": \"2019-12-11T05:54:08.588324Z\", \"created_time\": \"2019-12-11T05:51:58.097983Z\", \"created_time_dt\": \"2019-12-11T05:51:58.097983Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0601698442515896}, {\"run_id\": \"cnn_1576035422508121_207\", \"run_number\": 227, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:53:11.100323Z\", \"end_time\": \"2019-12-11T05:55:14.336888Z\", \"created_time\": \"2019-12-11T05:53:00.265596Z\", \"created_time_dt\": \"2019-12-11T05:53:00.265596Z\", \"duration\": \"0:02:14\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.006962029682080699}, {\"run_id\": \"cnn_1576035422508121_206\", \"run_number\": 228, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:53:10.76393Z\", \"end_time\": \"2019-12-11T05:55:05.849845Z\", \"created_time\": \"2019-12-11T05:53:00.278132Z\", \"created_time_dt\": \"2019-12-11T05:53:00.278132Z\", \"duration\": \"0:02:05\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.018547126558378046}, {\"run_id\": \"cnn_1576035422508121_208\", \"run_number\": 229, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:54:13.138875Z\", \"end_time\": \"2019-12-11T05:55:46.043812Z\", \"created_time\": \"2019-12-11T05:54:02.360522Z\", \"created_time_dt\": \"2019-12-11T05:54:02.360522Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.006127412691780667}, {\"run_id\": \"cnn_1576035422508121_209\", \"run_number\": 230, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:54:44.403908Z\", \"end_time\": \"2019-12-11T05:56:17.041539Z\", \"created_time\": \"2019-12-11T05:54:33.38857Z\", \"created_time_dt\": \"2019-12-11T05:54:33.38857Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007859291073742333}, {\"run_id\": \"cnn_1576035422508121_210\", \"run_number\": 231, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:55:45.828688Z\", \"end_time\": \"2019-12-11T05:57:46.052324Z\", \"created_time\": \"2019-12-11T05:55:34.937255Z\", \"created_time_dt\": \"2019-12-11T05:55:34.937255Z\", \"duration\": \"0:02:11\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0032283472158707634}, {\"run_id\": \"cnn_1576035422508121_211\", \"run_number\": 232, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:55:46.182845Z\", \"end_time\": \"2019-12-11T05:57:26.179655Z\", \"created_time\": \"2019-12-11T05:55:35.052126Z\", \"created_time_dt\": \"2019-12-11T05:55:35.052126Z\", \"duration\": \"0:01:51\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.00654708952157631}, {\"run_id\": \"cnn_1576035422508121_212\", \"run_number\": 233, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:56:16.277404Z\", \"end_time\": \"2019-12-11T05:57:21.942044Z\", \"created_time\": \"2019-12-11T05:56:06.140151Z\", \"created_time_dt\": \"2019-12-11T05:56:06.140151Z\", \"duration\": \"0:01:15\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.05911534516501891}, {\"run_id\": \"cnn_1576035422508121_213\", \"run_number\": 234, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:56:47.85421Z\", \"end_time\": \"2019-12-11T05:58:13.40615Z\", \"created_time\": \"2019-12-11T05:56:37.546262Z\", \"created_time_dt\": \"2019-12-11T05:56:37.546262Z\", \"duration\": \"0:01:35\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.32363284611756865}, {\"run_id\": \"cnn_1576035422508121_214\", \"run_number\": 235, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:57:49.773471Z\", \"end_time\": \"2019-12-11T05:59:22.580283Z\", \"created_time\": \"2019-12-11T05:57:39.451163Z\", \"created_time_dt\": \"2019-12-11T05:57:39.451163Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.3249271920203085}, {\"run_id\": \"cnn_1576035422508121_216\", \"run_number\": 236, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:58:20.73403Z\", \"end_time\": \"2019-12-11T06:01:09.232536Z\", \"created_time\": \"2019-12-11T05:58:10.442887Z\", \"created_time_dt\": \"2019-12-11T05:58:10.442887Z\", \"duration\": \"0:02:58\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.03149803058726921}, {\"run_id\": \"cnn_1576035422508121_215\", \"run_number\": 237, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:58:20.917911Z\", \"end_time\": \"2019-12-11T05:59:39.455606Z\", \"created_time\": \"2019-12-11T05:58:10.484701Z\", \"created_time_dt\": \"2019-12-11T05:58:10.484701Z\", \"duration\": \"0:01:28\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.07369449515532374}, {\"run_id\": \"cnn_1576035422508121_217\", \"run_number\": 238, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:58:51.984873Z\", \"end_time\": \"2019-12-11T06:00:31.814242Z\", \"created_time\": \"2019-12-11T05:58:41.548343Z\", \"created_time_dt\": \"2019-12-11T05:58:41.548343Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.006118425742817646}, {\"run_id\": \"cnn_1576035422508121_218\", \"run_number\": 239, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T05:59:54.76126Z\", \"end_time\": \"2019-12-11T06:01:17.272158Z\", \"created_time\": \"2019-12-11T05:59:43.197605Z\", \"created_time_dt\": \"2019-12-11T05:59:43.197605Z\", \"duration\": \"0:01:34\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.001643238996128086}, {\"run_id\": \"cnn_1576035422508121_219\", \"run_number\": 240, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:00:29.718285Z\", \"end_time\": \"2019-12-11T06:01:56.082424Z\", \"created_time\": \"2019-12-11T06:00:14.41429Z\", \"created_time_dt\": \"2019-12-11T06:00:14.41429Z\", \"duration\": \"0:01:41\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0055330156619500855}, {\"run_id\": \"cnn_1576035422508121_220\", \"run_number\": 241, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:01:27.177507Z\", \"end_time\": \"2019-12-11T06:03:46.968592Z\", \"created_time\": \"2019-12-11T06:01:16.615819Z\", \"created_time_dt\": \"2019-12-11T06:01:16.615819Z\", \"duration\": \"0:02:30\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0015591654600755624}, {\"run_id\": \"cnn_1576035422508121_221\", \"run_number\": 242, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:01:58.416904Z\", \"end_time\": \"2019-12-11T06:03:45.840432Z\", \"created_time\": \"2019-12-11T06:01:48.062353Z\", \"created_time_dt\": \"2019-12-11T06:01:48.062353Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0016763593153126556}, {\"run_id\": \"cnn_1576035422508121_222\", \"run_number\": 243, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:01:58.308901Z\", \"end_time\": \"2019-12-11T06:04:13.438558Z\", \"created_time\": \"2019-12-11T06:01:48.067973Z\", \"created_time_dt\": \"2019-12-11T06:01:48.067973Z\", \"duration\": \"0:02:25\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0056332422162998445}, {\"run_id\": \"cnn_1576035422508121_223\", \"run_number\": 244, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:02:30.040275Z\", \"end_time\": \"2019-12-11T06:03:49.456509Z\", \"created_time\": \"2019-12-11T06:02:19.087686Z\", \"created_time_dt\": \"2019-12-11T06:02:19.087686Z\", \"duration\": \"0:01:30\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.13684736390451985}, {\"run_id\": \"cnn_1576035422508121_225\", \"run_number\": 245, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:04:32.629252Z\", \"end_time\": \"2019-12-11T06:05:51.683164Z\", \"created_time\": \"2019-12-11T06:04:22.194939Z\", \"created_time_dt\": \"2019-12-11T06:04:22.194939Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001}, {\"run_id\": \"cnn_1576035422508121_224\", \"run_number\": 246, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:04:32.765877Z\", \"end_time\": \"2019-12-11T06:06:35.78696Z\", \"created_time\": \"2019-12-11T06:04:22.296194Z\", \"created_time_dt\": \"2019-12-11T06:04:22.296194Z\", \"duration\": \"0:02:13\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.03115695176485316}, {\"run_id\": \"cnn_1576035422508121_226\", \"run_number\": 247, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:04:32.851869Z\", \"end_time\": \"2019-12-11T06:07:01.423698Z\", \"created_time\": \"2019-12-11T06:04:22.311274Z\", \"created_time_dt\": \"2019-12-11T06:04:22.311274Z\", \"duration\": \"0:02:39\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.22290511482202266}, {\"run_id\": \"cnn_1576035422508121_227\", \"run_number\": 248, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:05:03.778224Z\", \"end_time\": \"2019-12-11T06:07:06.828043Z\", \"created_time\": \"2019-12-11T06:04:53.460528Z\", \"created_time_dt\": \"2019-12-11T06:04:53.460528Z\", \"duration\": \"0:02:13\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.040910633645115636}, {\"run_id\": \"cnn_1576035422508121_228\", \"run_number\": 249, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:06:38.375959Z\", \"end_time\": \"2019-12-11T06:08:12.207349Z\", \"created_time\": \"2019-12-11T06:06:25.814371Z\", \"created_time_dt\": \"2019-12-11T06:06:25.814371Z\", \"duration\": \"0:01:46\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.20713352164446805}, {\"run_id\": \"cnn_1576035422508121_229\", \"run_number\": 250, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:07:37.817437Z\", \"end_time\": \"2019-12-11T06:09:08.54178Z\", \"created_time\": \"2019-12-11T06:07:27.364369Z\", \"created_time_dt\": \"2019-12-11T06:07:27.364369Z\", \"duration\": \"0:01:41\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0019368769044895268}, {\"run_id\": \"cnn_1576035422508121_231\", \"run_number\": 251, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:08:08.750158Z\", \"end_time\": \"2019-12-11T06:09:20.857569Z\", \"created_time\": \"2019-12-11T06:07:58.404779Z\", \"created_time_dt\": \"2019-12-11T06:07:58.404779Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.13787058093596874}, {\"run_id\": \"cnn_1576035422508121_230\", \"run_number\": 252, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:08:08.910163Z\", \"end_time\": \"2019-12-11T06:10:09.341552Z\", \"created_time\": \"2019-12-11T06:07:58.471812Z\", \"created_time_dt\": \"2019-12-11T06:07:58.471812Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.06004263697312982}, {\"run_id\": \"cnn_1576035422508121_232\", \"run_number\": 253, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:09:11.728629Z\", \"end_time\": \"2019-12-11T06:10:51.449495Z\", \"created_time\": \"2019-12-11T06:09:00.791574Z\", \"created_time_dt\": \"2019-12-11T06:09:00.791574Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.01908731324179196}, {\"run_id\": \"cnn_1576035422508121_234\", \"run_number\": 254, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:10:13.452485Z\", \"end_time\": \"2019-12-11T06:11:53.250989Z\", \"created_time\": \"2019-12-11T06:10:02.885583Z\", \"created_time_dt\": \"2019-12-11T06:10:02.885583Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.00590688106778936}, {\"run_id\": \"cnn_1576035422508121_233\", \"run_number\": 255, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:10:14.385806Z\", \"end_time\": \"2019-12-11T06:11:47.007304Z\", \"created_time\": \"2019-12-11T06:10:02.895957Z\", \"created_time_dt\": \"2019-12-11T06:10:02.895957Z\", \"duration\": \"0:01:44\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.1525994362285173}, {\"run_id\": \"cnn_1576035422508121_235\", \"run_number\": 256, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:10:48.98727Z\", \"end_time\": \"2019-12-11T06:12:17.432659Z\", \"created_time\": \"2019-12-11T06:10:34.307023Z\", \"created_time_dt\": \"2019-12-11T06:10:34.307023Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.02606581450259649}, {\"run_id\": \"cnn_1576035422508121_236\", \"run_number\": 257, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:11:15.665768Z\", \"end_time\": \"2019-12-11T06:13:44.131629Z\", \"created_time\": \"2019-12-11T06:11:05.473152Z\", \"created_time_dt\": \"2019-12-11T06:11:05.473152Z\", \"duration\": \"0:02:38\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0068667258081689594}, {\"run_id\": \"cnn_1576035422508121_237\", \"run_number\": 258, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:12:17.739897Z\", \"end_time\": \"2019-12-11T06:14:38.080648Z\", \"created_time\": \"2019-12-11T06:12:07.080152Z\", \"created_time_dt\": \"2019-12-11T06:12:07.080152Z\", \"duration\": \"0:02:31\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0728891564968433}, {\"run_id\": \"cnn_1576035422508121_238\", \"run_number\": 259, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:12:17.584144Z\", \"end_time\": \"2019-12-11T06:13:43.502492Z\", \"created_time\": \"2019-12-11T06:12:07.197276Z\", \"created_time_dt\": \"2019-12-11T06:12:07.197276Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001}, {\"run_id\": \"cnn_1576035422508121_239\", \"run_number\": 260, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:12:49.542151Z\", \"end_time\": \"2019-12-11T06:14:29.69057Z\", \"created_time\": \"2019-12-11T06:12:39.011493Z\", \"created_time_dt\": \"2019-12-11T06:12:39.011493Z\", \"duration\": \"0:01:50\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.07290155808153516}, {\"run_id\": \"cnn_1576035422508121_240\", \"run_number\": 261, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:14:22.141047Z\", \"end_time\": \"2019-12-11T06:15:40.70763Z\", \"created_time\": \"2019-12-11T06:14:11.463724Z\", \"created_time_dt\": \"2019-12-11T06:14:11.463724Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.002046420499148176}, {\"run_id\": \"cnn_1576035422508121_241\", \"run_number\": 262, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:14:22.130535Z\", \"end_time\": \"2019-12-11T06:16:42.22352Z\", \"created_time\": \"2019-12-11T06:14:11.599969Z\", \"created_time_dt\": \"2019-12-11T06:14:11.599969Z\", \"duration\": \"0:02:30\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.002180554028306293}, {\"run_id\": \"cnn_1576035422508121_242\", \"run_number\": 263, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:14:52.888515Z\", \"end_time\": \"2019-12-11T06:16:32.512348Z\", \"created_time\": \"2019-12-11T06:14:42.765554Z\", \"created_time_dt\": \"2019-12-11T06:14:42.765554Z\", \"duration\": \"0:01:49\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.020300190813708544}, {\"run_id\": \"cnn_1576035422508121_243\", \"run_number\": 264, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:15:24.080388Z\", \"end_time\": \"2019-12-11T06:16:36.009469Z\", \"created_time\": \"2019-12-11T06:15:13.779797Z\", \"created_time_dt\": \"2019-12-11T06:15:13.779797Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.0084890643001954}, {\"run_id\": \"cnn_1576035422508121_244\", \"run_number\": 265, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:16:25.869287Z\", \"end_time\": \"2019-12-11T06:17:44.590175Z\", \"created_time\": \"2019-12-11T06:16:15.491396Z\", \"created_time_dt\": \"2019-12-11T06:16:15.491396Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.019861082617512404}, {\"run_id\": \"cnn_1576035422508121_245\", \"run_number\": 266, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:16:57.246789Z\", \"end_time\": \"2019-12-11T06:18:29.806735Z\", \"created_time\": \"2019-12-11T06:16:46.537244Z\", \"created_time_dt\": \"2019-12-11T06:16:46.537244Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.002239508395450194}, {\"run_id\": \"cnn_1576035422508121_246\", \"run_number\": 267, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:17:29.29644Z\", \"end_time\": \"2019-12-11T06:19:44.403626Z\", \"created_time\": \"2019-12-11T06:17:18.898225Z\", \"created_time_dt\": \"2019-12-11T06:17:18.898225Z\", \"duration\": \"0:02:25\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.05226935327254411}, {\"run_id\": \"cnn_1576035422508121_247\", \"run_number\": 268, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:17:44.494205Z\", \"end_time\": \"2019-12-11T06:19:11.786962Z\", \"created_time\": \"2019-12-11T06:17:18.946843Z\", \"created_time_dt\": \"2019-12-11T06:17:18.946843Z\", \"duration\": \"0:01:52\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.019564722760417593}, {\"run_id\": \"cnn_1576035422508121_248\", \"run_number\": 269, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:18:30.957171Z\", \"end_time\": \"2019-12-11T06:20:31.11244Z\", \"created_time\": \"2019-12-11T06:18:20.560718Z\", \"created_time_dt\": \"2019-12-11T06:18:20.560718Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.00675742905486149}, {\"run_id\": \"cnn_1576035422508121_249\", \"run_number\": 270, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:19:02.064049Z\", \"end_time\": \"2019-12-11T06:20:34.942062Z\", \"created_time\": \"2019-12-11T06:18:51.559478Z\", \"created_time_dt\": \"2019-12-11T06:18:51.559478Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.07293478528486587}, {\"run_id\": \"cnn_1576035422508121_250\", \"run_number\": 271, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:20:04.419544Z\", \"end_time\": \"2019-12-11T06:21:30.046871Z\", \"created_time\": \"2019-12-11T06:19:53.662924Z\", \"created_time_dt\": \"2019-12-11T06:19:53.662924Z\", \"duration\": \"0:01:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.07393220636738744}, {\"run_id\": \"cnn_1576035422508121_251\", \"run_number\": 272, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:20:35.864088Z\", \"end_time\": \"2019-12-11T06:22:36.035607Z\", \"created_time\": \"2019-12-11T06:20:25.204692Z\", \"created_time_dt\": \"2019-12-11T06:20:25.204692Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0025879161516872934}, {\"run_id\": \"cnn_1576035422508121_252\", \"run_number\": 273, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:21:07.002255Z\", \"end_time\": \"2019-12-11T06:23:07.102098Z\", \"created_time\": \"2019-12-11T06:20:56.211657Z\", \"created_time_dt\": \"2019-12-11T06:20:56.211657Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.05697366342912485}, {\"run_id\": \"cnn_1576035422508121_253\", \"run_number\": 274, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:21:06.768112Z\", \"end_time\": \"2019-12-11T06:22:37.299624Z\", \"created_time\": \"2019-12-11T06:20:56.260225Z\", \"created_time_dt\": \"2019-12-11T06:20:56.260225Z\", \"duration\": \"0:01:41\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.005068080803721647}, {\"run_id\": \"cnn_1576035422508121_254\", \"run_number\": 275, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:22:08.293487Z\", \"end_time\": \"2019-12-11T06:23:06.612621Z\", \"created_time\": \"2019-12-11T06:21:57.820355Z\", \"created_time_dt\": \"2019-12-11T06:21:57.820355Z\", \"duration\": \"0:01:08\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.04775256509327261}, {\"run_id\": \"cnn_1576035422508121_255\", \"run_number\": 276, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:23:10.842597Z\", \"end_time\": \"2019-12-11T06:24:43.308198Z\", \"created_time\": \"2019-12-11T06:23:00.470758Z\", \"created_time_dt\": \"2019-12-11T06:23:00.470758Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.1537841444126284}, {\"run_id\": \"cnn_1576035422508121_256\", \"run_number\": 277, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:23:11.801221Z\", \"end_time\": \"2019-12-11T06:24:59.536465Z\", \"created_time\": \"2019-12-11T06:23:01.347028Z\", \"created_time_dt\": \"2019-12-11T06:23:01.347028Z\", \"duration\": \"0:01:58\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0021524659022451867}, {\"run_id\": \"cnn_1576035422508121_258\", \"run_number\": 278, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:23:42.634437Z\", \"end_time\": \"2019-12-11T06:25:15.119601Z\", \"created_time\": \"2019-12-11T06:23:32.41078Z\", \"created_time_dt\": \"2019-12-11T06:23:32.41078Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.005746508484502595}, {\"run_id\": \"cnn_1576035422508121_257\", \"run_number\": 279, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:23:42.786433Z\", \"end_time\": \"2019-12-11T06:25:42.876453Z\", \"created_time\": \"2019-12-11T06:23:32.53427Z\", \"created_time_dt\": \"2019-12-11T06:23:32.53427Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.05697643906609627}, {\"run_id\": \"cnn_1576035422508121_259\", \"run_number\": 280, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:25:16.48877Z\", \"end_time\": \"2019-12-11T06:26:47.288431Z\", \"created_time\": \"2019-12-11T06:25:05.216903Z\", \"created_time_dt\": \"2019-12-11T06:25:05.216903Z\", \"duration\": \"0:01:42\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01, \"best_metric\": 0.0047462862197187}, {\"run_id\": \"cnn_1576035422508121_260\", \"run_number\": 281, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:25:47.364268Z\", \"end_time\": \"2019-12-11T06:27:47.75655Z\", \"created_time\": \"2019-12-11T06:25:36.771239Z\", \"created_time_dt\": \"2019-12-11T06:25:36.771239Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.001, \"best_metric\": 0.006850723495937085}, {\"run_id\": \"cnn_1576035422508121_262\", \"run_number\": 282, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:26:18.310978Z\", \"end_time\": \"2019-12-11T06:28:22.524064Z\", \"created_time\": \"2019-12-11T06:26:07.843188Z\", \"created_time_dt\": \"2019-12-11T06:26:07.843188Z\", \"duration\": \"0:02:14\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0063991007285838985}, {\"run_id\": \"cnn_1576035422508121_261\", \"run_number\": 283, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:26:19.065521Z\", \"end_time\": \"2019-12-11T06:29:15.609533Z\", \"created_time\": \"2019-12-11T06:26:08.911Z\", \"created_time_dt\": \"2019-12-11T06:26:08.911Z\", \"duration\": \"0:03:06\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.006526976231114212}, {\"run_id\": \"cnn_1576035422508121_263\", \"run_number\": 284, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:27:22.332582Z\", \"end_time\": \"2019-12-11T06:29:25.828095Z\", \"created_time\": \"2019-12-11T06:27:11.290494Z\", \"created_time_dt\": \"2019-12-11T06:27:11.290494Z\", \"duration\": \"0:02:14\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.001, \"best_metric\": 0.007265252055227693}, {\"run_id\": \"cnn_1576035422508121_264\", \"run_number\": 285, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:28:24.393255Z\", \"end_time\": \"2019-12-11T06:29:36.478151Z\", \"created_time\": \"2019-12-11T06:28:13.915397Z\", \"created_time_dt\": \"2019-12-11T06:28:13.915397Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.00275379910618342}, {\"run_id\": \"cnn_1576035422508121_265\", \"run_number\": 286, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:29:31.578775Z\", \"end_time\": \"2019-12-11T06:30:45.929966Z\", \"created_time\": \"2019-12-11T06:29:16.330613Z\", \"created_time_dt\": \"2019-12-11T06:29:16.330613Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.01, \"best_metric\": 0.01935265478466629}, {\"run_id\": \"cnn_1576035422508121_266\", \"run_number\": 287, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:29:57.77511Z\", \"end_time\": \"2019-12-11T06:31:30.587072Z\", \"created_time\": \"2019-12-11T06:29:47.503116Z\", \"created_time_dt\": \"2019-12-11T06:29:47.503116Z\", \"duration\": \"0:01:43\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.07288514858532656}, {\"run_id\": \"cnn_1576035422508121_267\", \"run_number\": 288, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:30:28.876239Z\", \"end_time\": \"2019-12-11T06:31:47.72879Z\", \"created_time\": \"2019-12-11T06:30:18.607165Z\", \"created_time_dt\": \"2019-12-11T06:30:18.607165Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0016870571097181513}, {\"run_id\": \"cnn_1576035422508121_268\", \"run_number\": 289, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:30:29.420863Z\", \"end_time\": \"2019-12-11T06:32:29.47873Z\", \"created_time\": \"2019-12-11T06:30:19.453052Z\", \"created_time_dt\": \"2019-12-11T06:30:19.453052Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.0022766976401005254}, {\"run_id\": \"cnn_1576035422508121_269\", \"run_number\": 290, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:31:32.302342Z\", \"end_time\": \"2019-12-11T06:32:51.07365Z\", \"created_time\": \"2019-12-11T06:31:21.815922Z\", \"created_time_dt\": \"2019-12-11T06:31:21.815922Z\", \"duration\": \"0:01:29\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.001, \"best_metric\": 0.0029165615173202576}, {\"run_id\": \"cnn_1576035422508121_270\", \"run_number\": 291, \"metric\": null, \"status\": \"Completed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:32:34.533071Z\", \"end_time\": \"2019-12-11T06:34:21.964559Z\", \"created_time\": \"2019-12-11T06:32:24.040525Z\", \"created_time_dt\": \"2019-12-11T06:32:24.040525Z\", \"duration\": \"0:01:57\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 72, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.01, \"best_metric\": 0.005538097966151466}, {\"run_id\": \"cnn_1576035422508121_271\", \"run_number\": 292, \"metric\": null, \"status\": \"Failed\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:32:34.452274Z\", \"end_time\": \"2019-12-11T06:33:46.837101Z\", \"created_time\": \"2019-12-11T06:32:24.105664Z\", \"created_time_dt\": \"2019-12-11T06:32:24.105664Z\", \"duration\": \"0:01:22\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.001, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001}, {\"run_id\": \"cnn_1576035422508121_272\", \"run_number\": 293, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:33:40.925828Z\", \"end_time\": \"2019-12-11T06:35:40.973315Z\", \"created_time\": \"2019-12-11T06:33:25.933238Z\", \"created_time_dt\": \"2019-12-11T06:33:25.933238Z\", \"duration\": \"0:02:15\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 10, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.001982201870616462}, {\"run_id\": \"cnn_1576035422508121_273\", \"run_number\": 294, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:33:40.94974Z\", \"end_time\": \"2019-12-11T06:35:40.938205Z\", \"created_time\": \"2019-12-11T06:33:30.913386Z\", \"created_time_dt\": \"2019-12-11T06:33:30.913386Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.002211332643894926}, {\"run_id\": \"cnn_1576035422508121_274\", \"run_number\": 295, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:34:16.818674Z\", \"end_time\": \"2019-12-11T06:36:12.809216Z\", \"created_time\": \"2019-12-11T06:34:02.095798Z\", \"created_time_dt\": \"2019-12-11T06:34:02.095798Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 336, \"param_--alpha\": 0.1, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.05200431593273591}, {\"run_id\": \"cnn_1576035422508121_275\", \"run_number\": 296, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:34:48.111763Z\", \"end_time\": \"2019-12-11T06:36:44.121074Z\", \"created_time\": \"2019-12-11T06:34:33.249801Z\", \"created_time_dt\": \"2019-12-11T06:34:33.249801Z\", \"duration\": \"0:02:10\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 10, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.002131714282460159}, {\"run_id\": \"cnn_1576035422508121_276\", \"run_number\": 297, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:36:17.493138Z\", \"end_time\": \"2019-12-11T06:37:16.004165Z\", \"created_time\": \"2019-12-11T06:36:06.933176Z\", \"created_time_dt\": \"2019-12-11T06:36:06.933176Z\", \"duration\": \"0:01:09\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.1, \"param_--batch-size\": 16, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.0001, \"best_metric\": 0.06013051780382184}, {\"run_id\": \"cnn_1576035422508121_277\", \"run_number\": 298, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:36:17.922614Z\", \"end_time\": \"2019-12-11T06:37:16.296653Z\", \"created_time\": \"2019-12-11T06:36:06.942105Z\", \"created_time_dt\": \"2019-12-11T06:36:06.942105Z\", \"duration\": \"0:01:09\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0.001, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 5, \"param_--latent-dim-2\": 0, \"param_--learning-rate\": 0.0001}, {\"run_id\": \"cnn_1576035422508121_278\", \"run_number\": 299, \"metric\": null, \"status\": \"Canceled\", \"run_type\": \"azureml.scriptrun\", \"training_percent\": null, \"start_time\": \"2019-12-11T06:36:48.581527Z\", \"end_time\": \"2019-12-11T06:37:14.915359Z\", \"created_time\": \"2019-12-11T06:36:38.39718Z\", \"created_time_dt\": \"2019-12-11T06:36:38.39718Z\", \"duration\": \"0:00:36\", \"hyperdrive_id\": \"1576035422508121\", \"arguments\": null, \"param_--T\": 168, \"param_--alpha\": 0, \"param_--batch-size\": 32, \"param_--kernel-size\": 3, \"param_--latent-dim-1\": 15, \"param_--latent-dim-2\": 5, \"param_--learning-rate\": 0.01}], \"children_metrics\": {\"categories\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47], \"series\": {\"Loss\": [{\"run_id\": 23, \"name\": 23, \"data\": [0.020725890247929334, 0.010024593204118477, 0.0090704512542384, 0.008610154757208291, 0.00813136291520456, 0.00770590203766608, 0.007484984204353046, 0.007400034857961653, 0.007282407866886895, 0.00729360576049827, 0.007254644394192442, 0.007196389967649982, 0.0071574906415872615, 0.007152187698037686, 0.007106514356456776], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 21, \"name\": 21, \"data\": [0.2703369496872616, 0.22773402824621763, 0.22570779683582995, 0.22538378162919456, 0.22727198467366197, 0.2252387340491853, 0.22712072021551952, 0.22591024509425914, 0.22501070849494195, 0.22604564031811103, 0.22413187966067272, 0.22861547972370086], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 24, \"name\": 24, \"data\": [1.2096249070425766, 0.10960411661771415, 0.06615772762393722, 0.0583825847968068, 0.058184880167719506, 0.05817448622439281, 0.05817243116762363, 0.05818731975682279, 0.05817653050724673, 0.05817891026480512, 0.0581732069944914, 0.05817225834878085, 0.05817345092331906], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 22, \"name\": 22, \"data\": [0.03076997265262252, 0.008175760210557282, 0.0056808218693376765, 0.004958591833855083, 0.004589394948539549, 0.004373417961295208, 0.004205411354067922, 0.004083890159994858, 0.003981927860618078, 0.0038910003045137713, 0.0038233991142241227, 0.0037632556652162914, 0.003703055994903665, 0.0036582783796193217, 0.0036183787383747825, 0.003579742877051797, 0.0035466354813436813, 0.0035045546563670644, 0.0034789628693515117, 0.003442040040782551, 0.0034192719863799137, 0.0033892715835996544, 0.0033678503603222073, 0.00334438600994331, 0.003321242216626226, 0.0033091944076914844, 0.0032792073446839803, 0.0032575102289948586, 0.0032391728141596427, 0.0032145216700450816, 0.003194083783834053, 0.0031698281754692016, 0.003146275419471925, 0.0031258645500063047, 0.0031067223909093896, 0.003085339827669689, 0.0030666024587230147, 0.0030462778300738343, 0.003034412477396361, 0.00301703864304587, 0.003000088801748222, 0.0029816281111329046, 0.002970501286876181, 0.0029591535680040977, 0.0029454476740072037, 0.0029341507450983523, 0.0029170771963723313, 0.0029059401590500783], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 25, \"name\": 25, \"data\": [0.04315875319541883, 0.021856069529309775, 0.023090986253849154, 0.020610862465104154, 0.02079653782227352, 0.02251108557810127], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 27, \"name\": 27, \"data\": [0.011932541226715304, 0.0058159757577142335, 0.005502127115436964, 0.005371187337494845, 0.005363389434393737, 0.005289679083347334], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 28, \"name\": 28, \"data\": [0.020012319655632507, 0.007999727710523695, 0.0073496327233599145, 0.00696684785385182, 0.006736959128879693, 0.006551960610164656, 0.006487846729203167, 0.006495921641395714, 0.006472463777191906, 0.006468156913868959, 0.0064419165378570295, 0.006466997381040351, 0.006445927478862374, 0.006431415790800701], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 26, \"name\": 26, \"data\": [0.007974982754182146, 0.0033919609204377133, 0.003031223601585954, 0.0028334611712034865, 0.0026997010931022166, 0.0026084838723619214, 0.002532661279391498, 0.0024648397939351177, 0.002407174722286709, 0.0023601198730407737, 0.0023140440719742034, 0.0022730311461786, 0.0022309668040598877, 0.00220746883122479, 0.002170936770999074, 0.002140824466423349, 0.0021236447554446414, 0.0020953463211716675, 0.0020762316027810044, 0.0020551705771855527, 0.002024961239823994, 0.002005190034761482, 0.001987625648128381, 0.0019670763165523905, 0.0019496120655487807, 0.001930585434035283, 0.0019156204787715032, 0.001897162662540781, 0.001888067285943108, 0.001867873663483055, 0.0018532087484346323, 0.0018297173368130165, 0.0018226855810561332, 0.0018070071960307524, 0.001794204137217537, 0.001782563242399316, 0.0017724954381248504, 0.001756206383385971, 0.0017414035854717736], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 29, \"name\": 29, \"data\": [0.015928273556531494, 0.0043702041424295065, 0.0038729171501622607, 0.0036434854167937546, 0.003498512426189866, 0.0033982037496295995, 0.0033186885227759785, 0.0032585176516717997, 0.0032061527941885937, 0.0031586188817904243, 0.003122324599720796, 0.0030879940078069345, 0.003062199116090844, 0.0030399226351942405, 0.003014417226396061, 0.002995256299420114, 0.0029803638659801025, 0.00296570272800763, 0.0029458328312167124, 0.0029371944235875086, 0.002918872806177953, 0.0029068258068099904, 0.0028951978002065264, 0.002884368256402823, 0.0028719150971581752], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 30, \"name\": 30, \"data\": [0.02858427978678842, 0.023653779242626512, 0.023954590584338423, 0.026375947452321885, 0.026248055086784117, 0.024987752972222582, 0.027161162890173084, 0.02356652807815455, 0.02357696208638523, 0.026875965525753654, 0.025416047101955372, 0.023874670853647747, 0.025392000680904993, 0.024540323803657976, 0.025110614281784596, 0.02562571741302626], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 31, \"name\": 31, \"data\": [0.050296180348996786, 0.007731404010602529, 0.007531200968197464, 0.007513410740424328, 0.007399176048359526, 0.007326110299931329, 0.0073065431433869815], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 33, \"name\": 33, \"data\": [0.0101280261602366, 0.0061288729817573365, 0.005740640420789622, 0.005645968003894637, 0.005513936171858679, 0.005454007454428573, 0.005414907800799715, 0.005342740123637193, 0.005485569755993631, 0.005459067439557998, 0.005469979071242107, 0.005459544321484759], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 34, \"name\": 34, \"data\": [0.1310927687329395, 0.110372187474609, 0.10965568211962844, 0.10864540396498977, 0.10998881811753801, 0.10847739190967944], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 32, \"name\": 32, \"data\": [0.050312999225658454, 0.017058580532163737, 0.011354538568103801, 0.009445628576535272, 0.008558170495633292, 0.008027149569130382, 0.007675526475804347, 0.007402709787275062, 0.0071828490773050355, 0.007016353326436043, 0.006893278968033499, 0.006796992463904398, 0.006719189573363053, 0.006658403004513761, 0.006609570124684179, 0.006562419440844518, 0.006536608317091732], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 35, \"name\": 35, \"data\": [0.31848366502266723, 0.06282549421605248, 0.06276924362149454, 0.06271652482772969, 0.06250009930272644, 0.06208999592851842, 0.06185393562254809, 0.061801866315833856, 0.061814296153025146, 0.061771000161403876, 0.06180376401502484, 0.061761667736072286], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 37, \"name\": 37, \"data\": [0.036640510812990953, 0.011930235459508342, 0.008880352033478603, 0.007745483204014982, 0.00720908259250866, 0.006896203755836354, 0.006684117814697017, 0.0065291756662045255, 0.006410547344124539, 0.006315929847636027, 0.00624419739336274, 0.006178222649110007, 0.006128479093886468, 0.006081794982384891, 0.00603925800071368, 0.006004609449890867, 0.005977427962486409, 0.005943998416805246, 0.005922647919718653, 0.005909978914176248, 0.005897253981855162, 0.005878983058165305], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 36, \"name\": 36, \"data\": [0.031594961057095215, 0.025375268736095687, 0.024843308993877367, 0.025092588281434142, 0.027914394067404775, 0.025487044166127616, 0.027020946071254298, 0.02498764963502947, 0.025652913643416958, 0.026960947017093985, 0.026239168878568475, 0.029388190225875722, 0.026561134307378897], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 38, \"name\": 38, \"data\": [0.008156751634980924, 0.003614136675271483, 0.0030218952065593895, 0.0027188702753800495, 0.0025532414369366357, 0.002456834476022602, 0.0023692703551699426, 0.0022973647759331082, 0.002252599788513997, 0.0021901861766665458, 0.002150043889219996, 0.0021086460094307865, 0.0020779614683672626], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 39, \"name\": 39, \"data\": [0.011597154973560677, 0.00519823999111574, 0.005032048000069253, 0.0049277472341451006, 0.004865567212266943, 0.004808689557126866, 0.0048241364186445874], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 40, \"name\": 40, \"data\": [0.24784700273133828, 0.2273620827032968, 0.22700563089084255, 0.22647066125551957, 0.22780779332457282, 0.22737560235198576], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 41, \"name\": 41, \"data\": [0.00753229372800203, 0.004179139012541808, 0.0038032795828396486, 0.0035331140185891653, 0.003385755106840398, 0.0032602576175788205, 0.003180973438780802, 0.0031124532929580474, 0.003068587256062725, 0.003017169889027015, 0.002952132528486926, 0.002918676322626971, 0.002850004663020425, 0.0027843844839847984, 0.002738739366904434], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 42, \"name\": 42, \"data\": [0.24810149964006167, 0.2315895004713365, 0.22916587034201763, 0.24511341893696767, 0.23235745846295525, 0.23340883389068073, 0.23084643155772727], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 43, \"name\": 43, \"data\": [0.1666736943979855, 0.059403687569757474, 0.05942826368941008, 0.059419045591778345, 0.05944624066811765, 0.05937609517515041], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 44, \"name\": 44, \"data\": [0.01654931913397563, 0.009590787906879462, 0.008550071492826782, 0.008293658396099749, 0.00804440525308654, 0.0079884422619823, 0.007909579584619013, 0.007826001712161492, 0.007791954608536276, 0.007693027913772393, 0.007682748939877593, 0.007695109999188386, 0.007578127033581706, 0.00759966443368259, 0.00755048755998356, 0.007537388185405509], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 47, \"name\": 47, \"data\": [0.05535249015200597, 0.019141640678389926, 0.012749568358851607, 0.010502677277629177, 0.009463684088045864, 0.008858023892000326, 0.008486658173537763, 0.008190476113845458, 0.007958300981971764, 0.007771556670808102, 0.0076184575793780875, 0.00747459284052103, 0.00735553059664788, 0.007248359472776833, 0.007162039291970011, 0.00706830175318191, 0.006988809953895466, 0.006918786228184577, 0.006865354358931033, 0.006809352933850859, 0.0067629750459602365], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 45, \"name\": 45, \"data\": [0.1671804445710757, 0.15531914313985584, 0.1588824392493002, 0.15627306462593935, 0.15772399866609244, 0.15622735844620966, 0.1565818151240979, 0.15822119864283488, 0.15756524001726796, 0.15592674649568988, 0.15506146720431604, 0.15550047281277135, 0.15701618232114165, 0.15755198172628815], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 46, \"name\": 46, \"data\": [0.00805995617621529, 0.004050850275127548, 0.0033624195781546394, 0.0029880123919770836, 0.0027510227864155717, 0.0025653875881312617, 0.002450544956903462, 0.0023421641943785544, 0.002280681171689697, 0.0021993196174571483, 0.0021361495521808197, 0.002093027020599561, 0.002027902410519459, 0.0019753211868406375, 0.0019251978972904238, 0.0018835079123099296, 0.0018478056666013454, 0.0018115135051630362, 0.0017977103138656569, 0.0017661400911182042, 0.0017500065224034058, 0.0017298502459841786, 0.0017151761818946665, 0.0016986736423054067, 0.0016788291425407001, 0.0016692985242529107, 0.0016594158630983944, 0.001645146560622632, 0.0016367722603320837, 0.0016260237926343738, 0.0016143861137396566, 0.0016027969165992467, 0.0016028137829170268, 0.0015945244538724814, 0.001578657798095073, 0.0015729768956281582, 0.0015690847311251694, 0.0015637935606174686, 0.0015582027407787507, 0.001552487119881224, 0.0015492243354569733, 0.001541528528050731, 0.0015332787820541165, 0.001527197303406981], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 48, \"name\": 48, \"data\": [0.03301850830784975, 0.015283860858861112, 0.011552707772589817, 0.009671277510543723, 0.008494823214980733, 0.007665244072368838, 0.0070382778916750575, 0.006552708564905508, 0.006159578564350579, 0.005844158946128661, 0.005592941568120709, 0.005414488832731131, 0.005279133031253582, 0.005164392431993593, 0.005082832182040037, 0.005007717797766793, 0.004946814336060051, 0.004898529849849713, 0.0048446153906730885, 0.004814143729417488, 0.004777266812851603, 0.00474429523083783, 0.004729723067921489, 0.004692752424392213, 0.004660875626894362, 0.004654601935666511, 0.004615954642863078, 0.0046039135852626225, 0.004586454546000543, 0.004566183947449157, 0.004547393438490248], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 49, \"name\": 49, \"data\": [0.25471361954600785, 0.057289131380164116, 0.057005344791693995, 0.056983209782265745, 0.057158157287365646, 0.05713989563027669, 0.05703246180176419, 0.057047075929316265, 0.05706881902111381, 0.05695115713140514, 0.05703092516653336, 0.05698596844769697, 0.05701695643221337, 0.05709458947995481, 0.0571215704126171], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 50, \"name\": 50, \"data\": [0.3597933738231245, 0.32930134583105813, 0.3288611026035951, 0.32797380582403857, 0.32880070514867893, 0.32842171498258993], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 51, \"name\": 51, \"data\": [0.027251667786919398, 0.025664810622584206, 0.027551494593348866, 0.022458058754319415, 0.024897161708860447, 0.023171900992617886], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 52, \"name\": 52, \"data\": [0.05797146971025985, 0.03932499737141974, 0.036200033759239136, 0.030421003437560257, 0.035564899053733896, 0.027433408605662277, 0.03135216872972842, 0.024077694944222735, 0.02715836619270987, 0.028353649690740834], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 53, \"name\": 53, \"data\": [1.5279553556519323, 0.17900020186728327, 0.09988076457315578, 0.06667096901243841, 0.05864779667918494, 0.05819787240238064, 0.05818356278163503, 0.058196483551559826, 0.05819641175063368, 0.05819555693164216, 0.058173411413095934, 0.05817952624374016], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 54, \"name\": 54, \"data\": [1.0087205533584183, 0.1796831084425756, 0.1052715470707271, 0.0647595925117824, 0.046423096859752135, 0.041439519103046926, 0.04100144383123138, 0.04095499517643965, 0.04095285342018243, 0.04094946826408589, 0.040936152733208206, 0.040939024379797774, 0.04095457748475915, 0.04095580085280763], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 55, \"name\": 55, \"data\": [1.7264841932848676, 0.1546765524329309, 0.09412763085979392, 0.06921989628334266, 0.06008849031754383, 0.05827208378816546, 0.05821599472453492, 0.058209301886642355, 0.05819567123201647, 0.058206486505979954], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 56, \"name\": 56, \"data\": [0.04463137083949853, 0.04233088955115165, 0.03892982353547723, 0.04358064688314231, 0.03843570048971186, 0.04024219660821356, 0.03500164560198385], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 57, \"name\": 57, \"data\": [0.138111004139848, 0.11508475970605678, 0.11521794818193559, 0.11494088120171922, 0.11515254906147986, 0.11502881189719041, 0.11506150486915415, 0.11524874731302415, 0.1153923522151831, 0.1148115495177183, 0.11522890303259448], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 58, \"name\": 58, \"data\": [0.018509106438447102, 0.010073065765704964, 0.009371179421764715, 0.008757503322533122, 0.007934344528837048, 0.007553354426049514, 0.007445266972427896, 0.007335395484131191, 0.00722182806085571, 0.007171524431799889], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 59, \"name\": 59, \"data\": [2.1853418913410994, 0.39058950046012697, 0.1590869068768432, 0.07454747246505629, 0.06059681527446718, 0.060118047253553894, 0.06003454998617597, 0.060025223213612894, 0.05999664433970517, 0.060001036147182295, 0.05998972871947165, 0.059980308375039544, 0.05999752162508418], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 60, \"name\": 60, \"data\": [0.03984844813584086, 0.014040368194945908, 0.009818849497464085, 0.008310321963165042, 0.007578780745104492, 0.007144473771451586, 0.006838945285809793, 0.006613292183316497, 0.006457693066109748, 0.006319737079110843, 0.006198532349802192, 0.00610827869894929, 0.006014278078568645, 0.005943197823636328, 0.005862152134545319, 0.005808613071905428, 0.005752628443873929, 0.005721853474891772], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 61, \"name\": 61, \"data\": [0.006626110337614364, 0.0038427234066142936, 0.0031378389331750156, 0.0027046622002868076, 0.00245409587211686, 0.0022699220155715035, 0.002104669826383208, 0.00199940806998477, 0.0019200586521339676, 0.0018652372758639528, 0.0018215526093190372, 0.0017886816555501984, 0.0017651133092852574, 0.0017268186240333076], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 62, \"name\": 62, \"data\": [0.03393466869533441, 0.009737772389110408, 0.00752331087474605, 0.006924626479499365, 0.006596093508259269, 0.006376893061872171, 0.006209755222852779, 0.0060737511894051275, 0.005983835197977138, 0.005919646873912704, 0.005860967810450699, 0.005797537366912036, 0.005758678824945092, 0.005693454382905376, 0.005672292120321695, 0.005636649223714135, 0.005593497014660946, 0.005555269872303349], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 63, \"name\": 63, \"data\": [0.0057524150253443936, 0.0031324067165296697, 0.002794151512023309, 0.0025869883679258297, 0.0024458220748727047, 0.002349938682529365, 0.0022637364987456964, 0.0022109814859201937, 0.002149838238931521, 0.0021095306471094737, 0.0020457836363290847, 0.0020128813195705287, 0.0019685016590068475, 0.0019453390472286428, 0.0019066030783603436, 0.001866485347668095, 0.0018511903983739787, 0.001811288416960176], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 65, \"name\": 65, \"data\": [0.017938139807607466, 0.0060230395277719425, 0.005930595432965523, 0.005843740171516409, 0.005792237425846328, 0.00567048140301245, 0.0056508147048178956, 0.005645142751090821, 0.005647565159870833, 0.0056571522234965, 0.0056045679237317035, 0.005690302487825511], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 64, \"name\": 64, \"data\": [0.02050160238634761, 0.01903458525900768, 0.017009624012051736, 0.0180697002013343, 0.016580273673171327, 0.017532308003770175, 0.018218444048331717, 0.01770355932452615], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 66, \"name\": 66, \"data\": [0.21978872876177694, 0.00821519109218829, 0.007873453604146204, 0.00786150271195098, 0.007633916431255052, 0.007819855547774602, 0.007753547428969952, 0.007028574753630603], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 68, \"name\": 68, \"data\": [0.13968746606634172, 0.1158295694845356, 0.11565548149786999, 0.11564231035406608, 0.11594538820850589, 0.11589687785011228, 0.11587451634681602, 0.115698455927347, 0.1159183255290254, 0.11587675883300341, 0.11572968286084614, 0.115735692142452], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 69, \"name\": 69, \"data\": [0.3934815283545084, 0.3385012477141725, 0.3361934370178743, 0.3342519811581855, 0.3412999101816668, 0.33551322301295305, 0.33929026729977607, 0.34037850382776863, 0.3489367628473543, 0.3472299419860419, 0.33959753590920366], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 70, \"name\": 70, \"data\": [0.1759589635499315, 0.15551283220252987, 0.1550205764493887, 0.15516122818533037, 0.154915320503704, 0.15572320344840399, 0.15543756351623655, 0.15767376965326765, 0.15396478664895596, 0.15545551680865563, 0.15550702730656082, 0.15586906835092973, 0.15568177950055737], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 71, \"name\": 71, \"data\": [0.025243532043299176, 0.009580268493613204, 0.007201952862409617, 0.006191756212381032, 0.005679732346851016, 0.005388657349541867, 0.0052116468958092145, 0.005096211761070133, 0.005000605830148192, 0.00489785111167235, 0.004840825418227313, 0.004808806768692692, 0.004763115607971648, 0.004739796005066575, 0.004691734893988189, 0.004666461785962697, 0.004629526528412361, 0.0045808356002160254, 0.004546681763450931, 0.004535382293259736, 0.0045082342629293205, 0.004509836951435867, 0.004491774623799808, 0.00448313701089664], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 72, \"name\": 72, \"data\": [0.02566785258861653, 0.020315636672517363, 0.02173841387193637, 0.020048756338967976, 0.02173185357519541, 0.020543081834153834, 0.02055173214004298, 0.020333759157126045], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 74, \"name\": 74, \"data\": [0.39172117915686955, 0.07374356314587914, 0.07371949133702065, 0.07370143318103559, 0.07371583116033541, 0.0736879717554942], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 73, \"name\": 73, \"data\": [0.008722145920668329, 0.003762992196188941, 0.003211036510830292, 0.00286210952127277, 0.002686453475668057, 0.0025520370433363307, 0.0024686012493936307, 0.0023851792983084893, 0.002325335392269321, 0.0022785266249103517, 0.0022276500501962203, 0.002186563409614926, 0.0021526128007113192, 0.002115812831400767, 0.0020894860943461357, 0.0020659926777994693], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 121, \"name\": 121, \"data\": [0.01843202577786171, 0.009444196318698606, 0.007733338941246149, 0.007404265246830651, 0.007244121378292895, 0.007186314704710871, 0.007129032197067944, 0.006988799399263467], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 124, \"name\": 124, \"data\": [0.03296730929459874, 0.009217590093997063, 0.008683499932655226, 0.00837838348505322, 0.008210047100867566, 0.008136569892754303, 0.00802589435613964, 0.007949204564481525, 0.007830722870499967, 0.00780985842184933, 0.00774604323348293, 0.007671564439550432, 0.007650524499432556, 0.007591414502079985, 0.007599603539623186, 0.007536933376816077, 0.007572589497911293], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 123, \"name\": 123, \"data\": [0.23792498562856051, 0.07440074694038364, 0.07437010329028063, 0.07428422202868006, 0.07400288214801201, 0.07392567114442962, 0.07389346084598157, 0.07393953010953838, 0.0739158943172588, 0.07393880826825718], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 125, \"name\": 125, \"data\": [2.6038938920771635, 0.5032139155845873, 0.2613035360892293, 0.15626867205105535, 0.1002512947957001, 0.07805689582082344, 0.0735629250507759, 0.07293521650773974, 0.07289161877243806, 0.07288265622098403, 0.07287697407238304, 0.07288007667899199, 0.0728744949072908, 0.07287584897262739], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 126, \"name\": 126, \"data\": [0.20551570206061298, 0.07463708098906278, 0.07442521593901849, 0.07413603122807784, 0.07413799667354173, 0.07409506540357143, 0.07418443287955306, 0.07413201930596551, 0.07416020750251864, 0.07417163949404267], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 127, \"name\": 127, \"data\": [0.014902160556389954, 0.004480760133222029, 0.003909990064048359, 0.003662833520545509, 0.0035013869689878062, 0.003379211412916509, 0.003277050024187708, 0.003206312205882643, 0.0031405870176264977, 0.003091051490947564, 0.003041022727086338, 0.002994508051777621, 0.002958026126160799, 0.0029250933682867407, 0.002890712406824423, 0.002863035172745171, 0.002834138688197517, 0.0028127435817556887, 0.0027866498774582337, 0.0027588910016403136, 0.0027393283681925373, 0.0027160836141038856], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 129, \"name\": 129, \"data\": [0.010206595663502759, 0.006368536625283799, 0.005945854325067159, 0.0057991340532726485, 0.00570967366360792, 0.005560288601697943, 0.0056577410393431615, 0.005661435722767901, 0.005527675198949129], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 128, \"name\": 128, \"data\": [0.10807526988704991, 0.050989530866667195, 0.051016325157007984, 0.0510257690842871, 0.0510773835471151, 0.05106056556234155, 0.051071070406826145], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 130, \"name\": 130, \"data\": [0.024698581708479387, 0.0045811591730842505, 0.003931786639265943, 0.0036672964264637883, 0.0035137826975404295, 0.003412719340817338, 0.0033357038857230264, 0.003281894073379605, 0.003236831958175413, 0.0031993137319312206, 0.0031625379858831577, 0.0031251394943300997, 0.0030964384707777644, 0.0030721993148916525, 0.00304710638220643, 0.003023463396489042, 0.0030039490060486747, 0.002990386029443382, 0.002970725590453793, 0.002947397192398948, 0.0029325139770262454, 0.0029192569647089146, 0.002898206399835457, 0.002885721810894132, 0.0028715427760967684, 0.002858776089823617, 0.002842762501537736, 0.0028289014355727025, 0.0028211713357325354, 0.002809723329278572, 0.0027960069300802465, 0.0027850685169387717], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 132, \"name\": 132, \"data\": [0.009528619118102655, 0.0038727588134895023, 0.0032212230013425693, 0.0028911381470118065, 0.0026861112694787563, 0.0025581305796863888, 0.0024462097595114142, 0.0023730607142236327, 0.002307111201127886, 0.0022497312161194162, 0.002210520792067638, 0.002174990297306968], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 131, \"name\": 131, \"data\": [0.0383402244588527, 0.005213897452536053, 0.004360296628127709, 0.00404959827996584, 0.003859947263272567, 0.0037264692146491245, 0.003626979108236413, 0.003553406689894886, 0.003489794689162015, 0.003438664026162204, 0.003395754124711501, 0.0033564328633133274, 0.003325529390918468, 0.003294731960485694, 0.0032714674621864455, 0.0032421477396978996, 0.0032232222859149054, 0.0032056457227697057, 0.003187194327748571, 0.003167552089792755, 0.0031551687682901883, 0.003140531751616402, 0.0031271427739685215], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 133, \"name\": 133, \"data\": [0.029397295078347575, 0.020744636543003903, 0.020152426106405393, 0.019116538972536838, 0.0205932482577805, 0.021806811446446483, 0.020276793918370468, 0.020188389972299062], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 75, \"name\": 75, \"data\": [1.3678940687028915, 0.15805487686224684, 0.06413583696428393, 0.06049339520807844, 0.06015866238536289, 0.06010292557429491, 0.060093620045607476, 0.06008939282542445, 0.0600825878528202], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 76, \"name\": 76, \"data\": [0.00975258635376384, 0.005896686959180767, 0.005758036985304684, 0.005573229088300895, 0.005442962412720048, 0.005343386844169937, 0.005311561526985799, 0.005244806306413056, 0.0052479131865009075, 0.00519257232554387], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 77, \"name\": 77, \"data\": [0.007075421737186878, 0.004082407616056852, 0.0036025037456873165, 0.003348413495730578, 0.00320741291718591, 0.0030694950667926905, 0.002939325441156663, 0.002868515598535647, 0.0028064894968546417], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 78, \"name\": 78, \"data\": [0.005496909937143544, 0.003144270359030814, 0.002506884902792319, 0.0022153454191626517, 0.0020444861924582137, 0.001954057656649594, 0.0018820515863534918, 0.0018198878016602365, 0.0017693531733669336, 0.0017310225124430104, 0.0017166116118850119, 0.001702603261411281, 0.0016876690200731373, 0.0016708134096046637, 0.001646545816153777], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 79, \"name\": 79, \"data\": [0.05682198631751546, 0.033217017587036234, 0.030872515398890354, 0.03078053675727252, 0.03471341960131474, 0.030854545735240647, 0.0346043408803958, 0.039126852922254526], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 80, \"name\": 80, \"data\": [0.022785841057359955, 0.005782803622949343, 0.005451593559800505, 0.0053052043430535985, 0.0052025261253907705, 0.005149571701969397, 0.0050134975962196175, 0.004970866349890542, 0.004989381330679434, 0.004935871898298846, 0.004938000701376656, 0.004883573212567681], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 81, \"name\": 81, \"data\": [1.7933396545449642, 0.5309777121607174, 0.2217163625892085, 0.0919356080933466, 0.05790003300135238, 0.052501943517875606, 0.052228169563879184, 0.05220585246808125, 0.052208570270556046, 0.0521919116306293, 0.05222913865549489, 0.052216251055383606, 0.05220304676092649, 0.052225102394173155, 0.052211141865138054, 0.052233652530043244, 0.052233522598546994], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 83, \"name\": 83, \"data\": [0.01821197837162013, 0.009750037285293696, 0.00877991590032755, 0.008365684410522028, 0.008188910997936305, 0.007979605097035951, 0.007682475354118431, 0.007422958910879678, 0.0074078062527271485, 0.007393131542151278, 0.007353556813204704, 0.007335491237741804, 0.0073558427543649755], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 84, \"name\": 84, \"data\": [0.20428344151545424, 0.15551004421487305, 0.15568199363381074, 0.15571614919627616, 0.15553509767569162, 0.15530867274076948, 0.15544674814145884, 0.15597967833107032, 0.15557928289386613, 0.15558850782709902, 0.15580231059219704, 0.15559065049132165, 0.15567281168032135, 0.15548450472490113], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 85, \"name\": 85, \"data\": [0.006729675494640558, 0.0032295182539908915, 0.0027458809577960964, 0.0025208744692122283, 0.002325696868866968, 0.0021786538692989002, 0.0020568563301194154, 0.0019632377394237844, 0.0019012616448843896, 0.001852091928388251, 0.0018051971671675007, 0.0017905766175827262, 0.001755727082154504, 0.0017426125942039507, 0.0017101648665375322, 0.0016977974842257311, 0.001684453842442636, 0.001665804975736201, 0.0016517128828775996, 0.0016478049487589743, 0.001625621431521832, 0.0016322448923087227, 0.0016188935870784726, 0.0016155656466410383], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 86, \"name\": 86, \"data\": [0.014527743926654608, 0.008157943386374837, 0.007793609017949308, 0.007581472283874751, 0.007442010206435296, 0.007259752526369162, 0.007187899285218429, 0.007142919216848105, 0.007148945789451994], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 87, \"name\": 87, \"data\": [0.37304716146134187, 0.2326143695035144, 0.23788152340984564, 0.23652329447915077, 0.23372745031863418, 0.23683481732769462, 0.233232098470561, 0.23364830189232988, 0.23496396739660422, 0.2388406909823226], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 89, \"name\": 89, \"data\": [0.009604604643470375, 0.0061583453126694615, 0.006061965297289296, 0.005904427813434462, 0.005973902979547962, 0.005856904733536743, 0.005966033431383774, 0.005781642908455964], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 88, \"name\": 88, \"data\": [0.0451456604664834, 0.01643318116021376, 0.01253315434502532, 0.010728991151230395, 0.009764663250472157, 0.009136680816576377, 0.00869123820534407, 0.008363768667487175, 0.008112720606544516, 0.007882945377846421, 0.007670185730505573, 0.007511646356971897, 0.007351304288208003, 0.007207085122550153, 0.007068511442592015, 0.006958386842571143, 0.006846027574101244, 0.006750625014407144, 0.006644438879300493, 0.006567782165387439, 0.006479584486463768, 0.006411740543344474, 0.006340467651447545, 0.006281977199023625, 0.006228621378790042, 0.0061674852275058825, 0.006115827355786904, 0.006064958282133192, 0.006034440675709, 0.005985632498325016, 0.005936361565191362], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 91, \"name\": 91, \"data\": [0.015610137385983892, 0.009314107581821702, 0.008283720050908945, 0.007790045397569212, 0.007562248979469001, 0.007497254115336182, 0.007436647127609238, 0.007413285977440465, 0.0073886748650452685, 0.007383124391799118], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 90, \"name\": 90, \"data\": [0.007619385040410324, 0.004204989659788896, 0.0036007903464007934, 0.0032632984359893913, 0.00305687300885284, 0.0028908095264304177, 0.0027486681467629665, 0.0026888240370457, 0.0026204287264713147, 0.0025983363227044427, 0.002550135014367401, 0.0025090834853220916, 0.0024852853790911264, 0.002446937718761141, 0.002409641773860261, 0.0023887076382898696, 0.0023774775638885945, 0.0023508566486766065, 0.002330840195451436], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 92, \"name\": 92, \"data\": [0.01878117674608135, 0.009484037638309344, 0.008354653207333433, 0.007761486697565825, 0.007537080582342813, 0.007435894559163645, 0.007420363446635907, 0.007347825920781899, 0.007363317765828032], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 93, \"name\": 93, \"data\": [0.041083318733863075, 0.026600850408132384, 0.0250151996336699, 0.022863940053782332, 0.02239200498258821, 0.02314992736529335, 0.024315668022147997], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 94, \"name\": 94, \"data\": [0.009195863105502206, 0.0035300869640089044, 0.0030804001254146865, 0.0028400706989316687, 0.0027028656908601573, 0.002595907672724036, 0.0025134660947806003, 0.0024504674277020305, 0.002388273812846014, 0.0023276697177458407, 0.0022822428003322283, 0.0022392810175187, 0.002194688718451449, 0.0021595581483271824, 0.0021200633799537323, 0.002098538083042801, 0.0020684155741281097, 0.0020412244872894626, 0.002016327090262933, 0.002001324649635207, 0.0019689436919898026, 0.001955593157174411], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 96, \"name\": 96, \"data\": [0.00828489969507693, 0.004136542220777458, 0.0034588238296976675, 0.0031144267807767605, 0.0028496974581269006, 0.0026915336654038365, 0.002564426893895588, 0.0024713229856310154, 0.002391322827202215, 0.002278872424158897, 0.002219806186929776, 0.002150637478210458, 0.0020954079746401447, 0.002052621989421208, 0.001995195306968414, 0.001964645971722809, 0.0019075027098691877, 0.0018852346998479538, 0.0018389220900815843, 0.0018096223448300364, 0.0017915446650011754, 0.0017693798549019055, 0.0017443429152237675, 0.0017261701835313817, 0.0017015754092010817, 0.0016880552035095416, 0.0016621246432036668, 0.0016454236919215969, 0.001628912583543553, 0.0016216333113423323, 0.00160095536015865, 0.001578251617845031, 0.0015633399822259724, 0.0015564324238287039, 0.0015432167530753582, 0.0015338566808518227, 0.0015159097882403644, 0.0015120560548694288, 0.0015056484845395416, 0.0014921563051374907], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 95, \"name\": 95, \"data\": [0.021637175052455073, 0.005927183038515117, 0.005241814675562747, 0.005088850924275625, 0.004736546778341232, 0.004735566227555641, 0.004595066460601846, 0.004535114380728043, 0.004550006362037757], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 97, \"name\": 97, \"data\": [0.022921579038217618, 0.005138040848379891, 0.0043346342286813885, 0.003989254890090488, 0.0037953312850632194, 0.003667831202639086, 0.003558103109637392, 0.0034748884652792897, 0.0034027430871345953, 0.003347811705588566, 0.0032961711606906796, 0.003256605227303953, 0.003224510964114035, 0.0031869932796328776, 0.003159867590865739, 0.003136034886217146, 0.0031117340317406016, 0.0030930921359989456, 0.003079935575048017], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 98, \"name\": 98, \"data\": [0.073281636760147, 0.037723253122472, 0.03663607793287393, 0.03657190053350568, 0.035476442497495035, 0.02679607184530897, 0.02674855675086882, 0.02557830820490909, 0.025963812965326596, 0.022761660627009205, 0.028544570102354797, 0.028375393375799675, 0.03240837987497622, 0.025897621900240073], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 99, \"name\": 99, \"data\": [0.0065812709732170115, 0.003077682783924619, 0.0024855959060782026, 0.002180717334249956, 0.0019913814311801207, 0.0018560787698855016, 0.0017792358081197479, 0.0017221678624627353, 0.0016678661300334654, 0.0016307780971795652, 0.0016233322832329537, 0.001583848154992595, 0.0015603774933573918, 0.0015388208275550653, 0.001523607883647456, 0.0015055120028568342, 0.0014990011809775162, 0.001497901016791068], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 101, \"name\": 101, \"data\": [0.04813367836129146, 0.025808984785147312, 0.02748827340990363, 0.02632534720569187, 0.02658008660157873, 0.023474579264809725], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 100, \"name\": 100, \"data\": [0.12668959354021456, 0.11641764644296415, 0.11660949974749067, 0.11664770960316237, 0.11642469510696561, 0.1164233387147027, 0.11653495801961646], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 102, \"name\": 102, \"data\": [0.008445759760647611, 0.003923388473202713, 0.0033742310835959202, 0.0030738082058483674, 0.0028528479082496846, 0.0027181221115950616, 0.0025886046597374786, 0.0024826925202144686, 0.002383798868222816, 0.00231115174550875, 0.0022526269381734033, 0.0022096180473732477, 0.0021536579507756273, 0.002119186934864705, 0.002076830763703249, 0.002042943087899936, 0.0020092404978463503, 0.00198481163444031, 0.0019520160954086456, 0.001924986276484593, 0.0018969921083682986], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 103, \"name\": 103, \"data\": [0.016837107899311535, 0.007238516996835924, 0.007005903849838447, 0.007020739566148908, 0.006991062450445254, 0.007035429727507996, 0.0069821487674246435, 0.006967784217045131], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 104, \"name\": 104, \"data\": [0.025637234315660052, 0.009793367219291974, 0.006560094986027897, 0.0056521996260372335, 0.005248204886670687, 0.00501683276644459, 0.004891491345422733, 0.0048000782963038835, 0.004752364197950902, 0.0046907893660737944, 0.004637943934671079, 0.004594566507314579, 0.00457156985328825, 0.004555898075851705], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 105, \"name\": 105, \"data\": [0.016592147429110953, 0.009869089937456223, 0.008893565702887228, 0.008413909885319558, 0.00812348805036317, 0.007955353992779976, 0.007836764513798536, 0.007683092863725224, 0.00761953046204774], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 106, \"name\": 106, \"data\": [1.12240509991762, 0.17176963275803403, 0.08911696788158965, 0.05514758627653662, 0.04813916197840783, 0.047831720679497085, 0.04783502814422426, 0.04782233185325431, 0.047825317957585343, 0.04783778259864469, 0.04781312272450727, 0.04780832901179263, 0.047795061662661405, 0.047755043659940065], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 107, \"name\": 107, \"data\": [0.4794108434629833, 0.059381728158098335, 0.04142222127940661, 0.041153550442576375, 0.04112630571494945, 0.041107116348252606, 0.04110977104738165, 0.041086194671555484, 0.04104026455056524, 0.040970231927101024, 0.040946687393872236, 0.04096164102230907], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 108, \"name\": 108, \"data\": [0.018365283241542345, 0.008135629574968007, 0.0075454051708039056, 0.007155484963386795, 0.0070893682402538355, 0.00700193239108007, 0.0070293946579702725, 0.00693749673527807, 0.00697714635707325, 0.006880742134511401, 0.006891092797403433, 0.006874804859893138, 0.006864738306689763, 0.006862256081647212, 0.006851420427921854, 0.0068821519820716605, 0.006876726401676438, 0.006874748573831059, 0.006907255873353548], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 109, \"name\": 109, \"data\": [0.284226381490463, 0.06331897199664852, 0.06328230348478135, 0.06322639386426895, 0.06312445413681976, 0.06273568995973404, 0.062301642068352245], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 110, \"name\": 110, \"data\": [0.18028680633513328, 0.06414068075560125, 0.06379367049774631, 0.06304034053193153, 0.06228779526762801, 0.06219825494390946, 0.06215475685918591, 0.06215757293426222, 0.06214200360284737], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 111, \"name\": 111, \"data\": [0.05502913245355777, 0.016960063217602465, 0.011727620508053115, 0.00952514824461267, 0.008405585014501926, 0.0077353761329034655, 0.007288226481708066, 0.006961488257889578, 0.006721138863454555, 0.0065299190750779945, 0.0063925783243226324, 0.006284112571574846, 0.006205955784875716, 0.00614594549373417, 0.006100740430220635, 0.0060519621483319155, 0.0060117102060010166, 0.005981080214989061, 0.0059649020761534995, 0.005948305381407173, 0.005920398863348728, 0.005902643863222624, 0.005884280438711291, 0.005875548327788995, 0.005859048301933702, 0.005850789132024163], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 113, \"name\": 113, \"data\": [0.019472905664068403, 0.004552888749600562, 0.0038955997364769706, 0.0035938773151050433, 0.0034270944557599167, 0.0033177797213181713, 0.003245159787537562, 0.003194229851925008, 0.003149986887614862, 0.0031120495464353392, 0.003080037490664245, 0.0030518922901333003, 0.0030330508139960848, 0.0030093425287489907, 0.002992155413923766, 0.002978204674607951, 0.002957641593366653, 0.002940982891242516, 0.002930051044503244, 0.0029141657756146504, 0.0029118339196333954, 0.002897984958991666, 0.002886556377179966], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 112, \"name\": 112, \"data\": [0.007580463073480122, 0.003917075510916132, 0.0035738229533787604, 0.003362691986351075, 0.0032294445922833462, 0.0031002865257560115, 0.0030064090150824685, 0.0029456677641462358, 0.0028973933115989496, 0.0028522494561773575, 0.0028207900257191335, 0.0028041348403418046, 0.0027836619750393967], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 114, \"name\": 114, \"data\": [0.11699880662542077, 0.011310969730046475, 0.009929568299407594, 0.008989158592420296, 0.00830659963641792, 0.00774500168774664, 0.0078098757679093056, 0.0076095507612394695, 0.007618361312980013, 0.007524722507118689, 0.00737012180515877, 0.007468600294034785, 0.007427849573660499, 0.007398891374689728, 0.007422544081519083, 0.007370728187296353], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 115, \"name\": 115, \"data\": [0.02969006744235696, 0.022168025833385653, 0.02216975070658251, 0.02123085768913044, 0.02253751514685586, 0.022185755984611442, 0.02209120389609944, 0.021468865494321126, 0.02250831452561255, 0.024100921478974864, 0.021295780998630363, 0.022230384169045592], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 116, \"name\": 116, \"data\": [0.1103518609338361, 0.046697068200400337, 0.0379064170375098, 0.033218957704612126, 0.06045662082321274, 0.044736601598342314, 0.04720380254673785, 0.04223102759334392], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 117, \"name\": 117, \"data\": [2.2501176059397117, 0.30389411700283414, 0.15890564567472543, 0.10636813870291648, 0.07611441547693928, 0.0626954393171961, 0.05880100583961409, 0.05819928815958708, 0.05818720279173222, 0.058161514991913806, 0.05818003832610334, 0.05818468876114867, 0.058179683784428324], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 119, \"name\": 119, \"data\": [0.013223231334439776, 0.004986632511875648, 0.004363826496288923, 0.0038762110036495933, 0.003590242592022668, 0.003347491042128247, 0.0031917026323527837, 0.003009503012854118, 0.002919066782724589, 0.0028120517993333962, 0.002740711564586454, 0.0026600314596370972, 0.002606227019044361, 0.00256287842809234, 0.0025120981385350414, 0.0024741813665082805, 0.0024163645886631857, 0.002378427795052225, 0.002328968983315816], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 118, \"name\": 118, \"data\": [1.0588580687368612, 0.13613339388819032, 0.05499804347608508, 0.05271607217066019, 0.052521290741745955, 0.05246135160905084, 0.05245295786140806, 0.052431351550578265, 0.052466534189635504, 0.05245088248368338, 0.052430747732764175, 0.05239481322129709, 0.05240762191620887], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 120, \"name\": 120, \"data\": [0.04491469458535702, 0.015611190198690287, 0.01062063055001668, 0.008891169389786718, 0.008063286699166313, 0.007617310164043862, 0.00734908847052438, 0.007153821229125181, 0.007015025311129135, 0.006908877697596422, 0.006817967194744121, 0.006757018044379732, 0.006705150002161064, 0.006671015101590443, 0.006629167913468962, 0.006599246700749996, 0.006575850767477221, 0.006540731053429278, 0.006517890829327799, 0.0065082539507353475, 0.006486634587010012], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 134, \"name\": 134, \"data\": [0.05760924212742217, 0.0243737872710047, 0.016674876985406668, 0.012626340417988744, 0.010354338672171963, 0.008994676606078484, 0.008109986685467082, 0.007532766332067766, 0.007133723656970872, 0.006832247326929417, 0.006598911182591376, 0.006423786656815757, 0.006273261597731523, 0.006148041591328621, 0.006042549364647977, 0.005936487443489661, 0.005858722474250893, 0.005799748268478938, 0.0057463796162591785], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 135, \"name\": 135, \"data\": [0.10574303197415161, 0.007499886664979888, 0.007339075337697751, 0.007319097105330926, 0.0075644779498075295, 0.007834319002502163, 0.007898347573493425], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 136, \"name\": 136, \"data\": [0.08683642808143602, 0.0239154040463175, 0.01666417754624908, 0.013310432558413054, 0.011298110680231807, 0.01008522351350451, 0.009286794578106505, 0.008742783840417339, 0.008346108851558226, 0.008021957025319623, 0.007768329540538411, 0.007557695231536322, 0.0073900902911223495, 0.007249309706878216, 0.00713629024432113, 0.007040928510125662, 0.006943811072082788, 0.006878663265749528, 0.006808978910028479, 0.006759859625405155, 0.00671388584099389, 0.006667920587979052, 0.006633530474140249, 0.006596217607299005, 0.006566677780645184, 0.006536482212590826, 0.006510404179892875, 0.006492021288975576, 0.006465596963153954], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 137, \"name\": 137, \"data\": [0.09565879128842274, 0.035371097411430956, 0.03286529785958358, 0.03464846883138921, 0.03868279251491609, 0.03745169747947272, 0.03247675533597945, 0.040557439323964155, 0.034769510011444286, 0.03437499426565548, 0.035349513853406046, 0.04106571178232841, 0.03256081445955516, 0.03771713718761576, 0.03486459839323696, 0.03849214048864462], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 139, \"name\": 139, \"data\": [0.027861219119615588, 0.025360609040769724, 0.026005882559657894, 0.02101013794210365, 0.0264303274872856, 0.020318767842743286, 0.020692359202746074, 0.02016178781143776, 0.02457370766684982, 0.022378849918783958, 0.02171364470622214, 0.020377126977479107, 0.023819208220699842, 0.025970861395262224, 0.02507470572696296, 0.02364269473246711, 0.021268226902032943, 0.021437143826451902], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 138, \"name\": 138, \"data\": [0.03672279539094876, 0.006393500133675634, 0.005540464704597989, 0.005085573159459499, 0.004837203857400357, 0.004687935124802752, 0.004513385707598058, 0.004503868074100709, 0.004350960547924331, 0.0042520620970222965, 0.004199459079879177, 0.004220346217823984, 0.004170841513716161, 0.004178871357613034, 0.004175774705623784, 0.004122214508639576, 0.004158387481820835], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 140, \"name\": 140, \"data\": [0.12485009976818678, 0.006906539806123003, 0.005753051038729426, 0.005477697209775495, 0.005360223011487535, 0.0051844769407364925, 0.004937968751496212, 0.004855388943285589, 0.004897923431356682, 0.004899130704725185, 0.004819371107591107, 0.004875328568216477], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 141, \"name\": 141, \"data\": [1.911692553252874, 0.5201431343512153, 0.177475862243225, 0.06757918829052621, 0.053583400282472604, 0.05267382321962863, 0.052392187352584094, 0.05223097549098743, 0.05216801623500346, 0.052142149007470086, 0.05210291513293579, 0.05213020155073202, 0.05211298359108773, 0.05212619743353331], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 143, \"name\": 143, \"data\": [0.1601950989509067, 0.05753287361986492, 0.057546115157837745, 0.05761963012139674, 0.05749912596984914, 0.057664098022650094, 0.05752641894271259, 0.05764381750714392, 0.05753204564273774, 0.05751850635263898, 0.05764061807110053, 0.05755131522415852], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 144, \"name\": 144, \"data\": [1.2931294378082412, 0.18708425312334445, 0.12315405701219236, 0.08162303835350389, 0.05740399995715393, 0.045706182823479165, 0.04167245847267353, 0.041074539502903705, 0.04098515334262879, 0.04096472266670282, 0.040949191567934515, 0.040946675821793725, 0.04097156812109417, 0.0409658735116019, 0.040959204006894, 0.04098000327243261], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 146, \"name\": 146, \"data\": [1.2781911751941935, 0.16602773666842677, 0.0816587749390989, 0.07324336369484592, 0.07296196025814276, 0.07296461524586412, 0.07295165127274876, 0.07293972862572135, 0.0729455691846876, 0.07294398794560891], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 148, \"name\": 148, \"data\": [0.016025111075324613, 0.00543803573078509, 0.005236536737411719, 0.005232697177436026, 0.005148247534554578, 0.005171413530617895], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 147, \"name\": 147, \"data\": [0.4962471302823038, 0.03174089417665926, 0.026940195390382234, 0.03169616618024027, 0.02246595649272443, 0.03757721180683054, 0.027474251742837158, 0.02372509993315934], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 149, \"name\": 149, \"data\": [1.233540078142169, 0.17855007462693567, 0.07181500201823512, 0.060569933189711024, 0.06011250433587349, 0.060034614280171504, 0.06002070838221965, 0.06000562342964731, 0.06002118454035793, 0.060006807117060336, 0.05999990603614398, 0.06001414770799925, 0.060003659315527785, 0.060003472205692514], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 150, \"name\": 150, \"data\": [0.026134028922610273, 0.004881657581900551, 0.0042720818980604165, 0.003975268246386253, 0.0038130597453061963, 0.003692772890944404, 0.003617243256418617, 0.0035502525602677097, 0.0035023069836998236, 0.0034542961875218174, 0.0034179178158839866, 0.0033847117545302984, 0.003357718495142354, 0.0033352017676033163, 0.003311107530496257, 0.0032935216727569628, 0.0032711154097621333, 0.0032528348098343437, 0.003238193060180914, 0.0032212303586517787], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 151, \"name\": 151, \"data\": [0.008852505437837556, 0.005657735647663196, 0.005460396845056178, 0.005409735993855545, 0.005471111766516036, 0.005309404856136725, 0.005206839925094761, 0.005165872252362477, 0.005145038914356496, 0.005104421163433735, 0.005095630727028677], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 152, \"name\": 152, \"data\": [0.5598356703117218, 0.07178258135585924, 0.04284667228094247, 0.041172295391705915, 0.041140338179296514, 0.04112943612136523, 0.0410780488464572, 0.041061128899031026, 0.04103743115565202, 0.04104165602539335, 0.04105248737758344, 0.04103686681892319, 0.04102848713130967], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 154, \"name\": 154, \"data\": [0.6419561402895905, 0.07386420460043089, 0.048223594079468565, 0.0479317071367395, 0.047922331883503956, 0.0479258270501602, 0.04791851700419158, 0.04788956267002818, 0.04787751616336082, 0.047854667636233465, 0.04784199684835828, 0.04787211107354831, 0.047809981358399645], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 153, \"name\": 153, \"data\": [0.03145086546051999, 0.01079417565924011, 0.008021450097356914, 0.007145262304484675, 0.006735759707934992, 0.00647596875035694, 0.006275257850762994, 0.0061113932777111845, 0.006000370140756222, 0.005887480066605189, 0.00578950471216631, 0.005715921511981637, 0.005627939403536982], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 155, \"name\": 155, \"data\": [0.31729177186104507, 0.20587139878006672, 0.2057522165067036, 0.20591897512783078, 0.20556085652412462, 0.2063042007638947], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 156, \"name\": 156, \"data\": [0.005617952479229521, 0.0027519467021950217, 0.0023148410239247678, 0.002162672926470756, 0.002061834398588344, 0.0019872943728850706, 0.0019469012759171197, 0.0018875131637959828, 0.0018568382855686867, 0.0018168844892520876, 0.00178467768862624, 0.0017769405395119935, 0.0017546178356327895, 0.0017373265744081393, 0.001720449330375773, 0.0017043049748055436], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 157, \"name\": 157, \"data\": [0.05256660219361056, 0.028620664006359764, 0.021595963797066582, 0.017657138675576875, 0.015238628703790905, 0.01364042501794331, 0.012445193638231787, 0.011560061791338045, 0.010870987830678167, 0.010317154937385853, 0.009863304885564517, 0.00944823615364486, 0.009092893818891118, 0.008787286144110715, 0.008506415078743645, 0.008271209925229272, 0.008049608743596814, 0.00785085108504557, 0.007658051497642388, 0.00748098290679498, 0.007317940413761863, 0.007129982381496832, 0.006995908899487901, 0.006827412374897391, 0.006686459380908186, 0.006541869903510371, 0.006435671642332203, 0.006351558082229752, 0.006266144529622225, 0.006192191119589876, 0.00613529505075418], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 159, \"name\": 159, \"data\": [0.005918277108225036, 0.003471357518986383, 0.0029638430849624046, 0.002700330529014958, 0.002546484422704368, 0.0024346821204989694, 0.002341174482790643, 0.002269919361568073, 0.00221108511367365, 0.0021529503175637984, 0.002117118849006948, 0.0020675421371362, 0.002029624936471228, 0.001990721671846166, 0.001967865821231068, 0.001948503516579437, 0.0019144898965590133, 0.0018883583015942044, 0.0018905687829541158, 0.0018774769655952612, 0.0018590775173598574, 0.0018395874517682382], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 158, \"name\": 158, \"data\": [0.35358345375091604, 0.07466233368278302, 0.07453369412350852, 0.07429796749380548, 0.07430427286982419, 0.07425885968619793, 0.07410803536793799], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 160, \"name\": 160, \"data\": [0.015609534641039707, 0.007839461449587528, 0.007467918745319385, 0.007335982537059749, 0.007275794873466799, 0.007185768101449533, 0.0071192020508192575, 0.007089736047942984], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 162, \"name\": 162, \"data\": [0.025112361633249956, 0.007802962702541341, 0.007577255394583713, 0.007615007954117988, 0.007952724676820252, 0.007390031532450935, 0.00751474866085598, 0.007244423346733363, 0.007170816212619382, 0.007220292053610594], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 163, \"name\": 163, \"data\": [2.0151022272786627, 0.5022416483515266, 0.2086573777928007, 0.09136992935826353, 0.0634233561689699, 0.06019810515056018, 0.06007587720935448, 0.060041258926468215, 0.06002433241274893, 0.060023087066599645], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 164, \"name\": 164, \"data\": [0.2603202095769047, 0.2245333863112899, 0.22389578380662714, 0.22452364051228588, 0.2245419418806101, 0.2242782526606766, 0.22373544442996518, 0.22452631948819485], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 166, \"name\": 166, \"data\": [0.027951209220415198, 0.007601362230882072, 0.0071207255551276475, 0.007013005626399993, 0.006961299402258681, 0.0069904399440699205], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 165, \"name\": 165, \"data\": [0.12969070818030334, 0.010483383459071039, 0.010128908958324082, 0.009933925151521321, 0.009676044702748566, 0.00953698115193981, 0.009466654321804909, 0.009407243066241038, 0.009059515976203266, 0.008997366249860084, 0.00910049849831349, 0.00886806398838928], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 167, \"name\": 167, \"data\": [0.016121082663335055, 0.0036263132903281603, 0.0028719978594103144, 0.0025720917972192124, 0.002355594691428079, 0.002208685221418945, 0.0020741609259443687, 0.0019602456578703886, 0.0018902702194866718, 0.0018127522827622762, 0.001766386047527436, 0.0017113379537427211, 0.001669741845967488, 0.0016478991165532857], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 169, \"name\": 169, \"data\": [0.047029205597577076, 0.015306604787344736, 0.010485915819474298, 0.008715999337581047, 0.00790051957919197, 0.007458319318848462, 0.007178385129274358, 0.0069658943009861815, 0.006795412265757997, 0.00666098442073198, 0.006539413899785407, 0.006434937231385069, 0.006349500353465255, 0.006267106541095251, 0.006202425032305711, 0.006146596303337434, 0.006097237324821934, 0.006054490066846822, 0.006015634919581108], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 168, \"name\": 168, \"data\": [0.02050496036115848, 0.007725597439067133, 0.007034322941190699, 0.006920123615842474, 0.006710754057277988, 0.006500675142759295, 0.006500220042888403, 0.006465188430371878, 0.006452637388108548, 0.0064457740593996185, 0.006436058941020129], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 170, \"name\": 170, \"data\": [0.05182321246171919, 0.01099280323377307, 0.008604302721454366, 0.007706428127548504, 0.007187769851384812, 0.006827687402431573, 0.0065560162444401136, 0.006340630783914157, 0.0061625924817205495, 0.00600800259951249, 0.005896214657503905, 0.005775271588444376, 0.005697892271890089, 0.005627972929617352, 0.0055673253102979615], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 171, \"name\": 171, \"data\": [0.21607529294127237, 0.055590411953726146, 0.04678281598599437, 0.04480569522408512, 0.03359475105362566, 0.03851552880771525, 0.05539412034372254, 0.036426588373076066, 0.03103081823005821, 0.03577002280942001, 0.04007158027893469, 0.04503863784939565, 0.0515163899004225], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 172, \"name\": 172, \"data\": [0.17151442531909164, 0.05664672428717693, 0.05656866391657587, 0.05650744897707479, 0.05656752944388822, 0.05649181320375083, 0.0565569685067935], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 173, \"name\": 173, \"data\": [0.007762787406731666, 0.004308683806177845, 0.0038568933018266844, 0.0035162024819177518, 0.0033241756491855573, 0.0031862243633428234, 0.003116959416363154, 0.002992010801421869, 0.002901176616506208, 0.002828404054583005, 0.0027826183625887318, 0.0027379967064423217, 0.0026734007447977194, 0.002615412127385485, 0.0025357061749514058, 0.0024821633803978017, 0.0024211396839388705, 0.0023966127350059283, 0.002365176025777255, 0.0023341315655118727, 0.0023095438107326523, 0.002269728150554195, 0.002246775404976133, 0.0022195613475938956, 0.0022104281170468023, 0.0021893067899117925, 0.0021751262783209903, 0.0021568797960382015, 0.0021449207633146977, 0.002126991913329401, 0.002133144534999331], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 174, \"name\": 174, \"data\": [0.3476487613350664, 0.07465726735294288, 0.07434869271065687, 0.07420700868473015, 0.07424092639600395, 0.07396361463696455, 0.07394936148266547, 0.0739228466504792, 0.0739042383068324, 0.07392730849792363, 0.07390674024258358], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 175, \"name\": 175, \"data\": [1.584000370599991, 0.12598302229707029, 0.07375941592232682, 0.0730578659387866, 0.07293873345284552, 0.07291059236142891, 0.07290250018490564, 0.07290062487303332, 0.072902081246961, 0.07289642681217721], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 176, \"name\": 176, \"data\": [0.02203003344028077, 0.004770559455476464, 0.004065475813288568, 0.003761455972728684, 0.0035867527091465257, 0.0034801181265110225, 0.0033963916335404683, 0.003336808930910552, 0.003281922701042843, 0.0032355471919881373, 0.0031994543439884224, 0.0031708006639687455, 0.003135306108050891, 0.003109638095661906, 0.003087849708105272, 0.003065645660819659, 0.0030472206802605317, 0.0030208610323134975, 0.0030121323149020834, 0.0029952168249101534], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 177, \"name\": 177, \"data\": [0.026533197495960108, 0.01106158795874422, 0.009383408966507079, 0.008341179356190345, 0.00795051898941059, 0.007771836804045785, 0.0076631754541556085, 0.007553106862534007, 0.007458772630020762, 0.007438033369718756, 0.007361856441061399], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 178, \"name\": 178, \"data\": [0.13713600420335084, 0.11539456080876544, 0.1152883650525642, 0.11530129575744988, 0.11560033317140879, 0.11536910950671582, 0.11530400282146851, 0.1154732910958533, 0.1154450617138885, 0.11557065708613376], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 180, \"name\": 180, \"data\": [0.006411115823379709, 0.003421837225054721, 0.002855951340673474, 0.0025505891489809814, 0.002290000298271933, 0.0021558676689392062, 0.0020849037266734895, 0.002017470927361237, 0.0019899899948317284, 0.0019279904700636428, 0.0019103078715050927, 0.0018798126665922333], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 181, \"name\": 181, \"data\": [0.01587953141008668, 0.009010164470344912, 0.008126477729104305, 0.007587658957534678, 0.007500544546438366, 0.007423571233810566, 0.007363563094889844, 0.007368609320497474, 0.007352281937989399, 0.007337688606528911], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 179, \"name\": 179, \"data\": [0.02337664196313408, 0.01826131620506083, 0.018645623304955682, 0.018766492457516257, 0.018463488519470165, 0.018541735532109072, 0.018245631696892448, 0.018510768436168105, 0.018493840922550117], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 182, \"name\": 182, \"data\": [0.09642994011321666, 0.04876954692915239, 0.04876231228429467, 0.0488497192514224, 0.048746973447536944, 0.048816021229485404, 0.048829266753488094, 0.04879980663632872, 0.048807938669906084, 0.048793161221626406, 0.048878551616299476, 0.048821335589240386, 0.04882583407853565], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 183, \"name\": 183, \"data\": [0.010205518176128614, 0.005716996256136933, 0.005384753328390376, 0.005125327035464693, 0.005075393829245972, 0.00495807048294012, 0.004857694112147825, 0.004884304945805944], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 184, \"name\": 184, \"data\": [0.28020278618610545, 0.0627936181867588, 0.06271703855369737, 0.06277918018591874, 0.0626327393607433, 0.0621190648925235], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 186, \"name\": 186, \"data\": [0.16153121804916026, 0.05656992072872269, 0.05657085628171994, 0.05667437581385011, 0.05652285932089067, 0.05646897826686734, 0.05648648102468789, 0.056544815359228714, 0.05649433204089367, 0.05658927858157442, 0.056449554634164414, 0.05656554180575701, 0.056500835745015694], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 188, \"name\": 188, \"data\": [0.01155453656244462, 0.004103127944130254, 0.0037785417676304976, 0.0036101441194420266, 0.003500727291868314, 0.0034138963031713145, 0.0033581604993521354, 0.0032948699473501365, 0.003255671671621342, 0.003211230679330779, 0.0031886618354284055, 0.003156564795002247], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 187, \"name\": 187, \"data\": [0.02924513082129759, 0.010211690533957204, 0.008205648158269992, 0.007471236100783465, 0.006993143531746854, 0.006642199399027997, 0.0063358980910593185, 0.006073085806254558, 0.0058648929912287, 0.005710292843522446, 0.005576460933851814, 0.005451321612853212, 0.0053566275931044745, 0.005288883711396256, 0.005234973874115784], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 189, \"name\": 189, \"data\": [0.013881833268853165, 0.004517865546340473, 0.0036743692556439746, 0.003257863439196952, 0.002987622656694897, 0.0028083555912989428, 0.002675530043707672, 0.002572703974732744, 0.002488237608638043, 0.0024138495900496684, 0.002362163348805465, 0.00231305007094509, 0.002277424032161808, 0.002235771695765901, 0.002208344636021039, 0.0021765257411938627, 0.0021513755015365944, 0.002128533636553088, 0.0021048029534342385, 0.0020805833432108044, 0.0020644650169725394, 0.0020443128098851025, 0.002023569994938719, 0.002009010284638119, 0.001997570716226677, 0.0019812031105329764, 0.001969541415377886], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 190, \"name\": 190, \"data\": [0.1345709950195228, 0.05982369817734164, 0.05983658763088981, 0.059813933611417944, 0.0598416035585011, 0.05981961937761968, 0.059819588226057666, 0.059784411036713746, 0.0597876751139593, 0.05986142697308073], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 191, \"name\": 191, \"data\": [0.2749250697927491, 0.22264110533621007, 0.22320913625886335, 0.2250164202368528, 0.22297739786443369, 0.22301359285424513], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 193, \"name\": 193, \"data\": [0.008620068565262486, 0.00522922712120301, 0.004978656621215743, 0.004870466091240258, 0.004894803445403004, 0.004821872690963848, 0.004794628387429872, 0.004783834353424602, 0.004737953442852887, 0.004763056986228894], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 192, \"name\": 192, \"data\": [0.2820005448292061, 0.06326285097486942, 0.06262712977699661, 0.06234705006610944, 0.06228552646910884, 0.062280701388598314, 0.062252012882699014, 0.06237140275394965, 0.0623060481003808, 0.06224243860030601], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 194, \"name\": 194, \"data\": [0.01625064820711892, 0.008233198607944909, 0.007749674478349368, 0.007528142631049924, 0.007424954806823433, 0.00741393914359267, 0.00739907253536242, 0.007346217012309931, 0.007374370724945591, 0.007374902857066457, 0.007349441096989589], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 195, \"name\": 195, \"data\": [0.03366620395516831, 0.0051558432901123484, 0.004389565866860172, 0.004053915585034592, 0.0038642372981734416, 0.003727622030762203, 0.0036292059562072565, 0.0035562012722632286, 0.0034911643468110917, 0.003440163649044495, 0.0033991219821135977, 0.0033599605116981027, 0.003330115977617758, 0.003301744694231444, 0.0032785355038160036, 0.0032612267131325325, 0.003239239493741709, 0.00322264659284676, 0.003211631706569885, 0.0031945878026022815, 0.003183132533644397, 0.0031682586725510806, 0.0031601394250775983, 0.003150409456391423, 0.0031415298782941246, 0.0031357802846431982, 0.003126890264115034, 0.0031194181139206673, 0.0031116535918884972, 0.00310609908423464, 0.0030986940943799656, 0.003089018474532566, 0.0030883068893807096, 0.0030714788974866947, 0.0030779243071513258, 0.0030666305601945985, 0.0030644709527255544, 0.0030558590074376024, 0.0030549353613836166, 0.0030465318060223117, 0.0030417262315517303], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 196, \"name\": 196, \"data\": [2.313687072879214, 0.4740322539972883, 0.24670606191807262, 0.13230484703562984, 0.08736597072662887, 0.07528045896877103, 0.07308511846197936, 0.0729299785646037, 0.0729206776049051, 0.07292103805830427, 0.07292361216685457, 0.07292580267595347], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 197, \"name\": 197, \"data\": [0.00847899308320943, 0.002837642250468391, 0.0023530754561151986, 0.0020937394140888776, 0.0019572320312868357, 0.0018901429058355219, 0.0018520962368673783, 0.0017922911297025936, 0.0017682394289591703, 0.0017573086836969243, 0.001732618861690956, 0.0017029088610284642, 0.001698041937845083, 0.001688642590155977, 0.0016690701143138166, 0.0016641365048132158], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 198, \"name\": 198, \"data\": [0.046408987252342174, 0.01880339830606417, 0.011753026429377148, 0.008960196013214931, 0.007755860314877778, 0.007191762302717283, 0.006872622670248334, 0.006653372731313731, 0.006485219470771072, 0.006367529441723551, 0.0062624869448402575, 0.006164894040280515, 0.006082422761446041, 0.006016061821576243, 0.005942312947918252, 0.00586601253115403, 0.005799047639843135, 0.005739965740820791, 0.005680765794042174, 0.005614343296934583, 0.005564159027484029, 0.005535413172791481, 0.005484850487439579, 0.005461328614336602, 0.0054286309450064605, 0.005402914238028287, 0.005368073592520433, 0.005344602989965361, 0.005339135751973052, 0.005317719610682364], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 200, \"name\": 200, \"data\": [0.023905876884082917, 0.005226300463044323, 0.004441989512285741, 0.004088304668272018, 0.003882742092179134, 0.0037406097721849477, 0.0036327200019359686, 0.00354276919667162, 0.003471170166200696, 0.0034057509769855943, 0.003358244956245406, 0.0033049081809582563, 0.0032685031826171006, 0.003225683699229458, 0.0031984641862135036, 0.0031700882600692957, 0.0031390089695007253, 0.003116073502252126, 0.003087721009374351, 0.003071636792234996, 0.0030529203308933195, 0.0030372846868399173, 0.0030274794999121057], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 199, \"name\": 199, \"data\": [0.16754302316977338, 0.15674775233293417, 0.15636326994570982, 0.15679178911279135, 0.1569874469463183, 0.15650728926310026], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 201, \"name\": 201, \"data\": [0.26336993338996534, 0.056335248994876214, 0.05603667625288838, 0.05607561490727055, 0.056078708598285586, 0.05600695836415593, 0.05607128668997773, 0.05598762355527637], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 202, \"name\": 202, \"data\": [0.218782059690524, 0.20817567927392105, 0.20805303537951014, 0.2077188635562704, 0.2091239044435944, 0.20776078155875233], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 205, \"name\": 205, \"data\": [0.022483666752062273, 0.004831331662732401, 0.004165079962032709, 0.0038957957624873854, 0.003720917258131461, 0.003597832241563924, 0.003510642795904762, 0.0034355080326597697, 0.003374951171907275, 0.0033188097537247773, 0.003266181436727245, 0.0032272239787602725, 0.003191156516101319, 0.003163056805333671, 0.0031357729379818153, 0.0031157261468567703, 0.0030912306161744903, 0.003072080498906974, 0.00305788335345385, 0.0030397237939579673, 0.0030204362000462465, 0.0030099846787144586], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 204, \"name\": 204, \"data\": [0.022840310250796622, 0.010166695684138239, 0.008317244824683394, 0.007814396239847865, 0.007594569844200277, 0.007451260834136257, 0.007394860473444928, 0.007370283364504608, 0.007368662372661282, 0.007348126523594492, 0.0073333295450978804, 0.007302198445655738, 0.007271907025542771, 0.007304372883349687, 0.00727518765799617], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 206, \"name\": 206, \"data\": [0.007153333584268726, 0.0041838672271433675, 0.003631597121807129, 0.00327752329211945, 0.0031068165424372237, 0.0029547164260601507, 0.002850758041952878, 0.0027654242683946896, 0.0026927167232861254, 0.002608816521423877, 0.002573718430898158, 0.0025092755691329756, 0.0024683672696469454, 0.002427202400232446, 0.0023937214966676976, 0.002350359636339595], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 207, \"name\": 207, \"data\": [0.130639919348304, 0.11577794768258953, 0.11580751633241892, 0.11560058613239728, 0.11570757041451223, 0.11564433579188914, 0.11562524874243846], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 208, \"name\": 208, \"data\": [0.021018563621786827, 0.011103287273742381, 0.009547426548812802, 0.00900975509126738, 0.008841159320068412, 0.008589185332153448, 0.00853509122272559, 0.008374231831399757, 0.00828960792511888, 0.008205417659533927, 0.008140907513989522, 0.00807405586303897, 0.00803522396761167, 0.008030137101512567], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 210, \"name\": 210, \"data\": [0.006115906103157633, 0.0033346254775227916, 0.002752363079396017, 0.0024293787239056345, 0.0022615447582776224, 0.00217675868301185, 0.0021198162607917333, 0.0020933050393145583, 0.002032715484814456, 0.002003778722736738, 0.001982711102448043, 0.001953074422430754, 0.0019408077883586554, 0.0019293987559962432, 0.0019108238242012653], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 209, \"name\": 209, \"data\": [0.03429664751038874, 0.011674677189275258, 0.009101138511242824, 0.008181381796412994, 0.007633447922400753, 0.0072370038988136594, 0.00693230326822968, 0.00666257415867757, 0.0064516539879081085, 0.006261224880777813, 0.006128004814553196, 0.00598250533649432, 0.005873852832780268, 0.005761652145270616, 0.0056875549972904785, 0.00559465864901222, 0.005533867344771175, 0.005479637204487376, 0.005409897586810898, 0.005336125672013105, 0.00530158337310446], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 211, \"name\": 211, \"data\": [0.009995592470979048, 0.003893772045576917, 0.003514846733201894, 0.003353829871892169, 0.0032668747025448395, 0.003200954594746383, 0.0031538526608990815, 0.0031434375724575733, 0.0030977112000327997, 0.0031052026577340435, 0.0030684387890890764, 0.003068088662295498, 0.003045131768589647, 0.0030320507628377306], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 212, \"name\": 212, \"data\": [0.008000183803222616, 0.003941807739484951, 0.0034253870599556856, 0.003227557847122316, 0.003057715508757098, 0.0029552745319839533, 0.0029012740698167905, 0.0028489521134723635, 0.002788278330343084, 0.002760561070484994, 0.0026966536367079015], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 213, \"name\": 213, \"data\": [0.005949096714699805, 0.003455962358793424, 0.0031051859535304854, 0.002937972471607217, 0.002790570619246171, 0.0026961174856757037, 0.002614645301895809, 0.0025560298302780378, 0.0024908039178358427, 0.0024401900726590733, 0.0023989663055624805, 0.002376716188847176, 0.0023448347342281783], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 215, \"name\": 215, \"data\": [0.008696762839319663, 0.005286736297466161, 0.005027029530992623, 0.0049074142362438705, 0.004833460865010605, 0.004889682227776295, 0.004756902013413003, 0.004712092223653448, 0.004734636429143786, 0.004707428443739913, 0.0046589534273998754], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 214, \"name\": 214, \"data\": [0.07531616785277662, 0.03343003979396638, 0.03607084771719646, 0.032490301042440405, 0.02945088761169042, 0.028750095594940267, 0.03308146659273663, 0.030517426367530107, 0.03274436905779741, 0.03638197366346823, 0.031916778175992276, 0.032537384014940024, 0.03346442921388914, 0.033191756650377546, 0.03508263706917204, 0.036523042169820816, 0.03601094226004449, 0.03571193182088586, 0.031345633804342035], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 216, \"name\": 216, \"data\": [0.036538855058332056, 0.012352718436455442, 0.007665818626173957, 0.006180612286217374, 0.005687701217095085, 0.005461044036272125, 0.005337553033452575, 0.00524855564728601, 0.005166862151310963, 0.005105995532462254, 0.005049033985019906, 0.005000838472025663, 0.00495605677968192, 0.004921106663961361, 0.004900218205916514, 0.004871444459940298, 0.004845674466416047], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 217, \"name\": 217, \"data\": [0.050629077905797674, 0.028470724280189617, 0.0349566606577249, 0.028942086894877466, 0.033468897890908275, 0.02857265327909625, 0.0286502359718118, 0.02693534602335467, 0.02910985201996982, 0.027022871256391582, 0.03566176113779173, 0.039962955352982124, 0.03305299337080151, 0.025061590317600312, 0.029270752070422845, 0.032608013876469055, 0.026496990404952854, 0.029835036122774838, 0.023821670553693636, 0.02761212474285259], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 218, \"name\": 218, \"data\": [0.02219809608197898, 0.009660310111515052, 0.009629234008454784, 0.009666686075684413, 0.009468006557492553, 0.00915248869916385, 0.008937223629939636, 0.008833282699228815, 0.00869666941554745], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 219, \"name\": 219, \"data\": [0.015564929409629846, 0.004243217180074321, 0.0036946242098656965, 0.003422197690669424, 0.003251260102091645, 0.0031139902409398276, 0.003023517953255229, 0.0029539632326478437, 0.0028858268132575093, 0.002829070737818808, 0.002782971033740508, 0.0027522767918674064, 0.002713739502984618, 0.002682595655975025, 0.0026444267091623105, 0.002619317457240966, 0.0025902857165140936, 0.0025652718869858785, 0.0025447143395611097, 0.00251956084320964, 0.0025023708160458896, 0.0024791010185081577, 0.0024604312503633667, 0.0024252121493217116, 0.0024067282149174943, 0.002385774247814472], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 220, \"name\": 220, \"data\": [0.008250345903072338, 0.003346062674542667, 0.0027372603200979087, 0.0024355734439434258, 0.0022946911937907445, 0.0022051745821589255, 0.0021417027370454825, 0.002117711443336039, 0.0020890310279177804, 0.002055856042764552, 0.0020426409805906734, 0.0020312242045209235, 0.002021048593052588, 0.001999847658530887, 0.001999021270067976, 0.0019747573342011834, 0.0019634427227172204, 0.0019560782659801533], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 221, \"name\": 221, \"data\": [0.030949295277299362, 0.011379307509673704, 0.007911045646268038, 0.0066270897756432095, 0.006048801542192068, 0.005756484680275433, 0.005602989462761625, 0.005475614108976739, 0.00539202579293711, 0.005314890458253577, 0.005261435498360361, 0.005213882934108476, 0.0051555922287858744, 0.0051127586628842425], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 222, \"name\": 222, \"data\": [0.14697729881090718, 0.05961908033722266, 0.05962384886753876, 0.059609134915903625, 0.05959429365346063, 0.059655922406981234, 0.059618847619308996, 0.05963304525524561, 0.05965762605147286, 0.05963802635544897, 0.05957448892037738, 0.059643938902077934, 0.05963111201615725, 0.05966115013705174, 0.059665451432020976, 0.05964153286154919, 0.05961697217958535], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 223, \"name\": 223, \"data\": [0.052431625106606254, 0.02176525300025465, 0.012949522804468384, 0.009627476533666973, 0.008327200499719273, 0.00766483376743953, 0.007251070043134498, 0.006926246603859656, 0.006647880086265402, 0.006376284229993871, 0.006122547272459581, 0.0059446284534540655, 0.005841977165457352, 0.0057517849233814115, 0.005676900275765674, 0.005594097327479173, 0.005539218652545117, 0.005496347076805998, 0.005421888737678275, 0.005388707255576969, 0.005347352956450442, 0.00532066555692208, 0.0052754667470803875, 0.005254201851754432, 0.0052276043994974935, 0.0052016252577481254, 0.005176860129620681, 0.00515807645077202, 0.005144864267809982], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 224, \"name\": 224, \"data\": [0.005922222721718975, 0.003596346815700395, 0.003071002773261703, 0.002828910147870168, 0.0026959735335073546, 0.0025928503081884486, 0.0025169498282695928, 0.002440287338598043, 0.002365811542189567, 0.0023285257602247717, 0.002276796262730008, 0.002230738801207111, 0.0022097761814799557], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 225, \"name\": 225, \"data\": [0.016534452344397363, 0.00933543828972371, 0.00870688332844087, 0.008174066926184225, 0.0077857174685585, 0.007641488344573667, 0.007554028029068149, 0.007506292332279006, 0.007447475628185444, 0.0074195785493639185, 0.007375393793134723, 0.007334340867345573, 0.0073114777567348885], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 226, \"name\": 226, \"data\": [1.1029767315424233, 0.18249079413054856, 0.07277542541664378, 0.06057730201814152, 0.060245608150354873, 0.0602166268125366, 0.06021113008540305, 0.06019768250516861, 0.06021110760753001, 0.060201932818295244, 0.060190684545977195, 0.06019617718099545, 0.060186654524200814, 0.060182514591016, 0.0601867002439648, 0.06018647691357462, 0.060195201105127466, 0.06017457125630131, 0.0601698442515896, 0.06020329516812442], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 228, \"name\": 228, \"data\": [0.021885026716110936, 0.01911690446606289, 0.0193259391983604, 0.018870016636885277, 0.018547126558378046, 0.018719845940697966, 0.018554068710975802, 0.018980438855700818, 0.019143186002204233, 0.018795922451611904, 0.018608890033163548], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 227, \"name\": 227, \"data\": [0.014858228212022815, 0.00769784288239461, 0.007304805202129646, 0.007208055377019385, 0.007156367540312901, 0.007095354005107116, 0.007096629460953402, 0.007032536675541764, 0.007045628925785824, 0.007004132615621263, 0.0069978710048258985, 0.0069806272609865124, 0.006962029682080699], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 229, \"name\": 229, \"data\": [0.015413605673843181, 0.006587081561047815, 0.00627902199753016, 0.006245697083034815, 0.006196696691004954, 0.00619054604591856, 0.0061561150149760996, 0.006139020222595296, 0.006127412691780667, 0.006148351566974095], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 230, \"name\": 230, \"data\": [0.019100661567968795, 0.009907592480185924, 0.00921997075553511, 0.008858042198938201, 0.008641046876881737, 0.008405987793849906, 0.008373396011146214, 0.00818881544627603, 0.00809041505738806, 0.007991329209157435, 0.007920112837151792, 0.007899197344198644, 0.007859291073742333], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 231, \"name\": 231, \"data\": [0.024329722865005933, 0.005959054769388955, 0.004568251056081311, 0.004201596138456338, 0.0040131063627731264, 0.0038953756311919407, 0.0038028816456670196, 0.003730908971214894, 0.0036715647482944993, 0.003621393728980362, 0.003575100499810905, 0.0035310173086368552, 0.0034859033137478123, 0.003453668995420071, 0.0034180191878720144, 0.00338803788730543, 0.0033604832050220888, 0.003329331749450094, 0.00330723502710532, 0.0032904618546018023, 0.0032662420435408766, 0.003244255621420742, 0.0032283472158707634], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 232, \"name\": 232, \"data\": [0.042650676542858985, 0.00671767870578825, 0.006636452105576237, 0.00654708952157631, 0.006613193444799874, 0.00662148680080957], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 233, \"name\": 233, \"data\": [0.26702065861946217, 0.05937132154518215, 0.05916526666196944, 0.0591495765985085, 0.05912167413768979, 0.05913233841858565, 0.05918873196617291, 0.05916226989834493, 0.05911534516501891, 0.05912780622250384], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 234, \"name\": 234, \"data\": [0.3801676494113866, 0.3242844358722978, 0.32394507713931503, 0.32363284611756865, 0.32482957725117406, 0.32422593539599237, 0.3241944342570668, 0.32586561311139123, 0.32418891175478864, 0.3237685643192501], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 235, \"name\": 235, \"data\": [0.3748787445868626, 0.325184103900543, 0.32504994066484477, 0.3265098028628497, 0.3250900635740741, 0.32588221165060444, 0.3249271920203085], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 237, \"name\": 237, \"data\": [0.38348837789647494, 0.07419749828918218, 0.07392808999089379, 0.07373206555845914, 0.07370410930279077, 0.07371858129153254, 0.07372734999283909, 0.07369449515532374, 0.0737454862895124], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 236, \"name\": 236, \"data\": [0.05589100476832374, 0.031838225458738795, 0.03894499830615618, 0.04653731558467098, 0.03512638925112975, 0.039301818639203887, 0.044026282166827975, 0.03199230058953015, 0.03300387530431154, 0.03149803058726921, 0.03939254101544959, 0.03326604177796535, 0.03767382824929631, 0.035760993717476514, 0.03545620351586332, 0.03368735663449813, 0.03445796256546306, 0.034293867410328305], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 238, \"name\": 238, \"data\": [0.019552214033556236, 0.007190565226114732, 0.006147813649294974, 0.006177120689517187, 0.006155454284690862, 0.0061378612959906475, 0.006118425742817646], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 239, \"name\": 239, \"data\": [0.007019224317166815, 0.0034960532750025254, 0.0029059595054292038, 0.002483865519421693, 0.0022358669290222706, 0.0020285096093370526, 0.0019132993187772826, 0.0018072815374909658, 0.0017518792689652923, 0.001692362638710047, 0.0016631805394713975, 0.001643238996128086], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 240, \"name\": 240, \"data\": [0.03747767371168369, 0.016280081452760633, 0.011368175618204518, 0.009212867561900132, 0.00824434916039006, 0.0076745814033748975, 0.007296074128627554, 0.006981396531844718, 0.006689717990995715, 0.006410935621545325, 0.006231023141583496, 0.0060985716343930785, 0.005990897277228882, 0.005880775598345586, 0.005782150954016033, 0.0057209886760582924, 0.005652835012801636, 0.005586053925306323, 0.0055330156619500855], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 241, \"name\": 241, \"data\": [0.0058008021963324476, 0.002861698647624495, 0.0024232207535676118, 0.002188453884029679, 0.0020223222225475967, 0.0019257073579952086, 0.001820387174320124, 0.0017582777667548362, 0.00172032753962903, 0.001701250763492017, 0.001664322301112664, 0.0016395944907797437, 0.0016279202474443607, 0.0016174573772369802, 0.0015939969560583396, 0.001569734212104486, 0.0015696662715218015, 0.0015591654600755624], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 242, \"name\": 242, \"data\": [0.009412177252398818, 0.0028205673873622633, 0.0023548082894660075, 0.0020927225828140674, 0.001951467912971003, 0.001856833619676504, 0.001801730920480138, 0.0017666527130576102, 0.0017534987598337712, 0.0017213698390631102, 0.0017066267992216439, 0.0016921023311523241, 0.0016826823243082558, 0.0016763593153126556], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 243, \"name\": 243, \"data\": [0.036070196192646516, 0.013589940221081629, 0.009855361572686315, 0.008398983294956896, 0.007538261155945128, 0.0070111494409336155, 0.006693617257086533, 0.006455652161340872, 0.006267651193288185, 0.0061199270668830215, 0.006001806446464187, 0.005894357154613004, 0.005820903617664545, 0.005736296113113699, 0.005676328144017153, 0.0056332422162998445], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 244, \"name\": 244, \"data\": [0.15704277486964693, 0.13684736390451985, 0.13711383419831355, 0.13707510860795485, 0.1370965227382934, 0.13690417391559234, 0.13693301990422785, 0.1370740042009139, 0.13705214863367585, 0.13699305845755522], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 246, \"name\": 246, \"data\": [0.04238327472753654, 0.03827641104849859, 0.03885501994533585, 0.03830598078657759, 0.03115695176485316, 0.03460670118189197, 0.0334310039924666, 0.03815569551047796, 0.03472713611185843, 0.03500235611755347, 0.03785451260848648, 0.031980751492994286, 0.03126530391740106], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 247, \"name\": 247, \"data\": [0.24915712526519732, 0.22365088396756144, 0.22290511482202266, 0.22303025475171706, 0.22313645376278451, 0.22369983451644132, 0.2239302892791265, 0.22346332216921092, 0.22329735850440527, 0.2241730224821038, 0.22356612056852418], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 248, \"name\": 248, \"data\": [0.7287195867436764, 0.11130871374020698, 0.055575736455670155, 0.04178788811185277, 0.04093154265789742, 0.0409352009295485, 0.0409390222339969, 0.04092869047662538, 0.040910633645115636, 0.04093515917820008, 0.04092281487567541, 0.040926616453743, 0.04093633289729119], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 249, \"name\": 249, \"data\": [0.2332970074166865, 0.20747543270793073, 0.2074535869931208, 0.20714983077161858, 0.20764100506422925, 0.2072643892792553, 0.20783337423044057, 0.20713352164446805, 0.20751669698487024], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 250, \"name\": 250, \"data\": [0.005919881662794128, 0.0033401737212656753, 0.0029073004344313956, 0.0027023796601201282, 0.002563861435038637, 0.0024250581795669064, 0.0022868868829499698, 0.002196210146416079, 0.0021365323245410074, 0.0021035334003170567, 0.0020664543961953063, 0.002027175687787725, 0.002000516856106105, 0.0019749819651645353, 0.00197490128838747, 0.0019390360941696275, 0.0019368769044895268], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 252, \"name\": 252, \"data\": [1.2387372197533106, 0.15390008389435633, 0.0630389536875755, 0.06024574424419934, 0.0601092512325876, 0.060078401626094756, 0.06008265880959826, 0.06007290244494529, 0.06005999425766358, 0.0600642386320171, 0.06006122265514794, 0.06007003000784157, 0.06004263697312982, 0.06006969228910685], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 251, \"name\": 251, \"data\": [0.16019125281164293, 0.13805291034295014, 0.13820302306371715, 0.13787591107959962, 0.13799997824754848, 0.13797042696373882, 0.13787058093596874, 0.13805526118050437], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 253, \"name\": 253, \"data\": [0.0229727879142921, 0.019993173358008657, 0.020122901562406592, 0.019503762662827735, 0.01982692551716031, 0.020098957596883182, 0.01978150962361378, 0.01942344971573519, 0.01908731324179196, 0.019604723820069427, 0.019731797961748027], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 255, \"name\": 255, \"data\": [0.18045934123631108, 0.15395442489626623, 0.15310583010525194, 0.1525994362285173, 0.15338068608399247, 0.1541151344817117, 0.15580291527304102, 0.15909737486296732, 0.15348157141863145, 0.15540814602809244, 0.1560214188358412, 0.15380539489997774, 0.158463741218269, 0.15435382601941483], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 254, \"name\": 254, \"data\": [0.032031852103192766, 0.011548952995029213, 0.008119048569926686, 0.00700564665790193, 0.0065260343728854656, 0.006293650952984162, 0.006171905701905348, 0.0060923087940395765, 0.006035084435649466, 0.005988490675248762, 0.005968641139373121, 0.005938671035729299, 0.005924064277258321, 0.00590688106778936], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 256, \"name\": 256, \"data\": [0.03793121346039424, 0.02945203227175304, 0.03207568448468562, 0.02606581450259649, 0.030420439771243875, 0.030609001436490847, 0.0269014006218783], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 257, \"name\": 257, \"data\": [0.018945117635926607, 0.008665303925472243, 0.007614083896310647, 0.007364650237230035, 0.007173810734711071, 0.00707739235390928, 0.006970077590892638, 0.0068667258081689594], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 258, \"name\": 258, \"data\": [2.425586427101938, 0.46831820216699543, 0.19744302772056258, 0.094498318494695, 0.07425159493391725, 0.07296431537287623, 0.07291159750510878, 0.07291565661060458, 0.07291330422795335, 0.07290915647267752, 0.07289563456145325, 0.07291414087941406, 0.07289781130869197, 0.0728891564968433, 0.07290347556962697, 0.07290820635899682, 0.0729077371426743, 0.07289385785800394, 0.0729022051475465, 0.07289025603561261, 0.07289423405583907, 0.0728973508720279], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 260, \"name\": 260, \"data\": [2.6311206662016358, 0.4926761467505667, 0.17752202749885976, 0.08447401869239934, 0.07333984303627272, 0.07295219205765681, 0.07291810623675778, 0.07292024326175457, 0.07290155808153516, 0.07290448541756395], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 261, \"name\": 261, \"data\": [0.008011006870121648, 0.0039881165664388376, 0.003356043919963099, 0.0029849641687556675, 0.002737628612003526, 0.0025278293456262027, 0.0023703661820083433, 0.0022624856800122915, 0.002196938029512705, 0.002139366368253201, 0.0021129132804603625, 0.0020782725091867756, 0.002061146813611897, 0.002046420499148176], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 262, \"name\": 262, \"data\": [0.017685570357660423, 0.004396271897071141, 0.0036698793885174864, 0.0032773081985116425, 0.0030439719714250397, 0.0028824047055596358, 0.0027624312244838314, 0.0026693733508746428, 0.0026039762022006504, 0.002528941192450039, 0.0024899979075026463, 0.002434682412784021, 0.002374540923377381, 0.002352878767667056, 0.00231904270796542, 0.002281907849854128, 0.002250113369960615, 0.002227748699094418, 0.0022062142056388677, 0.002180554028306293], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 263, \"name\": 263, \"data\": [0.025098339918488657, 0.021090856670122327, 0.020300190813708544, 0.021450358023444974, 0.020591090141793694, 0.02071350708662744], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 264, \"name\": 264, \"data\": [0.023828579660554062, 0.009057877310735476, 0.00876939329339791, 0.009068546888930853, 0.00917263933218327, 0.0084890643001954], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 265, \"name\": 265, \"data\": [0.03138268619835222, 0.021153265982143243, 0.02151428686207048, 0.020202280656515536, 0.02085478753895885, 0.021799836655709542, 0.020320922711931196, 0.019861082617512404, 0.022349592771783635, 0.019916105253514685, 0.02051303087765219, 0.020098080256612798], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 266, \"name\": 266, \"data\": [0.007426175539270734, 0.0030397396778006165, 0.0027356942738051867, 0.0025963733442118065, 0.0025011093199557564, 0.0024346020186716316, 0.002382009980466208, 0.0023325138885740945, 0.002300938773882295, 0.0022659140996312, 0.002239508395450194], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 267, \"name\": 267, \"data\": [1.1398656442343849, 0.1224271518345758, 0.05357043904439246, 0.052579986717052316, 0.052379413972103654, 0.052283243412577514, 0.05228553808377144, 0.05230827650072526, 0.05227814064452074, 0.05229432922441404, 0.05226935327254411], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 268, \"name\": 268, \"data\": [0.03418137146176476, 0.02417152702305462, 0.022107311122799088, 0.021188652959441556, 0.022825513484632503, 0.019564722760417593, 0.022028308903048504, 0.021547214167965033], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 269, \"name\": 269, \"data\": [0.05547983646885666, 0.014783069447542527, 0.010875226244343048, 0.00955895672490162, 0.00890463830435211, 0.00849726041687314, 0.008198809836025086, 0.007979955018961364, 0.007794091698009658, 0.007635943067828257, 0.007499181886439728, 0.0073796466597301135, 0.007277596946132856, 0.007182205987706256, 0.0071095802129477635, 0.007035143665719269, 0.006970962078754708, 0.00692000095472687, 0.006876411607508912, 0.006823150035684588, 0.006787296563328832, 0.00675742905486149], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 270, \"name\": 270, \"data\": [1.5793358787774558, 0.20426328509110242, 0.08786196049177525, 0.07315841772417182, 0.07296024332911327, 0.07294241951630598, 0.07294937049836658, 0.07293478528486587, 0.07293919090428491, 0.07294392000998548], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 271, \"name\": 271, \"data\": [0.21354660593905475, 0.07396954914042028, 0.07393220636738744, 0.07398544967806266, 0.07396903435653758, 0.07397293058055167, 0.07394672882460394, 0.07393449936557342], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 272, \"name\": 272, \"data\": [0.010357721005332597, 0.004436890786798271, 0.004101884075644125, 0.003910505889683429, 0.003732648235944746, 0.003609699094184388, 0.003516041953644702, 0.0033667750701328733, 0.0033211121712859097, 0.0032505475159541352, 0.0032055828076214083, 0.0031676689684656647, 0.0031081568350526845, 0.003066837573609198, 0.0030276341990683234, 0.0030049446105427703, 0.002969803452883555, 0.002937702510601443, 0.0029143732646014163, 0.0028766736636908533, 0.002848629848339106, 0.0028231302748295555, 0.0027769354966041353, 0.0027599758611681013, 0.002739928016029604, 0.0027218782290465717, 0.002690642910974007, 0.002672749953536081, 0.0026656688865666464, 0.0026374642097573083, 0.0026278059262120116, 0.0026234087309748924, 0.002617913985574785, 0.0026124050897200443, 0.0025879161516872934], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 273, \"name\": 273, \"data\": [0.2832950151271698, 0.05741047608260406, 0.057096813793384, 0.05707793071605134, 0.05697366342912485, 0.05702995354164904, 0.057043499568733884, 0.05708841689873924, 0.05699949224119017, 0.05700856547663658, 0.05700316981032084, 0.05705132827155621, 0.0570685188888289, 0.05702075135486704], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 274, \"name\": 274, \"data\": [0.02738471371535837, 0.007535772615478104, 0.006126971589146451, 0.005727577233652974, 0.005388377633865243, 0.005223346158500218, 0.005163043230579741, 0.005068080803721647], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 275, \"name\": 275, \"data\": [1.2254835157661004, 0.1488017866178682, 0.08853677647372682, 0.06018410517525025, 0.04977800185846979, 0.04784902909660946, 0.04779376665293746, 0.04775256509327261, 0.04777790707971275, 0.04777071140007595, 0.0477652771390793, 0.04778127372843299], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 276, \"name\": 276, \"data\": [0.16517784031369084, 0.1552805133612929, 0.15507043927996272, 0.15535262080613427, 0.15734210858603717, 0.15537735536079433, 0.155439512470601, 0.1561059944139949, 0.1537841444126284, 0.15607497779322288, 0.15565462523291987], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 277, \"name\": 277, \"data\": [0.007108118257479104, 0.003893931056052472, 0.0033432290650516946, 0.0030530075338970992, 0.0028100123851360617, 0.002669039000072154, 0.002555068387485862, 0.002465363345265586, 0.002369433373736219, 0.0023331205560935225, 0.002249200822167293, 0.0021995395250409178, 0.0021524659022451867], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 279, \"name\": 279, \"data\": [0.2704072027649798, 0.05932955408899605, 0.057741001616243626, 0.05697643906609627, 0.05706995204184945, 0.05707271043310759, 0.05698164590905625], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 278, \"name\": 278, \"data\": [0.025153323491404787, 0.007565843416229405, 0.006759398367885359, 0.006291712753831753, 0.005998201871110502, 0.005921952821427306, 0.005781792241300425, 0.0058695959321701015, 0.006214260131175872, 0.005861715896669888, 0.005746508484502595], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 280, \"name\": 280, \"data\": [0.013319633308566694, 0.005614176723064541, 0.005300643979054314, 0.005161535424748739, 0.005126372584647075, 0.005078261071596628, 0.005021401609799641, 0.005041387547799215, 0.004982223017996099, 0.004909328060830866, 0.004947532333507555, 0.004892560509625754, 0.0048099975103849954, 0.004801353759657052, 0.00480513494385128, 0.004801258542034694, 0.00478997517025787, 0.0047462862197187], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 281, \"name\": 281, \"data\": [0.020704500697929142, 0.011214455600778927, 0.009675028983158928, 0.008737907529195399, 0.008364505018393798, 0.00818290321732007, 0.007959500151443949, 0.007818019215771934, 0.007703197967736679, 0.0075843676037167344, 0.007496689600781239, 0.007434519684170761, 0.007358147531260967, 0.007308720899792016, 0.0072978517691351125, 0.007200915501721112, 0.007182071810973463, 0.007114016935161014, 0.007093827877975382, 0.007106928036768927, 0.007018023500239474, 0.007026711950917741, 0.006940626986453329, 0.006945350904363945, 0.006912715901721712, 0.006899246868812205, 0.0068981959530511295, 0.006852470664638871, 0.006856319137782835, 0.006850723495937085], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 283, \"name\": 283, \"data\": [0.012721996038744429, 0.007196200851264295, 0.006734098305668184, 0.006603134761320292, 0.0065713526486768335, 0.006565558369953958, 0.006578177524656397, 0.006557526420700669, 0.00654392974989792, 0.006526976231114212], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 282, \"name\": 282, \"data\": [0.06256214755263931, 0.025169106058164803, 0.015842426996787725, 0.012013311724887236, 0.010090677092039608, 0.009013310910080272, 0.008371156154587748, 0.007945080547077528, 0.0076340033707673095, 0.007403053096294151, 0.007237343163078752, 0.00710075218852141, 0.006986802344013108, 0.006906664574912515, 0.006829684435539429, 0.006759296436450671, 0.006713266822809374, 0.0066703217397343344, 0.006635813851753226, 0.006597832219956728, 0.006577357591678008, 0.006546033919921669, 0.006530139863073901, 0.006502064114950873, 0.006492557409110034, 0.006480884113471071, 0.006467342423133101, 0.006445002486669034, 0.006443420299346265, 0.006434028691470452, 0.00642170911903012, 0.006423072056553365, 0.006420132120427403, 0.0063991007285838985], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 284, \"name\": 284, \"data\": [0.022109114092393335, 0.010060514858060777, 0.009075366497845627, 0.008456552746295634, 0.008001737010615913, 0.007814519670111663, 0.007682118173029392, 0.007591692609633675, 0.0075162298148941575, 0.007414511087643413, 0.007387999787390307, 0.007323474016869117, 0.0073314564204795, 0.007274360736318137, 0.007265252055227693], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 285, \"name\": 285, \"data\": [0.008196100606274431, 0.004148631117175318, 0.0036672307366394773, 0.0033480542428728477, 0.0031287296279750567, 0.0030076287769312112, 0.0029441986666954465, 0.002880029368200428, 0.002851199963570336, 0.0028181161064142384, 0.002798753366929824, 0.0027660241490123955, 0.00275379910618342], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 286, \"name\": 286, \"data\": [0.02763807172376782, 0.02104050211317504, 0.020451550295578134, 0.02040040419908993, 0.02080962007789223, 0.02035658329112091, 0.019777549669669145, 0.0197297116244854, 0.019986612168388533, 0.01935265478466629], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 287, \"name\": 287, \"data\": [2.811895266366647, 0.5406597730609684, 0.23315469249599727, 0.11376180535059226, 0.07698274443686037, 0.07302603090945134, 0.07291973777085582, 0.07290219157706329, 0.07288926014155284, 0.0728959767693947, 0.07288674496206742, 0.07290401372326173, 0.0728957809633938, 0.07288514858532656, 0.07289094572300292, 0.07288856287717072, 0.07288819500935415, 0.0728880942586612], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 288, \"name\": 288, \"data\": [0.0070161395576389465, 0.0037603820192392418, 0.0030516995898961495, 0.002626691961125526, 0.0023385649810038814, 0.0021576210517811447, 0.002062087849964317, 0.00196367798966402, 0.001918782775967362, 0.001861092580737639, 0.001840742093651317, 0.0017951964924364226, 0.0017783374022738636, 0.0017358512102427627, 0.001723873240134908, 0.0016981665663126312, 0.0016870571097181513], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 289, \"name\": 289, \"data\": [0.012353787302731997, 0.00375403349776027, 0.003251819860410835, 0.002965340246107963, 0.0027902105614282303, 0.0026736032763902203, 0.0025704801950832577, 0.0025028450770789397, 0.0024414779708714714, 0.0023956327622772305, 0.0023595744726908553, 0.0023158123074847165, 0.0022766976401005254], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 290, \"name\": 290, \"data\": [0.00623782293711411, 0.003883350813550695, 0.0035315369923039934, 0.003302984869340105, 0.0031442906475544685, 0.003021264982794289, 0.0029165615173202576], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 291, \"name\": 291, \"data\": [0.021964105111663426, 0.006063082539788282, 0.005770119381466062, 0.005735631729669171, 0.005651022976654072, 0.0055756957055706005, 0.005792525474395812, 0.005542210760827546, 0.005577754296633144, 0.0056469906586133585, 0.0055655492498870975, 0.005538097966151466], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 293, \"name\": 293, \"data\": [0.011417685744183143, 0.004651101827104756, 0.0038087694522236336, 0.003336569891659326, 0.003043792238438355, 0.0028596728716527766, 0.0027059264441495453, 0.0025901939998519098, 0.002496490862813947, 0.0024251430127058987, 0.0023739302862109775, 0.002316361035916642, 0.0022755350916657223, 0.0022325293483328634, 0.0022035938648252654, 0.002162472230385946, 0.0021407791318603904, 0.0021049269341891245, 0.0020873991793431282, 0.002065487451381478, 0.0020469576065651218, 0.0020230349952655015, 0.0020065071944516933, 0.001982201870616462], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 294, \"name\": 294, \"data\": [0.007773508677074498, 0.0034063854284117423, 0.0029864752517049548, 0.0027948050108763858, 0.002667132901554538, 0.0025809895228385755, 0.0025117065765787603, 0.0024446435138611427, 0.0024022342538534, 0.002352556704413687, 0.00232374474552352, 0.0022806170185525826, 0.0022539497922057818, 0.0022333486751446208, 0.002211332643894926], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 295, \"name\": 295, \"data\": [1.69340702391429, 0.442316541812316, 0.26255374496360273, 0.1492225176893039, 0.08784287871574659, 0.061790609883680465, 0.05400241404239644, 0.05258524837391142, 0.05236157008535406, 0.05221496167247843, 0.052140938911729266, 0.052073216940641986, 0.052035745883285, 0.05202202387107166, 0.05203579657552436, 0.052027059259071085, 0.052013222390187214, 0.05203223290879948, 0.05203160202505276, 0.05203633639318453, 0.05200431593273591, 0.0520223539924533], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 296, \"name\": 296, \"data\": [0.02474887297692206, 0.004450062515243409, 0.0037166674110049483, 0.0033721706796423843, 0.0031680669886692122, 0.00302775649646896, 0.002923339473104503, 0.0028310854096589852, 0.0027615238801938324, 0.0027025305276990887, 0.002651665080127828, 0.0026075606234120603, 0.0025671272084088013, 0.0025296210466506687, 0.0024986793341427232, 0.0024655417595416187, 0.0024390723643949383, 0.0024096098366051476, 0.002384342796144919, 0.002363328793246186, 0.0023422302568515334, 0.002325018333821062, 0.002304919786778987, 0.002286948519683563, 0.0022688688441298796, 0.002254046270177323, 0.0022422249111641542, 0.0022258417354083238, 0.0022107075963909255, 0.00219822028162372, 0.0021864764621390516, 0.002180696369643073, 0.0021677020420829475, 0.0021533513570173046, 0.0021477550877439344, 0.002139593494531022, 0.002131714282460159], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 297, \"name\": 297, \"data\": [1.2690271207072596, 0.11608106276586391, 0.06140998610557861, 0.060373497701954784, 0.06013051780382184], \"mode\": \"lines\", \"stepped\": false}], \"validationLoss\": [{\"run_id\": 23, \"name\": 23, \"data\": [0.005383272065470616], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 21, \"name\": 21, \"data\": [0.15015862319204543], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 24, \"name\": 24, \"data\": [0.05040198800464471], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 22, \"name\": 22, \"data\": [0.002224967547226697], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 25, \"name\": 25, \"data\": [0.012224890353778998], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 27, \"name\": 27, \"data\": [0.003004929055330447], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 28, \"name\": 28, \"data\": [0.0051556491913894815], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 26, \"name\": 26, \"data\": [0.0017640709954624375], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 30, \"name\": 30, \"data\": [0.01452232330209679], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 31, \"name\": 31, \"data\": [0.005902380439996098], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 33, \"name\": 33, \"data\": [0.002915892643957502], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 34, \"name\": 34, \"data\": [0.07171800889902645], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 32, \"name\": 32, \"data\": [0.005822353737635745], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 35, \"name\": 35, \"data\": [0.052793802238172954], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 37, \"name\": 37, \"data\": [0.005043502009680702], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 36, \"name\": 36, \"data\": [0.01557492743142777], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 38, \"name\": 38, \"data\": [0.002160197576934782], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 39, \"name\": 39, \"data\": [0.0045023836027313436], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 40, \"name\": 40, \"data\": [0.17498758435249331], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 41, \"name\": 41, \"data\": [0.002225795991317783], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 42, \"name\": 42, \"data\": [0.16006517824199465], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 43, \"name\": 43, \"data\": [0.05136508113808102], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 45, \"name\": 45, \"data\": [0.12438862977756394], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 46, \"name\": 46, \"data\": [0.0013328425812586728], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 48, \"name\": 48, \"data\": [0.004241224828486641], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 49, \"name\": 49, \"data\": [0.04663222299681769], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 50, \"name\": 50, \"data\": [0.25621750156084694], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 51, \"name\": 51, \"data\": [0.011843787775271468], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 52, \"name\": 52, \"data\": [0.014181127275029818], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 53, \"name\": 53, \"data\": [0.05043937903311518], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 54, \"name\": 54, \"data\": [0.036506751428047816], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 55, \"name\": 55, \"data\": [0.049980949693255956], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 56, \"name\": 56, \"data\": [0.014623008854687213], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 57, \"name\": 57, \"data\": [0.09112190537982517], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 58, \"name\": 58, \"data\": [0.006154054444697168], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 59, \"name\": 59, \"data\": [0.05151197305983967], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 61, \"name\": 61, \"data\": [0.0013532351560166312], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 62, \"name\": 62, \"data\": [0.005183384347603553], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 65, \"name\": 65, \"data\": [0.0027405533409263524], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 64, \"name\": 64, \"data\": [0.011068285173839993], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 66, \"name\": 66, \"data\": [0.003503460552181221], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 68, \"name\": 68, \"data\": [0.08859932704104317], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 69, \"name\": 69, \"data\": [0.23419155710273318], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 70, \"name\": 70, \"data\": [0.1076953089899487], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 72, \"name\": 72, \"data\": [0.014384724127335682], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 74, \"name\": 74, \"data\": [0.06280205117331611], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 121, \"name\": 121, \"data\": [0.006100420741778281], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 124, \"name\": 124, \"data\": [0.006240233540948895], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 123, \"name\": 123, \"data\": [0.06394368588096566], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 125, \"name\": 125, \"data\": [0.06334953854481379], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 126, \"name\": 126, \"data\": [0.06563003472983837], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 129, \"name\": 129, \"data\": [0.003977724899434381], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 128, \"name\": 128, \"data\": [0.0476335294958618], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 133, \"name\": 133, \"data\": [0.013254636608892017], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 75, \"name\": 75, \"data\": [0.05154798308180438], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 76, \"name\": 76, \"data\": [0.0033166599304725727], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 77, \"name\": 77, \"data\": [0.0020419891128161303], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 78, \"name\": 78, \"data\": [0.0013762809329717937], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 79, \"name\": 79, \"data\": [0.015582497604191304], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 80, \"name\": 80, \"data\": [0.0026884200297192565], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 81, \"name\": 81, \"data\": [0.043841489983929526], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 83, \"name\": 83, \"data\": [0.006690734966347615], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 84, \"name\": 84, \"data\": [0.16816513496968483], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 85, \"name\": 85, \"data\": [0.0011435424714970092], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 86, \"name\": 86, \"data\": [0.007402486845643984], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 87, \"name\": 87, \"data\": [0.1466815974149439], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 89, \"name\": 89, \"data\": [0.004572066989365137], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 91, \"name\": 91, \"data\": [0.007162717253797584], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 90, \"name\": 90, \"data\": [0.0016600816963343984], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 92, \"name\": 92, \"data\": [0.0074968662216431566], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 93, \"name\": 93, \"data\": [0.011995987697607942], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 96, \"name\": 96, \"data\": [0.0014064463502210048], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 95, \"name\": 95, \"data\": [0.0021387382488076887], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 97, \"name\": 97, \"data\": [0.002348950886193456], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 98, \"name\": 98, \"data\": [0.015238590331541168], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 99, \"name\": 99, \"data\": [0.0012551041940848033], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 101, \"name\": 101, \"data\": [0.014773913597067197], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 100, \"name\": 100, \"data\": [0.11979398859871758], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 102, \"name\": 102, \"data\": [0.0018989403253524668], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 103, \"name\": 103, \"data\": [0.005185915646143258], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 105, \"name\": 105, \"data\": [0.006504128754345907], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 106, \"name\": 106, \"data\": [0.04130418797334035], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 107, \"name\": 107, \"data\": [0.03568603578541014], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 108, \"name\": 108, \"data\": [0.005389063505248891], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 109, \"name\": 109, \"data\": [0.053605104155010644], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 110, \"name\": 110, \"data\": [0.05703628808259964], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 111, \"name\": 111, \"data\": [0.004905416681948636], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 113, \"name\": 113, \"data\": [0.0022447216208092867], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 112, \"name\": 112, \"data\": [0.0020134543236862454], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 114, \"name\": 114, \"data\": [0.004888344462960959], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 115, \"name\": 115, \"data\": [0.013006622230427133], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 116, \"name\": 116, \"data\": [0.017801857967343595], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 117, \"name\": 117, \"data\": [0.04986203155583806], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 119, \"name\": 119, \"data\": [0.002441030806706597], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 118, \"name\": 118, \"data\": [0.0450914413564735], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 135, \"name\": 135, \"data\": [0.003024087423303475], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 137, \"name\": 137, \"data\": [0.015006950507975287], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 139, \"name\": 139, \"data\": [0.011741250712010596], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 138, \"name\": 138, \"data\": [0.0021443128391789895], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 140, \"name\": 140, \"data\": [0.0033215252009944785], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 141, \"name\": 141, \"data\": [0.04440190899703238], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 143, \"name\": 143, \"data\": [0.047321509445707004], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 144, \"name\": 144, \"data\": [0.035935639300280146], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 146, \"name\": 146, \"data\": [0.06377940707736546], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 148, \"name\": 148, \"data\": [0.002846034270219712], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 147, \"name\": 147, \"data\": [0.016977005327741306], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 149, \"name\": 149, \"data\": [0.05146467172437244], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 151, \"name\": 151, \"data\": [0.0030592600543362398], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 152, \"name\": 152, \"data\": [0.0363796507111854], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 154, \"name\": 154, \"data\": [0.04085927332441012], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 155, \"name\": 155, \"data\": [0.1735653539498647], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 156, \"name\": 156, \"data\": [0.0013313389787476303], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 159, \"name\": 159, \"data\": [0.001629698451466134], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 158, \"name\": 158, \"data\": [0.06324247146646182], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 160, \"name\": 160, \"data\": [0.0062398420491566265], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 162, \"name\": 162, \"data\": [0.003199217168407308], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 163, \"name\": 163, \"data\": [0.05174489956763056], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 164, \"name\": 164, \"data\": [0.18596254222922856], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 166, \"name\": 166, \"data\": [0.003568064700812101], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 165, \"name\": 165, \"data\": [0.005742028902750463], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 167, \"name\": 167, \"data\": [0.0012829577252786192], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 168, \"name\": 168, \"data\": [0.005130698552562131], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 171, \"name\": 171, \"data\": [0.016040522025691138], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 172, \"name\": 172, \"data\": [0.04623739992578824], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 174, \"name\": 174, \"data\": [0.06278936349683338], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 175, \"name\": 175, \"data\": [0.06318449539442857], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 176, \"name\": 176, \"data\": [0.002318606056117763], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 177, \"name\": 177, \"data\": [0.006192696715394656], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 178, \"name\": 178, \"data\": [0.0889696502023273], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 180, \"name\": 180, \"data\": [0.0014630956730494896], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 181, \"name\": 181, \"data\": [0.006285720297859775], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 179, \"name\": 179, \"data\": [0.011431117624872261], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 182, \"name\": 182, \"data\": [0.03933720994326803], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 183, \"name\": 183, \"data\": [0.0026546280779358414], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 184, \"name\": 184, \"data\": [0.05264983259969287], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 186, \"name\": 186, \"data\": [0.04498429223895073], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 188, \"name\": 188, \"data\": [0.0024702082559492234], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 190, \"name\": 190, \"data\": [0.050133142951462005], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 191, \"name\": 191, \"data\": [0.1557926532295015], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 193, \"name\": 193, \"data\": [0.004173968916034533], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 192, \"name\": 192, \"data\": [0.051992043604453406], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 194, \"name\": 194, \"data\": [0.0065857382956892255], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 195, \"name\": 195, \"data\": [0.0023256871487117477], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 196, \"name\": 196, \"data\": [0.06328127367628945], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 197, \"name\": 197, \"data\": [0.0013355807858816762], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 198, \"name\": 198, \"data\": [0.005165446389259564], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 200, \"name\": 200, \"data\": [0.0022414634398753856], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 199, \"name\": 199, \"data\": [0.15565382573339676], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 201, \"name\": 201, \"data\": [0.05069197565317154], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 202, \"name\": 202, \"data\": [0.20458938048945532], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 205, \"name\": 205, \"data\": [0.0023501593477299643], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 204, \"name\": 204, \"data\": [0.005834254208538267], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 206, \"name\": 206, \"data\": [0.0017049770905739731], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 207, \"name\": 207, \"data\": [0.12144456836912366], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 208, \"name\": 208, \"data\": [0.00643116044294503], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 210, \"name\": 210, \"data\": [0.0013957365626184684], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 211, \"name\": 211, \"data\": [0.002499009421767874], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 212, \"name\": 212, \"data\": [0.0019814027816108947], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 213, \"name\": 213, \"data\": [0.0018176650324474193], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 215, \"name\": 215, \"data\": [0.0034305506760978865], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 214, \"name\": 214, \"data\": [0.015841166054209075], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 216, \"name\": 216, \"data\": [0.004459024611343112], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 217, \"name\": 217, \"data\": [0.016155704462693796], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 218, \"name\": 218, \"data\": [0.004222676067406105], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 220, \"name\": 220, \"data\": [0.0019529759938854518], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 221, \"name\": 221, \"data\": [0.004569885533096062], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 222, \"name\": 222, \"data\": [0.050848245951864456], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 224, \"name\": 224, \"data\": [0.0016635032936594346], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 225, \"name\": 225, \"data\": [0.006955254052041305], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 228, \"name\": 228, \"data\": [0.013231184550871451], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 227, \"name\": 227, \"data\": [0.005643249621304373], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 229, \"name\": 229, \"data\": [0.0052181129136847125], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 230, \"name\": 230, \"data\": [0.006358126302560171], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 232, \"name\": 232, \"data\": [0.002912286698119715], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 233, \"name\": 233, \"data\": [0.049375854515367086], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 234, \"name\": 234, \"data\": [0.25771764583057827], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 235, \"name\": 235, \"data\": [0.2514227484663328], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 237, \"name\": 237, \"data\": [0.06341432912482156], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 236, \"name\": 236, \"data\": [0.015052126699851618], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 238, \"name\": 238, \"data\": [0.00481937907429205], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 239, \"name\": 239, \"data\": [0.0013107602245226088], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 241, \"name\": 241, \"data\": [0.0013852325814595033], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 242, \"name\": 242, \"data\": [0.0012231362837256813], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 244, \"name\": 244, \"data\": [0.1000188011262152], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 246, \"name\": 246, \"data\": [0.017523828076405658], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 247, \"name\": 247, \"data\": [0.17299199683798683], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 248, \"name\": 248, \"data\": [0.0359193198589815], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 249, \"name\": 249, \"data\": [0.19658257530795203], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 250, \"name\": 250, \"data\": [0.0014066553335093583], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 251, \"name\": 251, \"data\": [0.11823105497492684], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 253, \"name\": 253, \"data\": [0.01317054234031174], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 255, \"name\": 255, \"data\": [0.11925673070881102], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 254, \"name\": 254, \"data\": [0.0050444018302692305], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 256, \"name\": 256, \"data\": [0.014719901916881403], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 257, \"name\": 257, \"data\": [0.005982791125360463], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 258, \"name\": 258, \"data\": [0.06323361562358008], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 260, \"name\": 260, \"data\": [0.06275395063890352], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 261, \"name\": 261, \"data\": [0.0013646896469355044], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 262, \"name\": 262, \"data\": [0.00243828783924174], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 263, \"name\": 263, \"data\": [0.014445386651075549], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 264, \"name\": 264, \"data\": [0.007363354453506569], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 265, \"name\": 265, \"data\": [0.011373272186352147], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 266, \"name\": 266, \"data\": [0.002306173148746085], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 267, \"name\": 267, \"data\": [0.04373818652497397], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 268, \"name\": 268, \"data\": [0.01296727837373813], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 270, \"name\": 270, \"data\": [0.06334071316652828], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 271, \"name\": 271, \"data\": [0.0639162752777338], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 272, \"name\": 272, \"data\": [0.0017629054788913991], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 273, \"name\": 273, \"data\": [0.04851333606574271], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 274, \"name\": 274, \"data\": [0.00277469827948759], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 275, \"name\": 275, \"data\": [0.041013718396425244], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 276, \"name\": 276, \"data\": [0.13751542816559473], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 277, \"name\": 277, \"data\": [0.0013897957183265437], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 279, \"name\": 279, \"data\": [0.05280000931686825], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 278, \"name\": 278, \"data\": [0.002923253195412043], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 280, \"name\": 280, \"data\": [0.0024809239652111297], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 281, \"name\": 281, \"data\": [0.005290535459708836], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 283, \"name\": 283, \"data\": [0.00598454774202158], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 284, \"name\": 284, \"data\": [0.0058544770400557255], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 285, \"name\": 285, \"data\": [0.002076180939911865], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 286, \"name\": 286, \"data\": [0.01199281828271018], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 287, \"name\": 287, \"data\": [0.06339974883529875], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 288, \"name\": 288, \"data\": [0.0012444217721672935], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 289, \"name\": 289, \"data\": [0.0024333428979540863], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 290, \"name\": 290, \"data\": [0.0030126827710773795], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 291, \"name\": 291, \"data\": [0.002212893292825255], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 293, \"name\": 293, \"data\": [0.002089324466133904], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 294, \"name\": 294, \"data\": [0.0022788069776854374], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 295, \"name\": 295, \"data\": [0.044018238037824634], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 296, \"name\": 296, \"data\": [0.0018697456437318275], \"mode\": \"lines\", \"stepped\": false}], \"testMAPE\": [{\"run_id\": 23, \"name\": 23, \"data\": [0.04966857173157935], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 21, \"name\": 21, \"data\": [0.18463939276051536], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 24, \"name\": 24, \"data\": [0.13883501341431012], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 22, \"name\": 22, \"data\": [0.04153650575732333], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 25, \"name\": 25, \"data\": [0.06318916829994062], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 27, \"name\": 27, \"data\": [0.06307945084771363], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 28, \"name\": 28, \"data\": [0.047958565452591], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 26, \"name\": 26, \"data\": [0.03831787573866784], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 30, \"name\": 30, \"data\": [0.08809865671050195], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 31, \"name\": 31, \"data\": [0.0822462594876673], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 33, \"name\": 33, \"data\": [0.054735295802439285], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 34, \"name\": 34, \"data\": [0.17567993453378575], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 32, \"name\": 32, \"data\": [0.05019566502470353], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 35, \"name\": 35, \"data\": [0.15024412550954516], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 37, \"name\": 37, \"data\": [0.04677356689738681], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 36, \"name\": 36, \"data\": [0.08265091075959172], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 38, \"name\": 38, \"data\": [0.042385657497856166], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 39, \"name\": 39, \"data\": [0.06345127214009862], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 40, \"name\": 40, \"data\": [0.22340367570433226], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 41, \"name\": 41, \"data\": [0.04330773266623324], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 42, \"name\": 42, \"data\": [0.22054727682581265], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 43, \"name\": 43, \"data\": [0.1482593540916885], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 45, \"name\": 45, \"data\": [0.213844115832251], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 46, \"name\": 46, \"data\": [0.037255971086882785], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 48, \"name\": 48, \"data\": [0.047131914697121684], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 49, \"name\": 49, \"data\": [0.15793442157222148], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 50, \"name\": 50, \"data\": [0.16365195743283292], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 51, \"name\": 51, \"data\": [0.07939029692876891], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 52, \"name\": 52, \"data\": [0.08514741609691502], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 53, \"name\": 53, \"data\": [0.1383683800932794], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 54, \"name\": 54, \"data\": [0.12368544175551682], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 55, \"name\": 55, \"data\": [0.1402047998066834], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 56, \"name\": 56, \"data\": [0.07544295773367735], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 57, \"name\": 57, \"data\": [0.15975149783063844], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 58, \"name\": 58, \"data\": [0.04907566374708898], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 59, \"name\": 59, \"data\": [0.14229894948382987], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 61, \"name\": 61, \"data\": [0.038724219450986946], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 62, \"name\": 62, \"data\": [0.051087650505081454], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 65, \"name\": 65, \"data\": [0.043842024294438876], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 64, \"name\": 64, \"data\": [0.05922352282207179], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 66, \"name\": 66, \"data\": [0.04829593833962914], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 68, \"name\": 68, \"data\": [0.16813764012548557], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 69, \"name\": 69, \"data\": [0.3100007884494279], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 70, \"name\": 70, \"data\": [0.21908653651897164], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 72, \"name\": 72, \"data\": [0.06288451269227871], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 74, \"name\": 74, \"data\": [0.1487799387872113], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 121, \"name\": 121, \"data\": [0.05483027889347073], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 124, \"name\": 124, \"data\": [0.05082046159542608], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 123, \"name\": 123, \"data\": [0.1558824325047265], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 125, \"name\": 125, \"data\": [0.14440764071480344], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 126, \"name\": 126, \"data\": [0.15535608279152727], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 129, \"name\": 129, \"data\": [0.05449506982316187], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 128, \"name\": 128, \"data\": [0.14265324895097878], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 133, \"name\": 133, \"data\": [0.07299211571379735], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 75, \"name\": 75, \"data\": [0.1449275863672414], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 76, \"name\": 76, \"data\": [0.05352430076764425], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 77, \"name\": 77, \"data\": [0.041712950882659756], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 78, \"name\": 78, \"data\": [0.039758302757241584], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 79, \"name\": 79, \"data\": [0.08205004212635779], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 80, \"name\": 80, \"data\": [0.04892620894773224], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 81, \"name\": 81, \"data\": [0.1450676002791612], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 83, \"name\": 83, \"data\": [0.060399341038450405], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 84, \"name\": 84, \"data\": [0.26329024615954516], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 85, \"name\": 85, \"data\": [0.03594280089556881], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 86, \"name\": 86, \"data\": [0.061115333658956515], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 87, \"name\": 87, \"data\": [0.19048433553485042], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 89, \"name\": 89, \"data\": [0.05884114534353974], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 91, \"name\": 91, \"data\": [0.0577986852057244], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 90, \"name\": 90, \"data\": [0.0380286730066267], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 92, \"name\": 92, \"data\": [0.059934244308775496], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 93, \"name\": 93, \"data\": [0.06619560775040176], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 96, \"name\": 96, \"data\": [0.038476563486814615], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 95, \"name\": 95, \"data\": [0.04864549747945383], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 97, \"name\": 97, \"data\": [0.04370262202249621], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 98, \"name\": 98, \"data\": [0.06858435341372084], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 99, \"name\": 99, \"data\": [0.03634302890291729], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 101, \"name\": 101, \"data\": [0.0642274143362609], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 100, \"name\": 100, \"data\": [0.22707112975076224], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 102, \"name\": 102, \"data\": [0.04068671743536188], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 103, \"name\": 103, \"data\": [0.04938637422316314], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 105, \"name\": 105, \"data\": [0.05395989057088094], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 106, \"name\": 106, \"data\": [0.13181300502322363], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 107, \"name\": 107, \"data\": [0.12745895924787434], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 108, \"name\": 108, \"data\": [0.04833137595986334], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 109, \"name\": 109, \"data\": [0.14638753781530744], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 110, \"name\": 110, \"data\": [0.15279775723065286], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 111, \"name\": 111, \"data\": [0.04701138306893921], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 113, \"name\": 113, \"data\": [0.04102394551377072], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 112, \"name\": 112, \"data\": [0.04198607494432523], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 114, \"name\": 114, \"data\": [0.06172309008703132], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 115, \"name\": 115, \"data\": [0.07949929021242451], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 116, \"name\": 116, \"data\": [0.08141475248651853], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 117, \"name\": 117, \"data\": [0.1398662234204932], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 119, \"name\": 119, \"data\": [0.041684258995736745], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 118, \"name\": 118, \"data\": [0.14144803061734962], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 135, \"name\": 135, \"data\": [0.054203611247860724], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 137, \"name\": 137, \"data\": [0.0808684877004121], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 139, \"name\": 139, \"data\": [0.05948251534409437], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 138, \"name\": 138, \"data\": [0.04602544137881075], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 140, \"name\": 140, \"data\": [0.05555219410863862], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 141, \"name\": 141, \"data\": [0.14161585901101312], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 143, \"name\": 143, \"data\": [0.16444233430159777], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 144, \"name\": 144, \"data\": [0.12960594668456688], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 146, \"name\": 146, \"data\": [0.1446692100689738], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 148, \"name\": 148, \"data\": [0.05860010917175666], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 147, \"name\": 147, \"data\": [0.11642468124276187], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 149, \"name\": 149, \"data\": [0.14204963552035382], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 151, \"name\": 151, \"data\": [0.05438798200109524], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 152, \"name\": 152, \"data\": [0.12685294494014573], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 154, \"name\": 154, \"data\": [0.13611154370135894], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 155, \"name\": 155, \"data\": [0.22114169104965534], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 156, \"name\": 156, \"data\": [0.03692763227530203], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 159, \"name\": 159, \"data\": [0.04165883788583175], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 158, \"name\": 158, \"data\": [0.15032283684435457], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 160, \"name\": 160, \"data\": [0.05782823765814346], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 162, \"name\": 162, \"data\": [0.055881153480209105], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 163, \"name\": 163, \"data\": [0.1434577145938619], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 164, \"name\": 164, \"data\": [0.2567086074426661], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 166, \"name\": 166, \"data\": [0.05709553882752781], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 165, \"name\": 165, \"data\": [0.05970788203029131], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 167, \"name\": 167, \"data\": [0.04047128839011791], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 168, \"name\": 168, \"data\": [0.047249171067878515], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 171, \"name\": 171, \"data\": [0.07217500939604105], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 172, \"name\": 172, \"data\": [0.14544444354143454], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 174, \"name\": 174, \"data\": [0.14966440480977103], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 175, \"name\": 175, \"data\": [0.14499294882556413], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 176, \"name\": 176, \"data\": [0.0442768871052367], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 177, \"name\": 177, \"data\": [0.05042751780717045], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 178, \"name\": 178, \"data\": [0.16137549766902595], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 180, \"name\": 180, \"data\": [0.04212750013696957], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 181, \"name\": 181, \"data\": [0.05408721680962582], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 179, \"name\": 179, \"data\": [0.054577198493537704], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 182, \"name\": 182, \"data\": [0.12988047106776598], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 183, \"name\": 183, \"data\": [0.047716864392875175], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 184, \"name\": 184, \"data\": [0.14726555789884016], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 186, \"name\": 186, \"data\": [0.15151415598150764], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 188, \"name\": 188, \"data\": [0.04451837512530479], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 190, \"name\": 190, \"data\": [0.1520563795260858], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 191, \"name\": 191, \"data\": [0.1786462584931999], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 193, \"name\": 193, \"data\": [0.053177551649090146], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 192, \"name\": 192, \"data\": [0.14472973983650902], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 194, \"name\": 194, \"data\": [0.054900115992861584], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 195, \"name\": 195, \"data\": [0.04254414245368528], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 196, \"name\": 196, \"data\": [0.14486217170496093], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 197, \"name\": 197, \"data\": [0.03664490606115834], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 198, \"name\": 198, \"data\": [0.048453430449932035], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 200, \"name\": 200, \"data\": [0.044266296516479134], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 199, \"name\": 199, \"data\": [0.2746313951281759], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 201, \"name\": 201, \"data\": [0.15348734379351847], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 202, \"name\": 202, \"data\": [0.21297826247643617], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 205, \"name\": 205, \"data\": [0.04196053742605805], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 204, \"name\": 204, \"data\": [0.047259488199925224], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 206, \"name\": 206, \"data\": [0.03966767527621385], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 207, \"name\": 207, \"data\": [0.187594158349983], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 208, \"name\": 208, \"data\": [0.055245548220513856], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 210, \"name\": 210, \"data\": [0.03788378053991587], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 211, \"name\": 211, \"data\": [0.04414460464639597], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 212, \"name\": 212, \"data\": [0.03974634421644986], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 213, \"name\": 213, \"data\": [0.03892819979565169], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 215, \"name\": 215, \"data\": [0.048574893107037026], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 214, \"name\": 214, \"data\": [0.06455837141522046], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 216, \"name\": 216, \"data\": [0.044257999054543355], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 217, \"name\": 217, \"data\": [0.08197838072524594], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 218, \"name\": 218, \"data\": [0.0575881304582273], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 220, \"name\": 220, \"data\": [0.03762870479169334], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 221, \"name\": 221, \"data\": [0.04562779542672475], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 222, \"name\": 222, \"data\": [0.14833544307425817], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 224, \"name\": 224, \"data\": [0.037676633265050205], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 225, \"name\": 225, \"data\": [0.055760500968697564], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 228, \"name\": 228, \"data\": [0.0694159990786754], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 227, \"name\": 227, \"data\": [0.053042668456257054], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 229, \"name\": 229, \"data\": [0.053170296214206346], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 230, \"name\": 230, \"data\": [0.051997571010643055], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 232, \"name\": 232, \"data\": [0.052435778661467626], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 233, \"name\": 233, \"data\": [0.14373241855063404], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 234, \"name\": 234, \"data\": [0.20241918002732387], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 235, \"name\": 235, \"data\": [0.22169824639535718], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 237, \"name\": 237, \"data\": [0.14739667411110355], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 236, \"name\": 236, \"data\": [0.07980796453531956], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 238, \"name\": 238, \"data\": [0.05209170633529393], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 239, \"name\": 239, \"data\": [0.03802910824169105], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 241, \"name\": 241, \"data\": [0.040100943430600965], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 242, \"name\": 242, \"data\": [0.03761191982278062], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 244, \"name\": 244, \"data\": [0.1792086748187224], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 246, \"name\": 246, \"data\": [0.07431630524213113], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 247, \"name\": 247, \"data\": [0.2087196619560656], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 248, \"name\": 248, \"data\": [0.12513280558318474], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 249, \"name\": 249, \"data\": [0.21412900793720202], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 250, \"name\": 250, \"data\": [0.03781864226253416], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 251, \"name\": 251, \"data\": [0.16484870159130346], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 253, \"name\": 253, \"data\": [0.0654896200105806], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 255, \"name\": 255, \"data\": [0.15974997109834513], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 254, \"name\": 254, \"data\": [0.04743544756358511], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 256, \"name\": 256, \"data\": [0.07105022963067106], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 257, \"name\": 257, \"data\": [0.052741581683349525], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 258, \"name\": 258, \"data\": [0.1451763519577152], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 260, \"name\": 260, \"data\": [0.14701299744223], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 261, \"name\": 261, \"data\": [0.04140873711594621], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 262, \"name\": 262, \"data\": [0.046591661246966856], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 263, \"name\": 263, \"data\": [0.07117035391135988], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 264, \"name\": 264, \"data\": [0.060305819911642], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 265, \"name\": 265, \"data\": [0.06545579711783721], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 266, \"name\": 266, \"data\": [0.04514963129026984], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 267, \"name\": 267, \"data\": [0.14684659111992188], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 268, \"name\": 268, \"data\": [0.06898916721408725], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 270, \"name\": 270, \"data\": [0.14451096778387537], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 271, \"name\": 271, \"data\": [0.15037026518280358], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 272, \"name\": 272, \"data\": [0.04053011190327448], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 273, \"name\": 273, \"data\": [0.15253666959531084], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 274, \"name\": 274, \"data\": [0.06514008891600606], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 275, \"name\": 275, \"data\": [0.13357772165629275], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 276, \"name\": 276, \"data\": [0.19177053731612895], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 277, \"name\": 277, \"data\": [0.04429317623906634], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 279, \"name\": 279, \"data\": [0.1459097753561922], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 278, \"name\": 278, \"data\": [0.05258351233793241], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 280, \"name\": 280, \"data\": [0.048313751580639205], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 281, \"name\": 281, \"data\": [0.04975158267709675], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 283, \"name\": 283, \"data\": [0.05805790308659272], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 284, \"name\": 284, \"data\": [0.05056179421163605], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 285, \"name\": 285, \"data\": [0.03992790636493428], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 286, \"name\": 286, \"data\": [0.056895170493245405], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 287, \"name\": 287, \"data\": [0.14410736377257594], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 288, \"name\": 288, \"data\": [0.03813300882450517], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 289, \"name\": 289, \"data\": [0.04638876276814042], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 290, \"name\": 290, \"data\": [0.04720528197982372], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 291, \"name\": 291, \"data\": [0.04258741982385459], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 293, \"name\": 293, \"data\": [0.041070133214274954], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 294, \"name\": 294, \"data\": [0.044377782584956595], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 295, \"name\": 295, \"data\": [0.14309428719605527], \"mode\": \"lines\", \"stepped\": false}, {\"run_id\": 296, \"name\": 296, \"data\": [0.03919602212825984], \"mode\": \"lines\", \"stepped\": false}]}, \"metricName\": null, \"primaryMetricName\": \"Loss\", \"showLegend\": true}, \"run_metrics\": [{\"name\": \"best_child_by_primary_metric\", \"run_id\": \"cnn_1576035422508121\", \"categories\": [0], \"series\": [{\"data\": [{\"metric_name\": [\"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\", \"Loss\"], \"timestamp\": [\"2019-12-11 03:51:35.488148+00:00\", \"2019-12-11 03:52:05.475227+00:00\", \"2019-12-11 03:52:35.543195+00:00\", \"2019-12-11 03:53:08.092823+00:00\", \"2019-12-11 03:54:12.531500+00:00\", \"2019-12-11 03:54:42.776865+00:00\", \"2019-12-11 03:55:12.781408+00:00\", \"2019-12-11 03:55:42.658465+00:00\", \"2019-12-11 03:56:12.635845+00:00\", \"2019-12-11 03:56:45.453599+00:00\", \"2019-12-11 04:06:54.624317+00:00\", \"2019-12-11 04:07:24.705799+00:00\", \"2019-12-11 04:07:54.628954+00:00\", \"2019-12-11 04:08:24.711314+00:00\", \"2019-12-11 04:37:29.057441+00:00\", \"2019-12-11 04:37:29.057441+00:00\"], \"run_id\": [\"cnn_1576035422508121_2\", \"cnn_1576035422508121_3\", \"cnn_1576035422508121_3\", \"cnn_1576035422508121_3\", \"cnn_1576035422508121_6\", \"cnn_1576035422508121_6\", \"cnn_1576035422508121_6\", \"cnn_1576035422508121_6\", \"cnn_1576035422508121_6\", \"cnn_1576035422508121_6\", \"cnn_1576035422508121_26\", \"cnn_1576035422508121_26\", \"cnn_1576035422508121_26\", \"cnn_1576035422508121_26\", \"cnn_1576035422508121_74\", \"cnn_1576035422508121_74\"], \"metric_value\": [0.007282407866886895, 0.0038910003045137713, 0.003321242216626226, 0.0029059401590500783, 0.0026084838723619214, 0.0022309668040598877, 0.002024961239823994, 0.001897162662540781, 0.001794204137217537, 0.0017414035854717736, 0.0017151761818946665, 0.0016027969165992467, 0.0015492243354569733, 0.001527197303406981, 0.0014921563051374907, 0.0014921563051374907], \"final\": [false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, true]}]}]}], \"run_logs\": \"[2019-12-11T03:37:02.786948][API][INFO]Experiment created\\r\\n[2019-12-11T03:37:03.246296][GENERATOR][INFO]Trying to sample '4' jobs from the hyperparameter space\\r\\n[2019-12-11T03:37:03.358709][GENERATOR][INFO]Successfully sampled '4' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:37:03.6237438Z][SCHEDULER][INFO]The execution environment is being prepared. Please be patient as it can take a few minutes.\\r\\n[2019-12-11T03:37:33.9962392Z][SCHEDULER][INFO]The execution environment was successfully prepared.\\r\\n[2019-12-11T03:37:34.0029466Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_2'\\r\\n[2019-12-11T03:37:34.0015874Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_1'\\r\\n[2019-12-11T03:37:34.0044140Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_3'\\r\\n[2019-12-11T03:37:34.0013935Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_0'\\r\\n[2019-12-11T03:37:34.4854686Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_0'\\r\\n[2019-12-11T03:37:34.5440297Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_3'\\r\\n[2019-12-11T03:37:34.8364205Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_2'\\r\\n[2019-12-11T03:37:35.7667525Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_1'\\r\\n[2019-12-11T03:48:34.442228][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_0', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_2', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_3', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:49:04.459246][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_0', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_1', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_2', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_3', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:49:34.588063][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_0', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_1', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_2', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_3', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:50:04.454564][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_0', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_1', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_2', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_3', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:50:34.583555][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_0', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_1', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_2', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_3', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:51:04.720116][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_0', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_1', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_2', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_3', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:51:34.688646][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_0', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_1', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_3', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:52:04.526487][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_0, best experiment metric: 0.004205411354067922, job's best metric: 0.2252387340491853\\r\\n[2019-12-11T03:52:04.648385][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T03:52:07.492095][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T03:52:07.596873][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:52:13.3594536Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_0'\\r\\n[2019-12-11T03:52:13.8537589Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_0', previous status = 'RUNNING')]\\r\\n[2019-12-11T03:52:13.9122388Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_4'\\r\\n[2019-12-11T03:52:14.9869778Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_4'\\r\\n[2019-12-11T03:52:34.664002][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_4', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:52:37.438362][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T03:52:37.685777][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:52:45.2085432Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_5'\\r\\n[2019-12-11T03:52:45.2123769Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_6'\\r\\n[2019-12-11T03:52:45.6484737Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_6'\\r\\n[2019-12-11T03:52:45.6693740Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_5'\\r\\n[2019-12-11T03:53:04.565870][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_4', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_5', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_6', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:53:34.473232][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_5', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_6', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:53:37.468837][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T03:53:37.705785][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:53:46.1932993Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_7'\\r\\n[2019-12-11T03:53:46.1957212Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_8'\\r\\n[2019-12-11T03:53:46.6052343Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_7'\\r\\n[2019-12-11T03:53:46.6645089Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_8'\\r\\n[2019-12-11T03:54:04.536812][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_5', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_6', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_7', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_8', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:54:34.475199][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_7', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_8', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:54:37.401861][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T03:54:37.517605][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:54:47.3351963Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_9'\\r\\n[2019-12-11T03:54:48.0091116Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_9'\\r\\n[2019-12-11T03:55:04.624753][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_8', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_9', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:55:34.571922][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_9', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:55:37.485957][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T03:55:37.595560][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:55:48.4800096Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_10'\\r\\n[2019-12-11T03:55:48.9634022Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_10'\\r\\n[2019-12-11T03:56:06.668039][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_8, best experiment metric: 0.0022730311461786, job's best metric: 0.0030879940078069345\\r\\n[2019-12-11T03:56:06.793650][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T03:56:06.793562][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_10', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:56:19.1406104Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_8'\\r\\n[2019-12-11T03:56:19.4006844Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_8', previous status = 'RUNNING')]\\r\\n[2019-12-11T03:56:36.614512][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_10', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:56:37.489768][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T03:56:37.594103][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:56:49.7129229Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_11'\\r\\n[2019-12-11T03:56:49.7145172Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_12'\\r\\n[2019-12-11T03:56:50.2051945Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_12'\\r\\n[2019-12-11T03:56:50.3118556Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_11'\\r\\n[2019-12-11T03:57:06.665137][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_11', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:57:09.778205][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T03:57:09.889075][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:57:20.6649494Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_13'\\r\\n[2019-12-11T03:57:21.1747312Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_13'\\r\\n[2019-12-11T03:57:36.498989][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_11', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_12', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_13', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:57:39.442950][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T03:57:39.558765][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:57:51.5576514Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_14'\\r\\n[2019-12-11T03:57:52.9195056Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_14'\\r\\n[2019-12-11T03:58:06.470316][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_12', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_13', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_14', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:58:36.500315][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_11, best experiment metric: 0.0024648397939351177, job's best metric: 0.005342740123637193\\r\\n[2019-12-11T03:58:36.634126][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_13', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_14', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:58:36.634317][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T03:59:06.554943][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_12, best experiment metric: 0.0024648397939351177, job's best metric: 0.007402709787275062\\r\\n[2019-12-11T03:59:06.685778][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_14', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:59:06.685862][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T03:59:09.469549][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T03:59:09.575700][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:59:23.5154063Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_12'\\r\\n[2019-12-11T03:59:23.9674882Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_12', previous status = 'RUNNING')]\\r\\n[2019-12-11T03:59:24.0265167Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_15'\\r\\n[2019-12-11T03:59:24.0280445Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_16'\\r\\n[2019-12-11T03:59:24.4351306Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_16'\\r\\n[2019-12-11T03:59:24.5235700Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_15'\\r\\n[2019-12-11T03:59:36.484021][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_12, best experiment metric: 0.002170936770999074, job's best metric: 0.006609570124684179\\r\\n[2019-12-11T03:59:36.618589][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_15', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_16', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T03:59:36.618690][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T03:59:39.498482][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T03:59:39.712148][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T03:59:54.7853237Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_17'\\r\\n[2019-12-11T03:59:55.2550709Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_17'\\r\\n[2019-12-11T04:00:06.551538][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_15', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_16', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_17', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:00:09.532207][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:00:09.658004][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:00:25.4531440Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_18'\\r\\n[2019-12-11T04:00:25.9730333Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_18'\\r\\n[2019-12-11T04:00:36.558253][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_15', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_16', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_17', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:01:06.658862][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_15, best experiment metric: 0.002532661279391498, job's best metric: 0.006684117814697017\\r\\n[2019-12-11T04:01:06.765271][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_16, best experiment metric: 0.002532661279391498, job's best metric: 0.024843308993877367\\r\\n[2019-12-11T04:01:06.878367][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T04:01:06.878273][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_18', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:01:26.4202633Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_15'\\r\\n[2019-12-11T04:01:26.4210212Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_16'\\r\\n[2019-12-11T04:01:26.9866258Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_15', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_16', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:01:46.612638][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:01:46.721524][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:01:57.2620788Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_19'\\r\\n[2019-12-11T04:01:57.2639295Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_20'\\r\\n[2019-12-11T04:01:57.7006143Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_20'\\r\\n[2019-12-11T04:01:57.7662541Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_19'\\r\\n[2019-12-11T04:02:18.769293][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:02:18.956528][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:02:28.0230375Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_21'\\r\\n[2019-12-11T04:02:28.0247842Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_22'\\r\\n[2019-12-11T04:02:28.3991485Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_21'\\r\\n[2019-12-11T04:02:28.4560303Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_22'\\r\\n[2019-12-11T04:02:43.010579][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_19', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_20', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_21', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_22', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:03:12.527796][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_19', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_20', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_21', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_22', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:03:42.440027][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_19, best experiment metric: 0.0022973647759331082, job's best metric: 0.0031124532929580474\\r\\n[2019-12-11T04:03:42.932867][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_22', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:03:42.932955][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:03:48.430227][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:03:48.550587][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:03:59.1940824Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_19'\\r\\n[2019-12-11T04:03:59.5599542Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_19', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:03:59.6315307Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_23'\\r\\n[2019-12-11T04:04:00.6829390Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_23'\\r\\n[2019-12-11T04:04:12.508611][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_23', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:04:18.587588][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2019-12-11T04:04:18.739054][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:04:31.0292459Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_24'\\r\\n[2019-12-11T04:04:31.0308507Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_25'\\r\\n[2019-12-11T04:04:31.0325358Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_26'\\r\\n[2019-12-11T04:04:31.5291898Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_25'\\r\\n[2019-12-11T04:04:31.6226401Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_24'\\r\\n[2019-12-11T04:04:31.6175304Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_26'\\r\\n[2019-12-11T04:04:42.767711][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_23', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_24', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_25', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_26', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:05:12.539951][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_23', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_24', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_25', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_26', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:05:42.678633][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_23, best experiment metric: 0.002456834476022602, job's best metric: 0.0079884422619823\\r\\n[2019-12-11T04:05:42.790095][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_25', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_26', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:05:42.790180][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:06:02.4177192Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_23'\\r\\n[2019-12-11T04:06:02.9689404Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_23', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:06:12.543609][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_24, best experiment metric: 0.0022973647759331082, job's best metric: 0.008190476113845458\\r\\n[2019-12-11T04:06:12.693924][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:06:18.729834][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:06:18.884716][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:06:33.3163285Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_24'\\r\\n[2019-12-11T04:06:33.7132605Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_24', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:06:33.7670346Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_27'\\r\\n[2019-12-11T04:06:34.1567069Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_27'\\r\\n[2019-12-11T04:06:45.256905][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_25, best experiment metric: 0.0021901861766665458, job's best metric: 0.15531914313985584\\r\\n[2019-12-11T04:06:45.382232][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:06:45.382125][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_27', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:06:48.486540][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:06:48.611223][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:07:04.3817328Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_28'\\r\\n[2019-12-11T04:07:05.0606350Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_28'\\r\\n[2019-12-11T04:07:15.583174][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_27', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_28', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:07:18.541136][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:07:18.649104][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:07:35.2910948Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_29'\\r\\n[2019-12-11T04:07:35.7867912Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_29'\\r\\n[2019-12-11T04:07:45.689854][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_27', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_28', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:08:15.552491][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_27, best experiment metric: 0.002027902410519459, job's best metric: 0.005279133031253582\\r\\n[2019-12-11T04:08:15.721774][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:08:15.721684][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_28', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_29', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:08:36.3007764Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_27'\\r\\n[2019-12-11T04:08:36.7304650Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_27', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:08:45.488473][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_27, best experiment metric: 0.001645146560622632, job's best metric: 0.0046039135852626225\\r\\n[2019-12-11T04:08:45.587339][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_28, best experiment metric: 0.0021361495521808197, job's best metric: 0.05695115713140514\\r\\n[2019-12-11T04:08:45.701712][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T04:08:45.701632][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_29', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:08:48.677084][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:08:48.783238][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:09:07.0553273Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_30'\\r\\n[2019-12-11T04:09:07.0571271Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_31'\\r\\n[2019-12-11T04:09:07.5075592Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_30'\\r\\n[2019-12-11T04:09:07.5371695Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_31'\\r\\n[2019-12-11T04:09:18.512413][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:09:18.614486][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:09:37.7457723Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_32'\\r\\n[2019-12-11T04:09:37.7473291Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_33'\\r\\n[2019-12-11T04:09:38.1707894Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_32'\\r\\n[2019-12-11T04:09:39.9799445Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_33'\\r\\n[2019-12-11T04:09:45.575458][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_30', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_31', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:10:15.590402][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_31', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_32', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_33', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:10:45.643920][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_32', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_33', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:10:48.801367][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:10:48.935614][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:11:10.7137912Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_34'\\r\\n[2019-12-11T04:11:10.7155725Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_35'\\r\\n[2019-12-11T04:11:11.3140264Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_34'\\r\\n[2019-12-11T04:11:11.3768541Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_35'\\r\\n[2019-12-11T04:11:15.925781][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_33, best experiment metric: 0.0021901861766665458, job's best metric: 0.04094946826408589\\r\\n[2019-12-11T04:11:16.040158][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:11:18.778305][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:11:18.913153][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:11:41.7090018Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_36'\\r\\n[2019-12-11T04:11:42.2113336Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_36'\\r\\n[2019-12-11T04:11:48.264239][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_34', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_35', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:11:48.656796][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:11:48.774398][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:12:12.4352490Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_37'\\r\\n[2019-12-11T04:12:13.0676183Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_37'\\r\\n[2019-12-11T04:12:18.643202][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_34', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_35', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_36', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:12:48.673090][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_35', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_36', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_37', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:13:18.556903][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_37', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:13:18.540254][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:13:18.709101][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:13:43.9008849Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_38'\\r\\n[2019-12-11T04:13:43.9024331Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_39'\\r\\n[2019-12-11T04:13:44.5176772Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_39'\\r\\n[2019-12-11T04:13:45.1724650Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_38'\\r\\n[2019-12-11T04:13:48.532444][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:13:48.732504][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:14:15.8049874Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_40'\\r\\n[2019-12-11T04:14:15.8066135Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_41'\\r\\n[2019-12-11T04:14:16.3791502Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_41'\\r\\n[2019-12-11T04:14:16.6428650Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_40'\\r\\n[2019-12-11T04:14:21.540366][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_38', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_39', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:14:51.507255][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_38', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_39', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_40', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_41', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:15:21.516408][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_38, best experiment metric: 0.002456834476022602, job's best metric: 0.007144473771451586\\r\\n[2019-12-11T04:15:21.628099][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_39, best experiment metric: 0.002252599788513997, job's best metric: 0.05999664433970517\\r\\n[2019-12-11T04:15:21.735890][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T04:15:21.735806][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_40', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:15:47.4520672Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_38'\\r\\n[2019-12-11T04:15:47.8679535Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_38', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:15:51.247524][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:15:51.353581][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:15:51.750279][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_38, best experiment metric: 0.0017651133092852574, job's best metric: 0.006014278078568645\\r\\n[2019-12-11T04:15:51.871768][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:16:18.1630694Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_42'\\r\\n[2019-12-11T04:16:18.8740561Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_42'\\r\\n[2019-12-11T04:16:21.618327][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_40, best experiment metric: 0.0018215526093190372, job's best metric: 0.005860967810450699\\r\\n[2019-12-11T04:16:21.750872][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_42', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:16:21.750956][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:16:31.221213][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:16:31.341636][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:16:49.1933786Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_40'\\r\\n[2019-12-11T04:16:50.0975402Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_40', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:16:50.1610163Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_43'\\r\\n[2019-12-11T04:16:50.1627607Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_44'\\r\\n[2019-12-11T04:16:50.5253480Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_44'\\r\\n[2019-12-11T04:16:50.5511419Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_43'\\r\\n[2019-12-11T04:16:51.554340][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_40, best experiment metric: 0.0017268186240333076, job's best metric: 0.005555269872303349\\r\\n[2019-12-11T04:16:51.666448][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_42', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_43', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_44', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:16:51.666608][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:17:01.636386][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:17:01.750908][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:17:20.7718858Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_45'\\r\\n[2019-12-11T04:17:21.2935564Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_45'\\r\\n[2019-12-11T04:17:21.581637][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_42', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_43', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_44', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_45', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:17:51.680616][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_43', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_44', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_45', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:18:21.644955][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_42, best experiment metric: 0.0017886816555501984, job's best metric: 0.0020128813195705287\\r\\n[2019-12-11T04:18:21.7995589Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_42'\\r\\n[2019-12-11T04:18:21.773709][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_45', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:18:21.773876][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:18:22.2312272Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_42', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:18:31.577181][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2019-12-11T04:18:31.815672][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:18:52.5830917Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_46'\\r\\n[2019-12-11T04:18:52.5847951Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_47'\\r\\n[2019-12-11T04:18:52.5864003Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_48'\\r\\n[2019-12-11T04:18:53.0176221Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_47'\\r\\n[2019-12-11T04:18:53.0001090Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_46'\\r\\n[2019-12-11T04:18:53.0605719Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_48'\\r\\n[2019-12-11T04:19:01.782588][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:19:01.970031][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:19:21.678356][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_46', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_47', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_48', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:19:23.4201254Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_49'\\r\\n[2019-12-11T04:19:24.7194929Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_49'\\r\\n[2019-12-11T04:19:51.527288][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_46', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_47', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_48', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_49', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:20:21.690749][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_46', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_47', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_49', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:20:31.686958][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:20:31.817343][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:20:51.543506][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_47, best experiment metric: 0.002104669826383208, job's best metric: 0.11564231035406608\\r\\n[2019-12-11T04:20:51.651975][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_49', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:20:51.652058][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:20:55.7105746Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_47'\\r\\n[2019-12-11T04:20:56.1999847Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_47', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:20:56.2580949Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_50'\\r\\n[2019-12-11T04:20:56.6781648Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_50'\\r\\n[2019-12-11T04:21:01.578977][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:21:01.697553][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:21:21.575192][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_49, best experiment metric: 0.0017651133092852574, job's best metric: 0.15396478664895596\\r\\n[2019-12-11T04:21:21.694607][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:21:21.694506][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_50', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:21:26.9452448Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_51'\\r\\n[2019-12-11T04:21:27.3046138Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_51'\\r\\n[2019-12-11T04:21:31.511326][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:21:31.659139][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:21:51.678733][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_50', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_51', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:21:57.6035121Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_52'\\r\\n[2019-12-11T04:21:57.6050452Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_53'\\r\\n[2019-12-11T04:21:58.0783169Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_53'\\r\\n[2019-12-11T04:21:58.3397069Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_52'\\r\\n[2019-12-11T04:22:21.660432][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_50, best experiment metric: 0.0018652372758639528, job's best metric: 0.00489785111167235\\r\\n[2019-12-11T04:22:21.760544][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_51', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_52', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_53', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:22:21.760629][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:22:28.9015896Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_50'\\r\\n[2019-12-11T04:22:29.3757026Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_50', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:22:51.703201][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_52', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_53', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:23:01.607932][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:23:01.715376][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:23:30.1152123Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_54'\\r\\n[2019-12-11T04:23:30.7482328Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_54'\\r\\n[2019-12-11T04:23:31.662919][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:23:31.876455][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:23:51.543348][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_53, best experiment metric: 0.0018215526093190372, job's best metric: 0.0022276500501962203\\r\\n[2019-12-11T04:23:51.650883][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:23:51.650761][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_54', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:24:01.0969793Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_53'\\r\\n[2019-12-11T04:24:01.4855566Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_53', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:24:01.5449500Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_55'\\r\\n[2019-12-11T04:24:01.5472357Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_56'\\r\\n[2019-12-11T04:24:02.0010987Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_55'\\r\\n[2019-12-11T04:24:02.0280023Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_56'\\r\\n[2019-12-11T04:24:21.912599][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_54', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_55', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_56', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:24:31.519881][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:24:31.631701][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:24:32.3736797Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_57'\\r\\n[2019-12-11T04:24:32.9432631Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_57'\\r\\n[2019-12-11T04:24:51.650900][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_54, best experiment metric: 0.0022699220155715035, job's best metric: 0.06010292557429491\\r\\n[2019-12-11T04:24:51.759363][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_55', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_56', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_57', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:24:51.759449][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:25:03.2761958Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_54'\\r\\n[2019-12-11T04:25:04.4070090Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_54', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:25:21.548694][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_56, best experiment metric: 0.0019200586521339676, job's best metric: 0.0028064894968546417\\r\\n[2019-12-11T04:25:21.654029][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_57', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:25:21.654126][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:25:31.607036][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:25:31.831332][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:25:34.8514531Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_58'\\r\\n[2019-12-11T04:25:34.8530236Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_59'\\r\\n[2019-12-11T04:25:35.5117403Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_58'\\r\\n[2019-12-11T04:25:35.6688414Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_59'\\r\\n[2019-12-11T04:25:51.610612][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_57', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_58', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_59', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:26:01.645775][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:26:01.817434][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:26:05.9825267Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_60'\\r\\n[2019-12-11T04:26:06.4741287Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_60'\\r\\n[2019-12-11T04:26:21.682709][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_58', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_59', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_60', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:26:51.717409][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_59, best experiment metric: 0.001702603261411281, job's best metric: 0.004883573212567681\\r\\n[2019-12-11T04:26:51.868096][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:26:51.867933][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_60', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:27:21.750234][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_60, best experiment metric: 0.0018198878016602365, job's best metric: 0.05220585246808125\\r\\n[2019-12-11T04:27:21.862881][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:27:31.717467][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2019-12-11T04:27:31.826224][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:27:37.3040224Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_60'\\r\\n[2019-12-11T04:27:38.3154977Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_60', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:27:38.3877661Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_61'\\r\\n[2019-12-11T04:27:38.3907355Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_62'\\r\\n[2019-12-11T04:27:38.3924166Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_63'\\r\\n[2019-12-11T04:27:38.7360759Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_61'\\r\\n[2019-12-11T04:27:38.8116067Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_62'\\r\\n[2019-12-11T04:27:38.8841683Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_63'\\r\\n[2019-12-11T04:27:52.134398][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_61', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_62', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:28:01.675329][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:28:01.786844][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:28:09.1898483Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_64'\\r\\n[2019-12-11T04:28:10.5619575Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_64'\\r\\n[2019-12-11T04:28:21.740258][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_61', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_62', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_63', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:28:51.547042][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_61', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_62', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_63', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_64', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:29:21.826482][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_63, best experiment metric: 0.001954057656649594, job's best metric: 0.15530867274076948\\r\\n[2019-12-11T04:29:22.001850][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_62', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_64', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:29:22.001933][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:29:31.686150][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:29:31.791400][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:29:41.5922360Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_63'\\r\\n[2019-12-11T04:29:42.0867402Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_63', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:29:42.1647495Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_65'\\r\\n[2019-12-11T04:29:42.5457523Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_65'\\r\\n[2019-12-11T04:29:51.768196][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_62, best experiment metric: 0.001954057656649594, job's best metric: 0.007979605097035951\\r\\n[2019-12-11T04:29:51.890698][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:30:01.676990][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:30:01.781137][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:30:12.8839648Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_62'\\r\\n[2019-12-11T04:30:13.2440015Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_62', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:30:13.3127520Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_66'\\r\\n[2019-12-11T04:30:13.7223135Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_66'\\r\\n[2019-12-11T04:30:24.636562][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_65', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_66', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:30:31.647448][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:30:31.772695][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:30:44.0407139Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_67'\\r\\n[2019-12-11T04:30:44.0421387Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_68'\\r\\n[2019-12-11T04:30:44.4808519Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_68'\\r\\n[2019-12-11T04:30:44.5743770Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_67'\\r\\n[2019-12-11T04:30:54.649741][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_65', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_66', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:31:24.749281][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_65, best experiment metric: 0.0018820515863534918, job's best metric: 0.007187899285218429\\r\\n[2019-12-11T04:31:24.977988][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:31:24.977894][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_67', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_68', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:31:54.541185][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_66, best experiment metric: 0.0017310225124430104, job's best metric: 0.2326143695035144\\r\\n[2019-12-11T04:31:54.659716][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:31:54.659603][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_68', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:32:01.518332][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:32:01.636984][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:32:15.4689772Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_69'\\r\\n[2019-12-11T04:32:15.4733815Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_70'\\r\\n[2019-12-11T04:32:16.8047602Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_70'\\r\\n[2019-12-11T04:32:16.9595345Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_69'\\r\\n[2019-12-11T04:32:24.696824][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_68, best experiment metric: 0.0017693531733669336, job's best metric: 0.008112720606544516\\r\\n[2019-12-11T04:32:24.817834][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:32:31.630910][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:32:31.743337][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:32:47.2458958Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_68'\\r\\n[2019-12-11T04:32:47.6230618Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_68', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:32:47.6886564Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_71'\\r\\n[2019-12-11T04:32:48.1349145Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_71'\\r\\n[2019-12-11T04:32:57.436264][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_69', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_70', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:33:01.591280][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:33:01.699165][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:33:18.5379756Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_72'\\r\\n[2019-12-11T04:33:19.0187883Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_72'\\r\\n[2019-12-11T04:33:27.877905][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_70', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_71', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:33:58.033301][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_69, best experiment metric: 0.0017693531733669336, job's best metric: 0.0073886748650452685\\r\\n[2019-12-11T04:33:58.152535][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_70, best experiment metric: 0.001954057656649594, job's best metric: 0.0028908095264304177\\r\\n[2019-12-11T04:33:58.258420][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T04:33:58.258323][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_72', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:34:01.718983][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:34:01.836758][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:34:19.6709544Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_70'\\r\\n[2019-12-11T04:34:20.2304144Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_70', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:34:20.3179458Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_73'\\r\\n[2019-12-11T04:34:20.6754933Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_73'\\r\\n[2019-12-11T04:34:28.725478][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_71, best experiment metric: 0.0018198878016602365, job's best metric: 0.007347825920781899\\r\\n[2019-12-11T04:34:28.862441][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:34:32.056433][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:34:32.185840][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:34:50.9559429Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_74'\\r\\n[2019-12-11T04:34:50.9584827Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_75'\\r\\n[2019-12-11T04:34:51.4006030Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_75'\\r\\n[2019-12-11T04:34:51.4359038Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_74'\\r\\n[2019-12-11T04:34:59.047516][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_73', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:35:01.619492][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:35:01.834963][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:35:21.9283142Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_76'\\r\\n[2019-12-11T04:35:23.5246702Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_76'\\r\\n[2019-12-11T04:35:28.608250][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_74', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_75', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:35:58.679411][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_73, best experiment metric: 0.0018198878016602365, job's best metric: 0.0024504674277020305\\r\\n[2019-12-11T04:35:58.868303][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_75', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_76', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:35:58.868411][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:36:24.1631335Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_73'\\r\\n[2019-12-11T04:36:25.4694297Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_73', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:36:28.692211][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_73, best experiment metric: 0.001646545816153777, job's best metric: 0.002098538083042801\\r\\n[2019-12-11T04:36:28.805580][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_74, best experiment metric: 0.001646545816153777, job's best metric: 0.001995195306968414\\r\\n[2019-12-11T04:36:28.912091][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T04:36:31.550850][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:36:31.658685][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:36:55.9201018Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_74'\\r\\n[2019-12-11T04:36:56.2587460Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_74', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:36:56.3255160Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_77'\\r\\n[2019-12-11T04:36:56.6721745Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_77'\\r\\n[2019-12-11T04:36:58.655933][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_76, best experiment metric: 0.001646545816153777, job's best metric: 0.003136034886217146\\r\\n[2019-12-11T04:36:58.755892][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_77', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:36:58.755984][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:37:01.569580][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:37:01.679332][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:37:26.9705594Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_78'\\r\\n[2019-12-11T04:37:27.5199845Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_78'\\r\\n[2019-12-11T04:37:28.706690][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_77', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_78', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:37:31.616206][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:37:31.837172][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:37:57.9422277Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_80'\\r\\n[2019-12-11T04:37:57.9403654Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_79'\\r\\n[2019-12-11T04:37:58.3629659Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_80'\\r\\n[2019-12-11T04:37:58.4142428Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_79'\\r\\n[2019-12-11T04:37:58.737188][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_77', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_78', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_79', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_80', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:38:29.301174][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_77, best experiment metric: 0.0018820515863534918, job's best metric: 0.02674855675086882\\r\\n[2019-12-11T04:38:29.435038][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_78', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_79', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_80', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:38:29.435134][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:38:59.721960][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_79', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_80', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:39:01.576777][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:39:01.685915][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:39:29.2806775Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_81'\\r\\n[2019-12-11T04:39:29.740596][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_80', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:39:29.9566288Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_81'\\r\\n[2019-12-11T04:39:31.709121][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:39:31.802695][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:39:59.717504][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_81', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:40:00.2648147Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_82'\\r\\n[2019-12-11T04:40:00.8305094Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_82'\\r\\n[2019-12-11T04:40:01.698172][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:40:01.810075][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:40:29.764440][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_81', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_82', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:40:31.1632176Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_83'\\r\\n[2019-12-11T04:40:31.573394][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:40:31.6367290Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_83'\\r\\n[2019-12-11T04:40:31.685771][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:40:59.668721][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_81, best experiment metric: 0.0016678661300334654, job's best metric: 0.002383798868222816\\r\\n[2019-12-11T04:40:59.845617][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_82', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_83', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:40:59.845709][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:41:02.1037759Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_81'\\r\\n[2019-12-11T04:41:02.5375619Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_81', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:41:02.5995266Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_84'\\r\\n[2019-12-11T04:41:04.0089069Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_84'\\r\\n[2019-12-11T04:41:29.781128][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_83', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_84', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:41:31.835541][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:41:31.955107][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:41:34.3717532Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_85'\\r\\n[2019-12-11T04:41:34.8830496Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_85'\\r\\n[2019-12-11T04:41:59.933862][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_83, best experiment metric: 0.0018560787698855016, job's best metric: 0.00501683276644459\\r\\n[2019-12-11T04:42:00.053454][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:42:00.053234][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_84', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_85', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:42:01.690108][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:42:01.800917][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:42:05.2228841Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_83'\\r\\n[2019-12-11T04:42:05.5815352Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_83', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:42:05.6443647Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_86'\\r\\n[2019-12-11T04:42:06.0115379Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_86'\\r\\n[2019-12-11T04:42:29.686861][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_85', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_86', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:42:31.683013][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:42:31.789548][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:42:36.2700115Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_87'\\r\\n[2019-12-11T04:42:36.8219969Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_87'\\r\\n[2019-12-11T04:42:59.621491][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_85, best experiment metric: 0.0015388208275550653, job's best metric: 0.047755043659940065\\r\\n[2019-12-11T04:42:59.725921][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:42:59.725843][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_86', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_87', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:43:01.609536][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:43:01.729141][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:43:07.1894972Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_88'\\r\\n[2019-12-11T04:43:07.7996674Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_88'\\r\\n[2019-12-11T04:43:29.755285][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_87', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_88', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:43:31.640573][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:43:31.763421][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:43:38.1815100Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_89'\\r\\n[2019-12-11T04:43:39.5668450Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_89'\\r\\n[2019-12-11T04:44:00.370952][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_86, best experiment metric: 0.001583848154992595, job's best metric: 0.040946687393872236\\r\\n[2019-12-11T04:44:00.470187][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_87, best experiment metric: 0.0017221678624627353, job's best metric: 0.00693749673527807\\r\\n[2019-12-11T04:44:00.604456][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T04:44:00.604273][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_88', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_89', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:44:04.551140][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:44:04.660980][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:44:09.8877132Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_87'\\r\\n[2019-12-11T04:44:10.3448943Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_87', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:44:10.4044997Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_90'\\r\\n[2019-12-11T04:44:10.8309237Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_90'\\r\\n[2019-12-11T04:44:31.010516][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_89', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_90', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:44:34.765973][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:44:34.879500][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:44:41.2192829Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_91'\\r\\n[2019-12-11T04:44:41.2191053Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_92'\\r\\n[2019-12-11T04:44:41.6334441Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_92'\\r\\n[2019-12-11T04:44:41.6700708Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_91'\\r\\n[2019-12-11T04:45:00.700058][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_90', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_91', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_92', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:45:30.739243][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_90, best experiment metric: 0.001523607883647456, job's best metric: 0.006100740430220635\\r\\n[2019-12-11T04:45:30.859867][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:45:30.859778][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_91', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_92', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:45:34.726214][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:45:34.840571][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:45:42.2516518Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_90'\\r\\n[2019-12-11T04:45:42.6362873Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_90', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:45:42.7121319Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_93'\\r\\n[2019-12-11T04:45:43.1136131Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_93'\\r\\n[2019-12-11T04:46:00.638862][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_91, best experiment metric: 0.0015603774933573918, job's best metric: 0.0030330508139960848\\r\\n[2019-12-11T04:46:00.769315][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_92', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_93', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:46:00.769406][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:46:04.602439][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:46:04.758193][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:46:13.4497923Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_91'\\r\\n[2019-12-11T04:46:13.7978515Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_91', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:46:13.8625852Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_94'\\r\\n[2019-12-11T04:46:14.3040822Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_94'\\r\\n[2019-12-11T04:46:30.930902][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_92, best experiment metric: 0.0017221678624627353, job's best metric: 0.0029456677641462358\\r\\n[2019-12-11T04:46:31.042637][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_93', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_94', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:46:31.042736][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:46:34.903959][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:46:35.018606][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:46:44.6147404Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_92'\\r\\n[2019-12-11T04:46:44.9219272Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_92', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:46:44.9966310Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_95'\\r\\n[2019-12-11T04:46:45.4362954Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_95'\\r\\n[2019-12-11T04:47:01.043252][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_93, best experiment metric: 0.0017221678624627353, job's best metric: 0.0076095507612394695\\r\\n[2019-12-11T04:47:01.184472][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_94', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_95', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:47:01.184556][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:47:04.687634][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:47:04.798277][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:47:15.7916960Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_93'\\r\\n[2019-12-11T04:47:16.8807609Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_93', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:47:16.9694521Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_96'\\r\\n[2019-12-11T04:47:17.3506489Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_96'\\r\\n[2019-12-11T04:47:31.678829][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_93, best experiment metric: 0.0015055120028568342, job's best metric: 0.00737012180515877\\r\\n[2019-12-11T04:47:31.783289][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_95', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_96', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:47:31.783399][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:47:47.6850931Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_93'\\r\\n[2019-12-11T04:47:48.3454710Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_93', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:48:01.708974][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_93, best experiment metric: 0.0015055120028568342, job's best metric: 0.00737012180515877\\r\\n[2019-12-11T04:48:01.815099][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_94, best experiment metric: 0.0016307780971795652, job's best metric: 0.02123085768913044\\r\\n[2019-12-11T04:48:01.918320][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_95, best experiment metric: 0.0017221678624627353, job's best metric: 0.033218957704612126\\r\\n[2019-12-11T04:48:02.021650][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_96', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:48:02.021733][ENFORCER][INFO]Policy cancelled 3 jobs\\r\\n[2019-12-11T04:48:18.7025339Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_94'\\r\\n[2019-12-11T04:48:18.7017734Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_93'\\r\\n[2019-12-11T04:48:19.1288016Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_93', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_94', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:48:31.731799][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_96, best experiment metric: 0.0016678661300334654, job's best metric: 0.05818720279173222\\r\\n[2019-12-11T04:48:31.848979][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:48:38.833443][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2019-12-11T04:48:38.957689][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:48:49.5270384Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_97'\\r\\n[2019-12-11T04:48:49.5299409Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_99'\\r\\n[2019-12-11T04:48:49.5286339Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_98'\\r\\n[2019-12-11T04:48:50.0426252Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_98'\\r\\n[2019-12-11T04:48:50.0997673Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_97'\\r\\n[2019-12-11T04:48:50.2434068Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_99'\\r\\n[2019-12-11T04:49:01.708900][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_97', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_99', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:49:09.033705][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:49:09.147694][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:49:20.6248577Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_100'\\r\\n[2019-12-11T04:49:21.0793176Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_100'\\r\\n[2019-12-11T04:49:32.009984][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_97', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_98', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_99', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_100', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:50:01.654469][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_97', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_98', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_100', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:50:31.771466][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_97, best experiment metric: 0.0016307780971795652, job's best metric: 0.0028120517993333962\\r\\n[2019-12-11T04:50:31.874460][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_99, best experiment metric: 0.0016307780971795652, job's best metric: 0.006908877697596422\\r\\n[2019-12-11T04:50:31.987518][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_100', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:50:31.987641][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T04:50:52.1826460Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_97'\\r\\n[2019-12-11T04:50:52.1834396Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_99'\\r\\n[2019-12-11T04:50:52.9037293Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_97', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_99', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:51:01.980687][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_98, best experiment metric: 0.0017792358081197479, job's best metric: 0.05245295786140806\\r\\n[2019-12-11T04:51:02.080475][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_100, best experiment metric: 0.0017221678624627353, job's best metric: 0.006988799399263467\\r\\n[2019-12-11T04:51:02.197327][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T04:51:08.603544][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2019-12-11T04:51:08.747990][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:51:23.4100317Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_98'\\r\\n[2019-12-11T04:51:23.8571107Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_98', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:51:23.9239327Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_101'\\r\\n[2019-12-11T04:51:23.9278600Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_103'\\r\\n[2019-12-11T04:51:23.9255856Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_102'\\r\\n[2019-12-11T04:51:24.3330673Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_101'\\r\\n[2019-12-11T04:51:24.3745256Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_103'\\r\\n[2019-12-11T04:51:24.4265030Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_102'\\r\\n[2019-12-11T04:51:38.674912][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:51:38.794628][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:51:54.8586159Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_104'\\r\\n[2019-12-11T04:51:55.3342348Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_104'\\r\\n[2019-12-11T04:52:01.844524][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_101', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_102', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_103', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:52:31.674199][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_101', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_102', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_103', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_104', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:53:04.400416][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_103, best experiment metric: 0.0017221678624627353, job's best metric: 0.07389346084598157\\r\\n[2019-12-11T04:53:04.561670][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_102', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_104', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:53:04.561837][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:53:09.011034][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:53:09.249531][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:53:26.7316249Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_105'\\r\\n[2019-12-11T04:53:27.4419209Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_105'\\r\\n[2019-12-11T04:53:35.070146][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_102, best experiment metric: 0.0015603774933573918, job's best metric: 0.007650524499432556\\r\\n[2019-12-11T04:53:35.393112][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_104, best experiment metric: 0.0016307780971795652, job's best metric: 0.07288265622098403\\r\\n[2019-12-11T04:53:35.495723][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T04:53:39.815394][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:53:39.922333][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:53:57.8298196Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_106'\\r\\n[2019-12-11T04:53:58.9594259Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_106'\\r\\n[2019-12-11T04:54:05.914152][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_105', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:54:09.745675][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:54:09.858691][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:54:29.5251035Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_107'\\r\\n[2019-12-11T04:54:29.5267840Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_108'\\r\\n[2019-12-11T04:54:29.9560305Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_108'\\r\\n[2019-12-11T04:54:30.0657412Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_107'\\r\\n[2019-12-11T04:54:36.046864][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_106', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:55:05.886755][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_105, best experiment metric: 0.0016678661300334654, job's best metric: 0.07409506540357143\\r\\n[2019-12-11T04:55:06.015507][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:55:06.015295][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_107', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_108', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:55:36.222404][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_106, best experiment metric: 0.0016678661300334654, job's best metric: 0.0031405870176264977\\r\\n[2019-12-11T04:55:36.437090][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_108', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:55:36.437183][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:55:39.756974][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:55:39.903113][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:56:01.1822967Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_106'\\r\\n[2019-12-11T04:56:01.6356898Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_106', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:56:01.7063005Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_109'\\r\\n[2019-12-11T04:56:02.1339428Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_109'\\r\\n[2019-12-11T04:56:06.831669][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_106, best experiment metric: 0.001497901016791068, job's best metric: 0.0028127435817556887\\r\\n[2019-12-11T04:56:06.942478][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_107, best experiment metric: 0.0016678661300334654, job's best metric: 0.005527675198949129\\r\\n[2019-12-11T04:56:07.050214][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_108, best experiment metric: 0.0017792358081197479, job's best metric: 0.050989530866667195\\r\\n[2019-12-11T04:56:07.181927][ENFORCER][INFO]Policy cancelled 3 jobs\\r\\n[2019-12-11T04:56:09.656467][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:56:09.769850][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:56:32.5164697Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_110'\\r\\n[2019-12-11T04:56:32.5179704Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_111'\\r\\n[2019-12-11T04:56:33.0184218Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_111'\\r\\n[2019-12-11T04:56:37.793072][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_109', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:56:37.9335510Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_110'\\r\\n[2019-12-11T04:56:39.729956][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T04:56:39.858246][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:57:07.860462][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_110', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_111', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:57:08.3277952Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_112'\\r\\n[2019-12-11T04:57:08.8062669Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_112'\\r\\n[2019-12-11T04:57:37.814957][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_109, best experiment metric: 0.001497901016791068, job's best metric: 0.002947397192398948\\r\\n[2019-12-11T04:57:37.923147][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_111, best experiment metric: 0.0016307780971795652, job's best metric: 0.003438664026162204\\r\\n[2019-12-11T04:57:38.043855][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T04:57:38.043765][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_110', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_112', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:57:39.1871328Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_109'\\r\\n[2019-12-11T04:57:39.1879310Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_111'\\r\\n[2019-12-11T04:57:39.9780140Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_109', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_111', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:58:07.977747][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_110, best experiment metric: 0.0017792358081197479, job's best metric: 0.0024462097595114142\\r\\n[2019-12-11T04:58:08.148355][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:58:09.742807][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:58:09.872638][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:58:10.3935807Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_110'\\r\\n[2019-12-11T04:58:11.5743811Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_110', previous status = 'RUNNING')]\\r\\n[2019-12-11T04:58:11.6503707Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_113'\\r\\n[2019-12-11T04:58:11.6544955Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_114'\\r\\n[2019-12-11T04:58:12.0873774Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_113'\\r\\n[2019-12-11T04:58:12.1291290Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_114'\\r\\n[2019-12-11T04:58:37.805809][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_113', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_114', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:58:39.757541][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T04:58:39.914039][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T04:58:42.4634132Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_115'\\r\\n[2019-12-11T04:58:42.4649923Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_116'\\r\\n[2019-12-11T04:58:42.9089114Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_115'\\r\\n[2019-12-11T04:58:42.9597103Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_116'\\r\\n[2019-12-11T04:59:07.721000][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_113', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_114', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_115', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_116', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:59:37.918381][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_113, best experiment metric: 0.0016678661300334654, job's best metric: 0.007133723656970872\\r\\n[2019-12-11T04:59:38.016991][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T04:59:38.016897][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_114', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_115', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_116', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T04:59:43.6752713Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_113'\\r\\n[2019-12-11T04:59:43.9685307Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_113', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:00:07.812229][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_116', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:00:09.777836][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:00:09.887131][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:00:14.5436211Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_117'\\r\\n[2019-12-11T05:00:14.5451872Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_118'\\r\\n[2019-12-11T05:00:15.2937151Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_118'\\r\\n[2019-12-11T05:00:15.2959875Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_117'\\r\\n[2019-12-11T05:00:37.869247][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_115, best experiment metric: 0.0015055120028568342, job's best metric: 0.007040928510125662\\r\\n[2019-12-11T05:00:37.980425][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_117', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_118', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:00:37.980526][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:00:45.6139652Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_115'\\r\\n[2019-12-11T05:00:45.9724323Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_115', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:01:07.967393][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_116, best experiment metric: 0.0016233322832329537, job's best metric: 0.03247675533597945\\r\\n[2019-12-11T05:01:08.103140][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_117', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_118', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:01:08.103227][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:01:09.828381][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:01:09.959351][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:01:16.4080113Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_116'\\r\\n[2019-12-11T05:01:16.7702030Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_116', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:01:16.8552427Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_119'\\r\\n[2019-12-11T05:01:17.1895809Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_119'\\r\\n[2019-12-11T05:01:37.872436][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_117, best experiment metric: 0.0016233322832329537, job's best metric: 0.02016178781143776\\r\\n[2019-12-11T05:01:38.027157][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_118, best experiment metric: 0.0015388208275550653, job's best metric: 0.004170841513716161\\r\\n[2019-12-11T05:01:38.137682][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_119', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:01:38.137769][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:01:39.781502][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:01:40.015319][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:01:47.6191670Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_117'\\r\\n[2019-12-11T05:01:47.6200428Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_118'\\r\\n[2019-12-11T05:01:48.0458950Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_117', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_118', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:01:48.1565674Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_120'\\r\\n[2019-12-11T05:01:48.6080892Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_120'\\r\\n[2019-12-11T05:02:07.872883][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_119', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_120', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:02:09.842468][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:02:09.951788][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:02:18.9406059Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_121'\\r\\n[2019-12-11T05:02:18.9444285Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_122'\\r\\n[2019-12-11T05:02:19.4942221Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_121'\\r\\n[2019-12-11T05:02:19.5619784Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_122'\\r\\n[2019-12-11T05:02:37.755951][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_119, best experiment metric: 0.0016307780971795652, job's best metric: 0.004855388943285589\\r\\n[2019-12-11T05:02:37.955796][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_120', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_121', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_122', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:02:37.955890][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:02:49.9909344Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_119'\\r\\n[2019-12-11T05:02:50.3974330Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_119', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:03:08.189756][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_120', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_121', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_122', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:03:11.748869][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:03:11.866035][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:03:20.8421236Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_123'\\r\\n[2019-12-11T05:03:21.3527186Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_123'\\r\\n[2019-12-11T05:03:37.806561][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_120, best experiment metric: 0.0016678661300334654, job's best metric: 0.05216801623500346\\r\\n[2019-12-11T05:03:37.912224][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_121', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_122', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_123', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:03:37.912324][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:03:51.7016848Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_120'\\r\\n[2019-12-11T05:03:52.1433635Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_120', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:04:07.810573][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_123', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:04:11.800607][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:04:11.919314][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:04:22.5689034Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_125'\\r\\n[2019-12-11T05:04:22.5674042Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_124'\\r\\n[2019-12-11T05:04:23.2141068Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_124'\\r\\n[2019-12-11T05:04:23.2362764Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_125'\\r\\n[2019-12-11T05:04:37.868014][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_122, best experiment metric: 0.0016233322832329537, job's best metric: 0.05749912596984914\\r\\n[2019-12-11T05:04:37.967753][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_123, best experiment metric: 0.0016307780971795652, job's best metric: 0.04096472266670282\\r\\n[2019-12-11T05:04:38.073766][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:04:38.073680][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_124', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_125', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:04:53.6056223Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_122'\\r\\n[2019-12-11T05:04:53.6085642Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_123'\\r\\n[2019-12-11T05:04:53.9983787Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_122', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_123', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:05:07.767538][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_124', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_125', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:05:11.655695][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:05:11.781895][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:05:24.3936025Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_126'\\r\\n[2019-12-11T05:05:24.3953863Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_127'\\r\\n[2019-12-11T05:05:24.7890103Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_127'\\r\\n[2019-12-11T05:05:26.0644674Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_126'\\r\\n[2019-12-11T05:05:44.529636][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_124', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_125', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_126', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_127', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:06:11.678224][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:06:11.886603][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:06:14.792348][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_125, best experiment metric: 0.0017221678624627353, job's best metric: 0.07293972862572135\\r\\n[2019-12-11T05:06:14.904532][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_126', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_127', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:06:14.904621][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:06:26.7053362Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_125'\\r\\n[2019-12-11T05:06:27.7325475Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_125', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:06:27.7961869Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_128'\\r\\n[2019-12-11T05:06:28.1702694Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_128'\\r\\n[2019-12-11T05:06:41.796494][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:06:41.948557][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:06:44.851651][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_127, best experiment metric: 0.0017221678624627353, job's best metric: 0.02246595649272443\\r\\n[2019-12-11T05:06:44.952248][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:06:44.952150][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_128', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:06:58.5381596Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_129'\\r\\n[2019-12-11T05:06:59.0570711Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_129'\\r\\n[2019-12-11T05:07:11.773278][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:07:11.884595][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:07:14.773810][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_128', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_129', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:07:29.4517080Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_130'\\r\\n[2019-12-11T05:07:30.0310200Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_130'\\r\\n[2019-12-11T05:07:41.711599][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:07:41.818871][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:07:44.832698][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_129', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_130', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:08:00.3660661Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_131'\\r\\n[2019-12-11T05:08:00.8424658Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_131'\\r\\n[2019-12-11T05:08:18.314970][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_128, best experiment metric: 0.0015388208275550653, job's best metric: 0.05999990603614398\\r\\n[2019-12-11T05:08:18.478308][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_129, best experiment metric: 0.001583848154992595, job's best metric: 0.0033847117545302984\\r\\n[2019-12-11T05:08:18.587447][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:08:18.587350][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_130', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_131', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:08:31.2375371Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_128'\\r\\n[2019-12-11T05:08:31.2387831Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_129'\\r\\n[2019-12-11T05:08:31.7739849Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_128', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_129', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:08:41.773602][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:08:41.991701][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:08:48.728235][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_130, best experiment metric: 0.0017221678624627353, job's best metric: 0.005165872252362477\\r\\n[2019-12-11T05:08:48.838376][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_131', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:08:48.838463][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:09:02.2739852Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_130'\\r\\n[2019-12-11T05:09:02.7458974Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_130', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:09:02.8106750Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_132'\\r\\n[2019-12-11T05:09:02.8131778Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_133'\\r\\n[2019-12-11T05:09:03.2144141Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_133'\\r\\n[2019-12-11T05:09:03.3591617Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_132'\\r\\n[2019-12-11T05:09:11.899695][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:09:12.159785][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:09:18.735780][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_131, best experiment metric: 0.0017221678624627353, job's best metric: 0.041061128899031026\\r\\n[2019-12-11T05:09:18.837443][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_132', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_133', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:09:18.837530][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:09:33.7198889Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_131'\\r\\n[2019-12-11T05:09:34.5836269Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_131', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:09:34.6576244Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_134'\\r\\n[2019-12-11T05:09:35.0617667Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_134'\\r\\n[2019-12-11T05:09:48.874894][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_132', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_133', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_134', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:10:12.207887][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:10:12.325030][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:10:19.090693][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_132', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_134', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:10:35.7461303Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_135'\\r\\n[2019-12-11T05:10:36.6041281Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_135'\\r\\n[2019-12-11T05:10:48.782199][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_132, best experiment metric: 0.0017792358081197479, job's best metric: 0.04791851700419158\\r\\n[2019-12-11T05:10:48.885071][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_133, best experiment metric: 0.0017792358081197479, job's best metric: 0.006275257850762994\\r\\n[2019-12-11T05:10:49.642069][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:10:49.641984][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_135', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:11:06.9930400Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_133'\\r\\n[2019-12-11T05:11:06.9922408Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_132'\\r\\n[2019-12-11T05:11:07.5191794Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_132', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_133', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:11:11.824905][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:11:11.941167][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:11:19.812091][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_135', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:11:37.9865876Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_136'\\r\\n[2019-12-11T05:11:38.5792498Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_136'\\r\\n[2019-12-11T05:11:41.770202][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:11:41.899332][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:11:49.806776][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_135', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_136', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:12:08.9363849Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_137'\\r\\n[2019-12-11T05:12:08.9379924Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_138'\\r\\n[2019-12-11T05:12:09.3995603Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_138'\\r\\n[2019-12-11T05:12:09.4196531Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_137'\\r\\n[2019-12-11T05:12:20.486031][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_136', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_137', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_138', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:12:49.965315][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_136', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_137', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_138', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:13:11.791407][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:13:11.923151][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:13:19.828291][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_136, best experiment metric: 0.0017792358081197479, job's best metric: 0.012445193638231787\\r\\n[2019-12-11T05:13:19.947851][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_137', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_138', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:13:19.947939][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:13:40.7370805Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_136'\\r\\n[2019-12-11T05:13:41.2195454Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_136', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:13:41.2844048Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_139'\\r\\n[2019-12-11T05:13:41.6981712Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_139'\\r\\n[2019-12-11T05:13:49.871579][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_137, best experiment metric: 0.0017792358081197479, job's best metric: 0.002341174482790643\\r\\n[2019-12-11T05:13:49.975581][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_138', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:13:49.975666][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:14:12.0802381Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_137'\\r\\n[2019-12-11T05:14:11.872984][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:14:12.175855][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:14:12.5642250Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_137', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:14:19.803848][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_139', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:14:41.782807][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:14:41.923404][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:14:43.0443314Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_140'\\r\\n[2019-12-11T05:14:43.0472697Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_142'\\r\\n[2019-12-11T05:14:43.0458600Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_141'\\r\\n[2019-12-11T05:14:43.5323103Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_142'\\r\\n[2019-12-11T05:14:43.5753112Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_140'\\r\\n[2019-12-11T05:14:43.5976970Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_141'\\r\\n[2019-12-11T05:14:49.826271][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_139', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:15:20.330921][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_140', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_141', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_142', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:15:41.913894][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:15:42.037790][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:15:44.3910134Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_143'\\r\\n[2019-12-11T05:15:44.9302719Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_143'\\r\\n[2019-12-11T05:15:50.933098][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_140', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_141', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_142', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:16:11.914764][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:16:12.048618][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:16:15.3623122Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_144'\\r\\n[2019-12-11T05:16:15.3639884Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_145'\\r\\n[2019-12-11T05:16:15.7395404Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_145'\\r\\n[2019-12-11T05:16:15.7910279Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_144'\\r\\n[2019-12-11T05:16:20.762150][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_141', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_143', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:16:41.886309][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:16:42.002517][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:16:46.2402394Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_146'\\r\\n[2019-12-11T05:16:47.7816239Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_146'\\r\\n[2019-12-11T05:16:50.895134][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_143', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_144', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_145', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_146', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:17:20.968899][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_144', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_145', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_146', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:17:45.014276][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:17:45.129894][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:17:48.5122830Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_147'\\r\\n[2019-12-11T05:17:48.5137987Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_148'\\r\\n[2019-12-11T05:17:49.2385437Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_147'\\r\\n[2019-12-11T05:17:49.2363273Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_148'\\r\\n[2019-12-11T05:17:50.809612][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_145, best experiment metric: 0.0017221678624627353, job's best metric: 0.009407243066241038\\r\\n[2019-12-11T05:17:50.934253][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:17:50.934158][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_146', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_147', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_148', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:18:14.935474][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:18:15.089736][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:18:19.6307083Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_149'\\r\\n[2019-12-11T05:18:20.3494826Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_149'\\r\\n[2019-12-11T05:18:20.915759][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_147', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_148', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_149', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:18:45.169651][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:18:45.298571][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:18:50.7566177Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_150'\\r\\n[2019-12-11T05:18:50.855677][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_147', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_148', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_149', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:18:51.2615771Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_150'\\r\\n[2019-12-11T05:19:15.792642][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:19:15.924443][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:19:21.036824][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_147', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_149', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_150', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:19:21.7130540Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_151'\\r\\n[2019-12-11T05:19:22.2584248Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_151'\\r\\n[2019-12-11T05:19:51.258126][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_147, best experiment metric: 0.0017221678624627353, job's best metric: 0.0069658943009861815\\r\\n[2019-12-11T05:19:51.388065][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_150', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_151', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:19:51.388197][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:19:52.7768326Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_147'\\r\\n[2019-12-11T05:19:53.4514337Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_147', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:20:15.747560][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:20:15.863278][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:20:21.799856][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_149, best experiment metric: 0.0017792358081197479, job's best metric: 0.0065560162444401136\\r\\n[2019-12-11T05:20:21.907619][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_151', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:20:21.907711][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:20:24.1080745Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_149'\\r\\n[2019-12-11T05:20:24.5288132Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_149', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:20:24.6009156Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_152'\\r\\n[2019-12-11T05:20:24.9952750Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_152'\\r\\n[2019-12-11T05:20:45.734302][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:20:45.901323][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:20:52.384865][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_152', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:20:55.4164707Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_153'\\r\\n[2019-12-11T05:20:55.9506756Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_153'\\r\\n[2019-12-11T05:21:22.035299][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:21:22.170716][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:21:22.978626][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_152', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_153', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:21:26.6726569Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_154'\\r\\n[2019-12-11T05:21:26.6741745Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_155'\\r\\n[2019-12-11T05:21:27.1590579Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_154'\\r\\n[2019-12-11T05:21:27.1714997Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_155'\\r\\n[2019-12-11T05:21:52.976721][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_152, best experiment metric: 0.0016678661300334654, job's best metric: 0.002901176616506208\\r\\n[2019-12-11T05:21:53.080060][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:21:53.079969][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_153', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_154', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_155', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:21:57.5733196Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_152'\\r\\n[2019-12-11T05:21:58.1457016Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_152', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:22:22.842430][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:22:22.986393][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:22:23.429499][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_153, best experiment metric: 0.0016678661300334654, job's best metric: 0.0739042383068324\\r\\n[2019-12-11T05:22:23.545257][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:22:23.545161][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_154', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_155', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:22:28.6044472Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_156'\\r\\n[2019-12-11T05:22:29.1031623Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_156'\\r\\n[2019-12-11T05:22:52.719193][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:22:52.841395][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:22:54.271559][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_154', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_156', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:22:59.5461497Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_157'\\r\\n[2019-12-11T05:23:00.0916164Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_157'\\r\\n[2019-12-11T05:23:24.900604][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_155, best experiment metric: 0.001583848154992595, job's best metric: 0.0031708006639687455\\r\\n[2019-12-11T05:23:25.183297][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:23:25.183208][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_156', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_157', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:23:30.5456544Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_155'\\r\\n[2019-12-11T05:23:30.9194049Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_155', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:23:52.835836][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2019-12-11T05:23:52.955043][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:23:54.859637][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_157', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:24:01.6263972Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_158'\\r\\n[2019-12-11T05:24:01.6298860Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_160'\\r\\n[2019-12-11T05:24:01.6280272Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_159'\\r\\n[2019-12-11T05:24:02.3080681Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_160'\\r\\n[2019-12-11T05:24:02.3176002Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_158'\\r\\n[2019-12-11T05:24:02.4248617Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_159'\\r\\n[2019-12-11T05:24:25.046769][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_157, best experiment metric: 0.0018560787698855016, job's best metric: 0.1152883650525642\\r\\n[2019-12-11T05:24:25.260579][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_158', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_159', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_160', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:24:25.260670][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:24:55.761841][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:24:55.882611][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:24:56.072259][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_158', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_159', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_160', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:25:03.3784789Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_161'\\r\\n[2019-12-11T05:25:04.0431816Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_161'\\r\\n[2019-12-11T05:25:26.008699][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_159', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_160', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_161', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:25:56.133738][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_158, best experiment metric: 0.0016307780971795652, job's best metric: 0.0019279904700636428\\r\\n[2019-12-11T05:25:56.246964][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_160, best experiment metric: 0.0017221678624627353, job's best metric: 0.018245631696892448\\r\\n[2019-12-11T05:25:56.356069][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_161', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:25:56.356156][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:26:05.1957793Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_158'\\r\\n[2019-12-11T05:26:05.8697647Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_158', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:26:26.513509][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2019-12-11T05:26:26.640242][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:26:36.4056429Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_162'\\r\\n[2019-12-11T05:26:36.4071262Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_163'\\r\\n[2019-12-11T05:26:36.4085608Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_164'\\r\\n[2019-12-11T05:26:36.8757674Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_162'\\r\\n[2019-12-11T05:26:36.8958421Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_164'\\r\\n[2019-12-11T05:26:36.9413244Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_163'\\r\\n[2019-12-11T05:26:56.871037][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_161, best experiment metric: 0.0016307780971795652, job's best metric: 0.048746973447536944\\r\\n[2019-12-11T05:26:56.974693][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_162', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_163', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_164', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:26:56.974779][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:27:26.796979][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:27:26.925917][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_162', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_163', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_164', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:27:27.013056][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:27:37.7429948Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_165'\\r\\n[2019-12-11T05:27:38.1390117Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_165'\\r\\n[2019-12-11T05:27:56.996654][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_162', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_163', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_165', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:28:26.890945][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:28:26.969508][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_162, best experiment metric: 0.0017792358081197479, job's best metric: 0.004857694112147825\\r\\n[2019-12-11T05:28:27.016355][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:28:27.078573][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_165', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:28:27.078662][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:28:39.0362632Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_166'\\r\\n[2019-12-11T05:28:39.0378914Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_167'\\r\\n[2019-12-11T05:28:39.6151845Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_167'\\r\\n[2019-12-11T05:28:39.6134398Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_166'\\r\\n[2019-12-11T05:28:56.897691][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:28:57.019482][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:28:58.804689][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_166', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_167', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:29:10.0816631Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_168'\\r\\n[2019-12-11T05:29:10.5522310Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_168'\\r\\n[2019-12-11T05:29:29.023584][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_165, best experiment metric: 0.0017221678624627353, job's best metric: 0.05646897826686734\\r\\n[2019-12-11T05:29:29.136299][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_166', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_167', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_168', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:29:29.136394][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:29:41.0082386Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_165'\\r\\n[2019-12-11T05:29:41.4337868Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_165', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:29:57.074219][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:29:57.201469][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:29:58.877257][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_166', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_167', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_168', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:30:11.9368933Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_169'\\r\\n[2019-12-11T05:30:12.4099648Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_169'\\r\\n[2019-12-11T05:30:29.012583][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_167, best experiment metric: 0.0018560787698855016, job's best metric: 0.006642199399027997\\r\\n[2019-12-11T05:30:29.123143][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_168', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_169', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:30:29.123237][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:30:42.8459143Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_167'\\r\\n[2019-12-11T05:30:43.5614277Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_167', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:30:56.815403][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:30:56.949411][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:30:58.944923][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_166, best experiment metric: 0.0016233322832329537, job's best metric: 0.0031886618354284055\\r\\n[2019-12-11T05:30:59.058390][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_168, best experiment metric: 0.0017792358081197479, job's best metric: 0.002675530043707672\\r\\n[2019-12-11T05:30:59.205187][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:30:59.205100][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_169', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:31:14.0606915Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_168'\\r\\n[2019-12-11T05:31:15.3816719Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_168', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:31:15.4589094Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_170'\\r\\n[2019-12-11T05:31:15.9229256Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_170'\\r\\n[2019-12-11T05:31:26.814786][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:31:26.957054][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:31:29.932560][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_170', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:31:46.3458882Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_171'\\r\\n[2019-12-11T05:31:46.3473657Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_172'\\r\\n[2019-12-11T05:31:46.8537844Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_172'\\r\\n[2019-12-11T05:31:47.1692636Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_171'\\r\\n[2019-12-11T05:32:00.228708][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:32:00.361176][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:32:00.476852][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_170', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_171', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_172', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:32:17.6261052Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_173'\\r\\n[2019-12-11T05:32:18.1941910Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_173'\\r\\n[2019-12-11T05:32:30.944548][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_171', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_172', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_173', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:33:00.911470][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_171', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_172', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_173', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:33:03.789919][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:33:03.906362][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:33:19.1951774Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_174'\\r\\n[2019-12-11T05:33:19.8215590Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_174'\\r\\n[2019-12-11T05:33:31.240973][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_172', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_173', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:34:01.844997][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_171, best experiment metric: 0.0016307780971795652, job's best metric: 0.004737953442852887\\r\\n[2019-12-11T05:34:02.340356][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:34:02.340260][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_174', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:34:03.838033][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:34:03.962475][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:34:20.8113602Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_175'\\r\\n[2019-12-11T05:34:20.8130152Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_176'\\r\\n[2019-12-11T05:34:21.3117377Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_175'\\r\\n[2019-12-11T05:34:21.3389701Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_176'\\r\\n[2019-12-11T05:34:32.836012][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_175', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_176', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:34:36.912338][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:34:37.038772][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:34:51.8236360Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_177'\\r\\n[2019-12-11T05:34:52.3456156Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_177'\\r\\n[2019-12-11T05:35:02.926081][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_174, best experiment metric: 0.001497901016791068, job's best metric: 0.0031945878026022815\\r\\n[2019-12-11T05:35:03.033151][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_175', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_176', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:35:03.033259][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:35:22.7373079Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_174'\\r\\n[2019-12-11T05:35:24.0171926Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_174', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:35:32.914306][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_174, best experiment metric: 0.001497901016791068, job's best metric: 0.0030666305601945985\\r\\n[2019-12-11T05:35:33.045687][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_175', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_176', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_177', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:35:33.045777][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:36:03.034388][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_175, best experiment metric: 0.0016233322832329537, job's best metric: 0.0729206776049051\\r\\n[2019-12-11T05:36:03.188987][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:36:03.188872][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_177', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:36:06.812626][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:36:07.286786][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:36:25.0358888Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_178'\\r\\n[2019-12-11T05:36:25.0378281Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_179'\\r\\n[2019-12-11T05:36:25.4602405Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_179'\\r\\n[2019-12-11T05:36:25.5134988Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_178'\\r\\n[2019-12-11T05:36:34.038064][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_177, best experiment metric: 0.001583848154992595, job's best metric: 0.006164894040280515\\r\\n[2019-12-11T05:36:34.263237][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:36:37.854848][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:36:37.981618][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:36:56.0262029Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_177'\\r\\n[2019-12-11T05:36:56.4218708Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_177', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:36:56.4858769Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_180'\\r\\n[2019-12-11T05:36:56.8642523Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_180'\\r\\n[2019-12-11T05:37:05.012144][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_178', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_179', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:37:07.836613][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:37:08.056561][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:37:27.3294006Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_181'\\r\\n[2019-12-11T05:37:27.8684431Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_181'\\r\\n[2019-12-11T05:37:34.809597][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_179', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_180', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:38:04.832690][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_178, best experiment metric: 0.0016678661300334654, job's best metric: 0.003471170166200696\\r\\n[2019-12-11T05:38:04.952642][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:38:04.952536][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_180', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_181', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:38:28.7336652Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_178'\\r\\n[2019-12-11T05:38:29.1152212Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_178', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:38:35.401716][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_178, best experiment metric: 0.0014990011809775162, job's best metric: 0.0031390089695007253\\r\\n[2019-12-11T05:38:35.531397][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_181', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:38:35.531491][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:38:37.855930][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2019-12-11T05:38:37.975249][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:38:59.7087763Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_182'\\r\\n[2019-12-11T05:38:59.7146948Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_184'\\r\\n[2019-12-11T05:38:59.7105046Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_183'\\r\\n[2019-12-11T05:39:00.1907336Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_182'\\r\\n[2019-12-11T05:39:00.2203575Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_184'\\r\\n[2019-12-11T05:39:00.2897302Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_183'\\r\\n[2019-12-11T05:39:08.054293][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:39:08.177542][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:39:31.1344768Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_185'\\r\\n[2019-12-11T05:39:32.0213752Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_185'\\r\\n[2019-12-11T05:39:40.830779][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_182', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_183', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_184', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:40:14.408477][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_182', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_183', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_184', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_185', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:40:40.892143][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:40:41.055973][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:40:45.346836][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_183', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_185', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:41:03.4364930Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_186'\\r\\n[2019-12-11T05:41:04.0123952Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_186'\\r\\n[2019-12-11T05:41:10.780637][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:41:10.915118][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:41:34.4589235Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_187'\\r\\n[2019-12-11T05:41:35.1840977Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_187'\\r\\n[2019-12-11T05:41:41.029240][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:41:41.253753][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:41:45.132215][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_186', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:42:05.5981319Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_189'\\r\\n[2019-12-11T05:42:05.5939833Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_188'\\r\\n[2019-12-11T05:42:06.2223978Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_189'\\r\\n[2019-12-11T05:42:06.2452291Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_188'\\r\\n[2019-12-11T05:42:15.354378][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_187', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:42:41.279548][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:42:41.395301][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:42:45.957205][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_187', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_188', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_189', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:43:07.2273075Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_190'\\r\\n[2019-12-11T05:43:07.7427926Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_190'\\r\\n[2019-12-11T05:43:15.840952][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_187, best experiment metric: 0.0016307780971795652, job's best metric: 0.008205417659533927\\r\\n[2019-12-11T05:43:15.970726][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_188', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_189', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:43:15.970812][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:43:42.029546][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:43:42.183781][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:43:48.890159][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_190', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:43:48.890243][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:43:48.776349][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_188, best experiment metric: 0.0016678661300334654, job's best metric: 0.002032715484814456\\r\\n[2019-12-11T05:44:08.8385496Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_191'\\r\\n[2019-12-11T05:44:09.6003882Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_191'\\r\\n[2019-12-11T05:44:12.025107][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:44:12.177563][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:44:18.950381][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_189, best experiment metric: 0.0016307780971795652, job's best metric: 0.006261224880777813\\r\\n[2019-12-11T05:44:19.070152][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:44:40.0617991Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_189'\\r\\n[2019-12-11T05:44:40.5152805Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_189', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:44:40.5995290Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_192'\\r\\n[2019-12-11T05:44:41.0082973Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_192'\\r\\n[2019-12-11T05:44:48.990654][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_189, best experiment metric: 0.0014990011809775162, job's best metric: 0.005533867344771175\\r\\n[2019-12-11T05:44:49.107556][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_190, best experiment metric: 0.0015388208275550653, job's best metric: 0.0030320507628377306\\r\\n[2019-12-11T05:44:49.258372][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:44:49.258283][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_191', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:45:14.805028][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:45:14.930739][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:45:20.011174][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_192', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:45:42.0113265Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_194'\\r\\n[2019-12-11T05:45:42.0087801Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_193'\\r\\n[2019-12-11T05:45:42.4196557Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_194'\\r\\n[2019-12-11T05:45:42.5093184Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_193'\\r\\n[2019-12-11T05:45:44.864512][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:45:45.010345][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:45:49.852613][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_192', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:46:12.9578641Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_195'\\r\\n[2019-12-11T05:46:13.7811088Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_195'\\r\\n[2019-12-11T05:46:20.026665][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_192, best experiment metric: 0.0016307780971795652, job's best metric: 0.0024401900726590733\\r\\n[2019-12-11T05:46:20.133595][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:46:20.133449][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_193', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_194', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:46:44.866674][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:46:45.013465][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:46:49.933170][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_193', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_194', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_195', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:47:14.6208066Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_196'\\r\\n[2019-12-11T05:47:15.7447920Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_196'\\r\\n[2019-12-11T05:47:19.976030][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_195', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:47:44.884995][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:47:45.022108][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:47:46.1888518Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_197'\\r\\n[2019-12-11T05:47:46.6998886Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_197'\\r\\n[2019-12-11T05:47:53.438453][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_194, best experiment metric: 0.0016233322832329537, job's best metric: 0.028750095594940267\\r\\n[2019-12-11T05:47:53.574631][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_195, best experiment metric: 0.0016678661300334654, job's best metric: 0.005166862151310963\\r\\n[2019-12-11T05:47:53.691489][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:47:53.691397][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_196', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:48:14.968548][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:48:15.102966][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:48:17.1874672Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_194'\\r\\n[2019-12-11T05:48:17.7898415Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_194', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:48:17.8894083Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_198'\\r\\n[2019-12-11T05:48:18.2511938Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_198'\\r\\n[2019-12-11T05:48:24.067206][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_196', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_197', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:48:44.964823][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:48:45.084274][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:48:48.7233437Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_199'\\r\\n[2019-12-11T05:48:49.3836659Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_199'\\r\\n[2019-12-11T05:48:54.017339][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_196, best experiment metric: 0.0018560787698855016, job's best metric: 0.028470724280189617\\r\\n[2019-12-11T05:48:54.129243][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_197', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_198', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:48:54.129332][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:49:15.193103][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:49:15.344580][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:49:19.8565038Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_196'\\r\\n[2019-12-11T05:49:20.3660622Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_196', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:49:20.4389480Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_200'\\r\\n[2019-12-11T05:49:20.9574331Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_200'\\r\\n[2019-12-11T05:49:29.612065][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_198, best experiment metric: 0.0016233322832329537, job's best metric: 0.002782971033740508\\r\\n[2019-12-11T05:49:29.725613][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_199', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:49:29.725698][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:49:44.978192][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:49:45.110595][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:49:51.4169532Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_198'\\r\\n[2019-12-11T05:49:52.7092489Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_198', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:49:52.7728765Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_201'\\r\\n[2019-12-11T05:49:53.1483680Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_201'\\r\\n[2019-12-11T05:50:00.326295][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_200', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:50:19.180801][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:50:19.307094][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:50:23.6529563Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_202'\\r\\n[2019-12-11T05:50:24.2462923Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_202'\\r\\n[2019-12-11T05:50:31.100931][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_199, best experiment metric: 0.0015388208275550653, job's best metric: 0.001999847658530887\\r\\n[2019-12-11T05:50:31.234669][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_200, best experiment metric: 0.001583848154992595, job's best metric: 0.005213882934108476\\r\\n[2019-12-11T05:50:31.366651][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:50:31.366559][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_201', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:50:52.736498][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:50:52.862408][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:50:54.7401794Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_199'\\r\\n[2019-12-11T05:50:55.1936917Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_199', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:50:55.2563495Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_203'\\r\\n[2019-12-11T05:50:55.6256831Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_203'\\r\\n[2019-12-11T05:51:02.157893][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_199, best experiment metric: 0.001497901016791068, job's best metric: 0.0019560782659801533\\r\\n[2019-12-11T05:51:02.285766][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_201, best experiment metric: 0.0017792358081197479, job's best metric: 0.05959429365346063\\r\\n[2019-12-11T05:51:02.462690][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_202', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:51:02.462786][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:51:22.976830][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:51:23.200559][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:51:26.1000504Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_201'\\r\\n[2019-12-11T05:51:26.5142951Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_201', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:51:26.5824768Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_204'\\r\\n[2019-12-11T05:51:27.0715143Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_204'\\r\\n[2019-12-11T05:51:33.085897][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_201, best experiment metric: 0.0015055120028568342, job's best metric: 0.05957448892037738\\r\\n[2019-12-11T05:51:33.244452][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:51:33.244364][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_203', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:51:53.932457][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:51:54.050682][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:51:57.5917894Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_205'\\r\\n[2019-12-11T05:51:58.1624982Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_205'\\r\\n[2019-12-11T05:52:03.946640][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_202, best experiment metric: 0.0014990011809775162, job's best metric: 0.005539218652545117\\r\\n[2019-12-11T05:52:04.052226][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_203, best experiment metric: 0.0018560787698855016, job's best metric: 0.0025928503081884486\\r\\n[2019-12-11T05:52:04.163541][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:52:04.163433][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_204', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:52:28.7029147Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_202'\\r\\n[2019-12-11T05:52:28.7041169Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_203'\\r\\n[2019-12-11T05:52:29.1630987Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_202', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_203', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:52:35.167184][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_202, best experiment metric: 0.001497901016791068, job's best metric: 0.00515807645077202\\r\\n[2019-12-11T05:52:35.281430][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_203, best experiment metric: 0.0015603774933573918, job's best metric: 0.0022097761814799557\\r\\n[2019-12-11T05:52:35.389923][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_205', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:52:35.390009][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T05:52:53.962765][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:52:54.091673][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:52:59.7526189Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_206'\\r\\n[2019-12-11T05:52:59.7540871Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_207'\\r\\n[2019-12-11T05:53:00.3385054Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_206'\\r\\n[2019-12-11T05:53:00.3299468Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_207'\\r\\n[2019-12-11T05:53:06.245199][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_204, best experiment metric: 0.001583848154992595, job's best metric: 0.007334340867345573\\r\\n[2019-12-11T05:53:06.487865][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:53:36.927720][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_205, best experiment metric: 0.001583848154992595, job's best metric: 0.060190684545977195\\r\\n[2019-12-11T05:53:37.190195][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:53:37.190095][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_206', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_207', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:53:53.970424][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:53:54.125052][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:54:01.5317876Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_205'\\r\\n[2019-12-11T05:54:01.8878020Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_205', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:54:02.0043394Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_208'\\r\\n[2019-12-11T05:54:02.4328368Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_208'\\r\\n[2019-12-11T05:54:07.859060][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_205, best experiment metric: 0.001583848154992595, job's best metric: 0.060190684545977195\\r\\n[2019-12-11T05:54:07.984716][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_206', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_207', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:54:07.984992][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:54:23.954778][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:54:24.081902][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:54:32.9263263Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_209'\\r\\n[2019-12-11T05:54:33.4517029Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_209'\\r\\n[2019-12-11T05:54:37.962315][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_208', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:55:08.639643][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_207, best experiment metric: 0.001583848154992595, job's best metric: 0.0069806272609865124\\r\\n[2019-12-11T05:55:08.765345][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:55:08.765261][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_208', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_209', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:55:23.949429][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:55:24.171321][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:55:34.5276116Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_210'\\r\\n[2019-12-11T05:55:34.5292603Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_211'\\r\\n[2019-12-11T05:55:35.0026674Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_210'\\r\\n[2019-12-11T05:55:35.1178352Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_211'\\r\\n[2019-12-11T05:55:38.990173][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_208, best experiment metric: 0.0017792358081197479, job's best metric: 0.0061561150149760996\\r\\n[2019-12-11T05:55:39.114940][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_209', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_210', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_211', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:55:39.115033][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:55:53.999601][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:55:54.143381][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:56:05.5968783Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_212'\\r\\n[2019-12-11T05:56:06.2078259Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_212'\\r\\n[2019-12-11T05:56:08.863420][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_209, best experiment metric: 0.0016678661300334654, job's best metric: 0.00809041505738806\\r\\n[2019-12-11T05:56:08.997586][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_210', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_211', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_212', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:56:08.997670][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:56:24.155797][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:56:24.329881][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:56:36.7228477Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_213'\\r\\n[2019-12-11T05:56:37.6521417Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_213'\\r\\n[2019-12-11T05:56:39.159430][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_210', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_211', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_212', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_213', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:57:08.975909][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_210, best experiment metric: 0.0018560787698855016, job's best metric: 0.0038953756311919407\\r\\n[2019-12-11T05:57:09.086669][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_212', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_213', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:57:09.086759][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:57:24.818792][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:57:25.010326][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:57:38.6416702Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_210'\\r\\n[2019-12-11T05:57:39.0459810Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_210', previous status = 'RUNNING')]\\r\\n[2019-12-11T05:57:39.079550][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_210, best experiment metric: 0.001523607883647456, job's best metric: 0.0034180191878720144\\r\\n[2019-12-11T05:57:39.1202804Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_214'\\r\\n[2019-12-11T05:57:39.192383][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_213', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:57:39.192503][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:57:39.5110841Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_214'\\r\\n[2019-12-11T05:57:55.244546][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T05:57:55.376442][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:58:09.9906909Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_216'\\r\\n[2019-12-11T05:58:09.9890124Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_215'\\r\\n[2019-12-11T05:58:10.5015223Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_216'\\r\\n[2019-12-11T05:58:10.5463927Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_215'\\r\\n[2019-12-11T05:58:12.727282][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_213, best experiment metric: 0.0016678661300334654, job's best metric: 0.32363284611756865\\r\\n[2019-12-11T05:58:12.846933][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_214', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_215', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_216', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:58:12.847027][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T05:58:25.870729][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:58:25.996854][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:58:41.0534714Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_217'\\r\\n[2019-12-11T05:58:41.6301068Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_217'\\r\\n[2019-12-11T05:58:43.595119][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_214', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_215', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_216', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_217', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:59:14.034232][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_215', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_216', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_217', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:59:28.390713][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:59:28.518911][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T05:59:42.7984045Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_218'\\r\\n[2019-12-11T05:59:43.2612868Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_218'\\r\\n[2019-12-11T05:59:43.907825][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_216', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_217', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_218', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T05:59:58.975731][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T05:59:59.099645][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:00:13.8458545Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_219'\\r\\n[2019-12-11T06:00:14.049497][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_218', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:00:14.5153924Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_219'\\r\\n[2019-12-11T06:00:44.339956][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_216, best experiment metric: 0.0015055120028568342, job's best metric: 0.03149803058726921\\r\\n[2019-12-11T06:00:44.664914][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_218', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_219', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:00:44.665019][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:00:45.0734978Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_216'\\r\\n[2019-12-11T06:00:45.5333076Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_216', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:00:59.029264][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:00:59.180762][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:01:15.054911][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_219', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:01:16.1535893Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_220'\\r\\n[2019-12-11T06:01:16.6917009Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_220'\\r\\n[2019-12-11T06:01:28.940583][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:01:29.160714][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:01:45.203736][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_219, best experiment metric: 0.0017221678624627353, job's best metric: 0.006981396531844718\\r\\n[2019-12-11T06:01:45.310835][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:01:45.310744][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_220', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:01:47.1747796Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_219'\\r\\n[2019-12-11T06:01:47.5427581Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_219', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:01:47.6663077Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_222'\\r\\n[2019-12-11T06:01:47.6640248Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_221'\\r\\n[2019-12-11T06:01:48.1416082Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_221'\\r\\n[2019-12-11T06:01:48.1553872Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_222'\\r\\n[2019-12-11T06:01:59.840640][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:01:59.957399][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:02:16.314199][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_220', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_221', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_222', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:02:18.6674603Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_223'\\r\\n[2019-12-11T06:02:19.1512210Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_223'\\r\\n[2019-12-11T06:02:47.216692][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_221', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_222', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_223', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:03:18.366280][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_223', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:03:48.990426][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_222, best experiment metric: 0.0016678661300334654, job's best metric: 0.006267651193288185\\r\\n[2019-12-11T06:03:49.101120][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_223, best experiment metric: 0.0016307780971795652, job's best metric: 0.13684736390451985\\r\\n[2019-12-11T06:03:49.227810][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T06:03:50.7648796Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_222'\\r\\n[2019-12-11T06:03:51.1350246Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_222', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:03:59.893249][GENERATOR][INFO]Trying to sample '3' jobs from the hyperparameter space\\r\\n[2019-12-11T06:04:00.024940][GENERATOR][INFO]Successfully sampled '3' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:04:21.7492589Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_224'\\r\\n[2019-12-11T06:04:21.7507772Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_225'\\r\\n[2019-12-11T06:04:21.7523451Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_226'\\r\\n[2019-12-11T06:04:22.2543457Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_225'\\r\\n[2019-12-11T06:04:22.3542752Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_224'\\r\\n[2019-12-11T06:04:22.3730455Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_226'\\r\\n[2019-12-11T06:04:24.384268][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_224', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_225', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_226', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:04:29.956150][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:04:30.088238][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:04:52.9109986Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_227'\\r\\n[2019-12-11T06:04:53.5185761Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_227'\\r\\n[2019-12-11T06:04:55.615461][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_224', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_225', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_226', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_227', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:05:26.109743][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_224', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_225', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_226', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_227', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:05:56.082853][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_226', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_227', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:06:00.024636][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:06:00.243595][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:06:25.1311705Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_228'\\r\\n[2019-12-11T06:06:25.8769334Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_228'\\r\\n[2019-12-11T06:06:26.525323][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_224, best experiment metric: 0.0016307780971795652, job's best metric: 0.03115695176485316\\r\\n[2019-12-11T06:06:26.782774][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:06:26.782617][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_227', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:06:57.505234][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_226, best experiment metric: 0.0016678661300334654, job's best metric: 0.22290511482202266\\r\\n[2019-12-11T06:06:57.606085][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_227, best experiment metric: 0.0016678661300334654, job's best metric: 0.040910633645115636\\r\\n[2019-12-11T06:06:57.773598][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_228', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:06:57.773686][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T06:07:00.957830][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:07:01.207408][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:07:26.9268032Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_229'\\r\\n[2019-12-11T06:07:27.4264596Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_229'\\r\\n[2019-12-11T06:07:28.067657][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_228', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_229', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:07:31.970768][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:07:32.207806][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:07:57.9970236Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_231'\\r\\n[2019-12-11T06:07:57.9954454Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_230'\\r\\n[2019-12-11T06:07:58.009525][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_229', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:07:58.4821854Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_231'\\r\\n[2019-12-11T06:07:58.5319109Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_230'\\r\\n[2019-12-11T06:08:27.962959][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_229', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_230', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_231', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:08:32.939028][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:08:33.058427][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:08:58.034920][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_229, best experiment metric: 0.0016307780971795652, job's best metric: 0.0021035334003170567\\r\\n[2019-12-11T06:08:58.179396][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:08:58.179290][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_230', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_231', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:08:59.5149531Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_229'\\r\\n[2019-12-11T06:09:00.3054416Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_229', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:09:00.3831515Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_232'\\r\\n[2019-12-11T06:09:00.8485805Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_232'\\r\\n[2019-12-11T06:09:29.154158][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_230', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_232', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:09:32.992125][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:09:33.108617][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:09:59.113006][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_230, best experiment metric: 0.0018560787698855016, job's best metric: 0.060078401626094756\\r\\n[2019-12-11T06:09:59.231842][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_232', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:09:59.231931][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:10:02.1261605Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_230'\\r\\n[2019-12-11T06:10:02.4056289Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_230', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:10:02.4805597Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_233'\\r\\n[2019-12-11T06:10:02.4822616Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_234'\\r\\n[2019-12-11T06:10:02.9420780Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_234'\\r\\n[2019-12-11T06:10:03.1778765Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_233'\\r\\n[2019-12-11T06:10:30.244921][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_233', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_234', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:10:33.036365][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:10:33.170377][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:10:33.6963706Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_235'\\r\\n[2019-12-11T06:10:34.3939604Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_235'\\r\\n[2019-12-11T06:11:01.833609][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_233', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_234', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_235', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:11:02.916649][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:11:03.039651][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:11:04.9137442Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_236'\\r\\n[2019-12-11T06:11:05.5575694Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_236'\\r\\n[2019-12-11T06:11:31.920764][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_235', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_236', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:12:01.934226][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_236', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:12:03.119800][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:12:03.313307][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:12:06.5543403Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_237'\\r\\n[2019-12-11T06:12:06.5558875Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_238'\\r\\n[2019-12-11T06:12:07.1477650Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_237'\\r\\n[2019-12-11T06:12:07.2667890Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_238'\\r\\n[2019-12-11T06:12:32.076798][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_236', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_237', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_238', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:12:33.934197][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:12:34.072978][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:12:37.7792485Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_239'\\r\\n[2019-12-11T06:12:39.1064350Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_239'\\r\\n[2019-12-11T06:13:02.280431][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_236', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_237', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_238', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_239', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:13:33.781082][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_237', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_238', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_239', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:14:04.066008][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:14:04.132415][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_239', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:14:04.183484][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:14:10.9183340Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_240'\\r\\n[2019-12-11T06:14:10.9199846Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_241'\\r\\n[2019-12-11T06:14:11.5292929Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_240'\\r\\n[2019-12-11T06:14:11.6600947Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_241'\\r\\n[2019-12-11T06:14:33.954914][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_237, best experiment metric: 0.0015055120028568342, job's best metric: 0.0728891564968433\\r\\n[2019-12-11T06:14:34.077825][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:14:34.077730][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_240', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_241', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:14:34.634120][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:14:34.513763][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:14:42.1997591Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_242'\\r\\n[2019-12-11T06:14:42.8317292Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_242'\\r\\n[2019-12-11T06:15:04.253848][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_240', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_241', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_242', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:15:04.946262][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:15:05.145590][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:15:13.3353075Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_243'\\r\\n[2019-12-11T06:15:13.8366647Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_243'\\r\\n[2019-12-11T06:15:35.261431][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_240, best experiment metric: 0.0018560787698855016, job's best metric: 0.0025278293456262027\\r\\n[2019-12-11T06:15:35.372461][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_241', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_242', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_243', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:15:35.372591][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:16:05.075785][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:16:05.199516][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:16:06.410221][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_242', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_243', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:16:14.9240627Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_244'\\r\\n[2019-12-11T06:16:15.5754792Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_244'\\r\\n[2019-12-11T06:16:35.973215][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:16:36.197653][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:16:37.006162][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_241, best experiment metric: 0.001497901016791068, job's best metric: 0.0022062142056388677\\r\\n[2019-12-11T06:16:37.111979][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_244', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:16:37.112064][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:16:46.0894382Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_245'\\r\\n[2019-12-11T06:16:46.6109688Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_245'\\r\\n[2019-12-11T06:17:06.964256][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:17:07.050393][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_244', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_245', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:17:07.093491][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:17:17.2557637Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_247'\\r\\n[2019-12-11T06:17:17.2543053Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_246'\\r\\n[2019-12-11T06:17:18.9606118Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_246'\\r\\n[2019-12-11T06:17:19.0317019Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_247'\\r\\n[2019-12-11T06:17:37.010149][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_244, best experiment metric: 0.0017792358081197479, job's best metric: 0.020202280656515536\\r\\n[2019-12-11T06:17:37.133035][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:17:37.132948][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_245', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_246', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:18:06.975101][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:18:07.202383][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_246', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_247', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:18:07.202694][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:18:20.0200324Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_248'\\r\\n[2019-12-11T06:18:20.6184834Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_248'\\r\\n[2019-12-11T06:18:38.140719][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:18:38.298097][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:18:38.325372][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_247', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_248', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:18:51.1437330Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_249'\\r\\n[2019-12-11T06:18:51.6179813Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_249'\\r\\n[2019-12-11T06:19:09.241847][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_246, best experiment metric: 0.0017221678624627353, job's best metric: 0.052283243412577514\\r\\n[2019-12-11T06:19:09.351230][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_247, best experiment metric: 0.0017792358081197479, job's best metric: 0.019564722760417593\\r\\n[2019-12-11T06:19:09.458881][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T06:19:09.458786][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_248', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_249', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:19:22.2158549Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_246'\\r\\n[2019-12-11T06:19:22.5534914Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_246', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:19:38.998803][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:19:39.154005][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:19:40.153949][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_246, best experiment metric: 0.0016233322832329537, job's best metric: 0.05226935327254411\\r\\n[2019-12-11T06:19:40.258826][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_249', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:19:40.258913][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:19:53.2665993Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_250'\\r\\n[2019-12-11T06:19:53.7254898Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_250'\\r\\n[2019-12-11T06:20:10.074620][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:20:10.213488][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:20:11.185962][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_248, best experiment metric: 0.0016233322832329537, job's best metric: 0.007499181886439728\\r\\n[2019-12-11T06:20:11.305190][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_250', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:20:11.305310][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:20:24.2435050Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_248'\\r\\n[2019-12-11T06:20:24.7402235Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_248', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:20:24.8358701Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_251'\\r\\n[2019-12-11T06:20:25.2624217Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_251'\\r\\n[2019-12-11T06:20:40.065644][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:20:40.269938][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:20:42.485143][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_250', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_251', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:20:55.7861383Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_252'\\r\\n[2019-12-11T06:20:55.7877417Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_253'\\r\\n[2019-12-11T06:20:56.2712733Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_252'\\r\\n[2019-12-11T06:20:56.3284255Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_253'\\r\\n[2019-12-11T06:21:15.467154][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_251', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_252', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_253', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:21:41.108795][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:21:41.240248][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:21:46.238148][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_252', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_253', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:21:57.4340144Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_254'\\r\\n[2019-12-11T06:21:57.8784642Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_254'\\r\\n[2019-12-11T06:22:24.354006][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_251, best experiment metric: 0.001497901016791068, job's best metric: 0.0025879161516872934\\r\\n[2019-12-11T06:22:24.469296][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_253, best experiment metric: 0.0017221678624627353, job's best metric: 0.005068080803721647\\r\\n[2019-12-11T06:22:24.582707][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_254', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:22:24.582866][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T06:22:28.4099393Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_251'\\r\\n[2019-12-11T06:22:28.4110604Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_253'\\r\\n[2019-12-11T06:22:29.0493355Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_251', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_253', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:22:42.219801][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:22:42.396954][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:22:55.273272][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_252, best experiment metric: 0.0015603774933573918, job's best metric: 0.05697366342912485\\r\\n[2019-12-11T06:22:55.368825][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_254, best experiment metric: 0.0018560787698855016, job's best metric: 0.04784902909660946\\r\\n[2019-12-11T06:22:55.509375][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T06:22:59.6585035Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_252'\\r\\n[2019-12-11T06:22:59.6592058Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_254'\\r\\n[2019-12-11T06:23:00.0998269Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_252', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_254', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:23:00.1740027Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_255'\\r\\n[2019-12-11T06:23:00.1754368Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_256'\\r\\n[2019-12-11T06:23:00.5337886Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_255'\\r\\n[2019-12-11T06:23:01.4117670Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_256'\\r\\n[2019-12-11T06:23:13.185644][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:23:13.364316][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:23:26.218808][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_255', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_256', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:23:31.9650508Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_257'\\r\\n[2019-12-11T06:23:31.9670093Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_258'\\r\\n[2019-12-11T06:23:32.4716350Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_258'\\r\\n[2019-12-11T06:23:32.6369507Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_257'\\r\\n[2019-12-11T06:23:57.285171][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_255', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_256', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_257', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_258', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:24:28.239990][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_256', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_257', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_258', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:24:44.567857][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:24:44.715743][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:24:59.241432][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_256, best experiment metric: 0.0017792358081197479, job's best metric: 0.002555068387485862\\r\\n[2019-12-11T06:24:59.360658][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:24:59.360557][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_257', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_258', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:25:04.5394167Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_259'\\r\\n[2019-12-11T06:25:05.2774883Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_259'\\r\\n[2019-12-11T06:25:15.306244][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:25:15.472586][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:25:30.242789][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_257, best experiment metric: 0.0017792358081197479, job's best metric: 0.05697643906609627\\r\\n[2019-12-11T06:25:30.358902][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:25:30.358820][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_259', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:25:35.8700794Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_257'\\r\\n[2019-12-11T06:25:36.3540974Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_257', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:25:36.4411269Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_260'\\r\\n[2019-12-11T06:25:36.8468080Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_260'\\r\\n[2019-12-11T06:25:46.207822][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:25:46.339597][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:26:01.099271][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_260', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:26:07.4227973Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_261'\\r\\n[2019-12-11T06:26:07.4243252Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_262'\\r\\n[2019-12-11T06:26:07.9150864Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_262'\\r\\n[2019-12-11T06:26:08.9701038Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_261'\\r\\n[2019-12-11T06:26:31.119804][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_259, best experiment metric: 0.001497901016791068, job's best metric: 0.0047462862197187\\r\\n[2019-12-11T06:26:31.277016][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:26:31.276921][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_261', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_262', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:26:39.4977292Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_259'\\r\\n[2019-12-11T06:26:39.8982263Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_259', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:26:48.125386][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:26:48.248056][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:27:02.612588][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_261', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_262', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:27:10.6590705Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_263'\\r\\n[2019-12-11T06:27:11.3497752Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_263'\\r\\n[2019-12-11T06:27:33.146895][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_260, best experiment metric: 0.0015055120028568342, job's best metric: 0.007200915501721112\\r\\n[2019-12-11T06:27:33.269359][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_261', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_262', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_263', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:27:33.269467][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:27:42.0207943Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_260'\\r\\n[2019-12-11T06:27:42.4638064Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_260', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:27:49.027941][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:27:49.180749][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:28:04.281831][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_262, best experiment metric: 0.0016233322832329537, job's best metric: 0.007237343163078752\\r\\n[2019-12-11T06:28:04.392309][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:28:04.392215][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_261', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_263', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:28:13.1580947Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_262'\\r\\n[2019-12-11T06:28:13.5410072Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_262', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:28:13.6116823Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_264'\\r\\n[2019-12-11T06:28:13.9795858Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_264'\\r\\n[2019-12-11T06:28:35.429324][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_261', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_263', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_264', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:28:50.054297][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:28:50.184518][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:29:06.111008][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_261, best experiment metric: 0.0018560787698855016, job's best metric: 0.006565558369953958\\r\\n[2019-12-11T06:29:06.227244][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_264', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:29:06.227348][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:29:15.1258903Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_261'\\r\\n[2019-12-11T06:29:15.9451764Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_261', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:29:16.0171460Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_265'\\r\\n[2019-12-11T06:29:16.4162736Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_265'\\r\\n[2019-12-11T06:29:21.001982][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:29:21.129989][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:29:37.283462][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_265', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:29:47.0119705Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_266'\\r\\n[2019-12-11T06:29:47.6047564Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_266'\\r\\n[2019-12-11T06:29:51.119395][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:29:51.245465][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:30:08.725007][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_265', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_266', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:30:18.1817632Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_267'\\r\\n[2019-12-11T06:30:18.1832576Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_268'\\r\\n[2019-12-11T06:30:18.6647096Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_267'\\r\\n[2019-12-11T06:30:19.5125964Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_268'\\r\\n[2019-12-11T06:30:39.356654][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_266', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_267', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_268', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:30:52.386431][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:30:52.647650][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:31:10.391015][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_267', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_268', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:31:20.6382514Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_269'\\r\\n[2019-12-11T06:31:21.8788804Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_269'\\r\\n[2019-12-11T06:31:41.315934][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_267, best experiment metric: 0.0014990011809775162, job's best metric: 0.0016870571097181513\\r\\n[2019-12-11T06:31:41.447058][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:31:41.446969][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_269', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:31:53.071072][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:31:53.198885][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:32:12.540247][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_268, best experiment metric: 0.0015603774933573918, job's best metric: 0.0022766976401005254\\r\\n[2019-12-11T06:32:12.657622][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:32:23.1316585Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_268'\\r\\n[2019-12-11T06:32:23.5514640Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_268', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:32:23.6303846Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_270'\\r\\n[2019-12-11T06:32:23.6320188Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_271'\\r\\n[2019-12-11T06:32:24.1015379Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_270'\\r\\n[2019-12-11T06:32:24.1715450Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_271'\\r\\n[2019-12-11T06:32:43.129183][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_270', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_271', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:32:54.141539][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:32:54.372141][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:33:13.199624][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_270', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_271', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:33:25.2443994Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_272'\\r\\n[2019-12-11T06:33:25.2462161Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_273'\\r\\n[2019-12-11T06:33:25.9910502Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_272'\\r\\n[2019-12-11T06:33:30.9737682Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_273'\\r\\n[2019-12-11T06:33:43.318338][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_270', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_271', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_272', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_273', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:33:55.419189][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:33:55.640555][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:34:01.6338540Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_274'\\r\\n[2019-12-11T06:34:02.1579134Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_274'\\r\\n[2019-12-11T06:34:14.395990][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_272', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_273', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:34:26.220819][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:34:26.362430][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:34:32.7706668Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_275'\\r\\n[2019-12-11T06:34:33.3230208Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_275'\\r\\n[2019-12-11T06:34:45.326092][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_272', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_273', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_274', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:35:16.826817][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_273, best experiment metric: 0.0017792358081197479, job's best metric: 0.0025117065765787603\\r\\n[2019-12-11T06:35:16.503838][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_272, best experiment metric: 0.0017792358081197479, job's best metric: 0.0027059264441495453\\r\\n[2019-12-11T06:35:16.942308][ENFORCER][INFO]Policy cancelled 2 jobs\\r\\n[2019-12-11T06:35:16.941984][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_274', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_275', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:35:34.4564388Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_272'\\r\\n[2019-12-11T06:35:34.4572763Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_273'\\r\\n[2019-12-11T06:35:34.9407515Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_272', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_273', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:35:47.272581][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_274, best experiment metric: 0.0017221678624627353, job's best metric: 0.05258524837391142\\r\\n[2019-12-11T06:35:47.508245][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_275', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:35:47.508330][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:35:57.275976][GENERATOR][INFO]Trying to sample '2' jobs from the hyperparameter space\\r\\n[2019-12-11T06:35:57.491520][GENERATOR][INFO]Successfully sampled '2' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:36:05.6838391Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_274'\\r\\n[2019-12-11T06:36:06.4283064Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_274', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:36:06.5139410Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_277'\\r\\n[2019-12-11T06:36:06.5121844Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_276'\\r\\n[2019-12-11T06:36:06.9993606Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_276'\\r\\n[2019-12-11T06:36:07.0020959Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_277'\\r\\n[2019-12-11T06:36:18.311512][ENFORCER][INFO]Request cancellation of job https://southcentralus.experiments.azureml.net/subscriptions/6fa1b60b-c4be-4966-a446-261a3ad62d42/resourceGroups/AML2/providers/Microsoft.MachineLearningServices/workspaces/AML2/experiments/cnn/runs/cnn_1576035422508121_275, best experiment metric: 0.0016233322832329537, job's best metric: 0.002651665080127828\\r\\n[2019-12-11T06:36:18.438820][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_276', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:36:18.438958][ENFORCER][INFO]Policy cancelled 1 jobs\\r\\n[2019-12-11T06:36:28.143313][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:36:28.275715][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:36:37.6624713Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_275'\\r\\n[2019-12-11T06:36:37.9894009Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_275', previous status = 'RUNNING')]\\r\\n[2019-12-11T06:36:38.0912920Z][SCHEDULER][INFO]Scheduling job, id='cnn_1576035422508121_278'\\r\\n[2019-12-11T06:36:38.4644674Z][SCHEDULER][INFO]Successfully scheduled a job. Id='cnn_1576035422508121_278'\\r\\n[2019-12-11T06:36:49.939172][ENFORCER][INFO]Jobs [RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_276', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_277', data_container_id=None), RunKey(run_scope=RunScope(host='https://southcentralus.experiments.azureml.net', subscription_id='6fa1b60b-c4be-4966-a446-261a3ad62d42', resource_group='AML2', workspace_name='AML2', project_name='cnn'), run_id='cnn_1576035422508121_278', data_container_id=None)] do not contain any metrics named 'Loss' at this moment, policy cannot be applied.\\r\\n[2019-12-11T06:36:59.064820][GENERATOR][INFO]Trying to sample '1' jobs from the hyperparameter space\\r\\n[2019-12-11T06:36:59.192261][GENERATOR][INFO]Successfully sampled '1' jobs, they will soon be submitted to the execution target.\\r\\n[2019-12-11T06:37:06.869346][CONTROLLER][INFO]Experiment has been marked for cancellation.\\r\\n[2019-12-11T06:37:06.869503][CONTROLLER][WARNING]The experiment is taking longer than max_duration, 180 minutes. The system is canceling the experiment.\\r\\n[2019-12-11T06:37:09.0104427Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_276'\\r\\n[2019-12-11T06:37:09.0115392Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_277'\\r\\n[2019-12-11T06:37:09.0122614Z][SCHEDULER][INFO]Cancelling job, id='cnn_1576035422508121_278'\\r\\n[2019-12-11T06:37:09.4051207Z][SCHEDULER][INFO]Updating job statuses to cancelled: [(job id = 'cnn_1576035422508121_276', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_277', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_278', previous status = 'RUNNING'), (job id = 'cnn_1576035422508121_279', previous status = 'QUEUED')]\\r\\n[2019-12-11T06:37:39.284363][CONTROLLER][INFO]Experiment was 'ExperimentStatus.RUNNING', is 'ExperimentStatus.FINISHED'.\\n\\nRun is completed.\", \"graph\": {}, \"widget_settings\": {\"childWidgetDisplay\": \"popup\", \"send_telemetry\": false, \"log_level\": \"NOTSET\", \"sdk_version\": \"1.0.76\"}, \"loading\": false}" }, "metadata": {}, "output_type": "display_data" @@ -695,14 +641,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['--datadir', '$AZUREML_DATAREFERENCE_1d825e505b3b466ab46d88a56e20cc87', '--scriptdir', '$AZUREML_DATAREFERENCE_568f879a0645498e8c7732e801abef98', '--T', '336', '--alpha', '0', '--batch-size', '16', '--kernel-size', '3', '--latent-dim-1', '5', '--latent-dim-2', '10', '--learning-rate', '0.001']\n" + "['--datadir', '$AZUREML_DATAREFERENCE_bc05cd0424b44823ae2861964d34c82e', '--scriptdir', '$AZUREML_DATAREFERENCE_8f1bb2deb4dd4fe9a157ea3319691d2b', '--T', '336', '--alpha', '0', '--batch-size', '32', '--kernel-size', '3', '--latent-dim-1', '15', '--latent-dim-2', '0', '--learning-rate', '0.0001']\n" ] } ], @@ -713,34 +659,57 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'Loss': [0.005301605221908996,\n", - " 0.0027711154075417367,\n", - " 0.0023859904158548724,\n", - " 0.002179504699378535,\n", - " 0.0020667108011005074,\n", - " 0.001964724402389645,\n", - " 0.0018639376981250812,\n", - " 0.0017758649233368938,\n", - " 0.0017312512995875738,\n", - " 0.0016791180893398455,\n", - " 0.00162177898623937,\n", - " 0.0015962386112888377,\n", - " 0.0015538364558274282,\n", - " 0.0015424007355146622,\n", - " 0.0015143105867715138,\n", - " 0.0014977911621101615,\n", - " 0.001495059733049223],\n", - " 'validationLoss': 0.0013326180317865994,\n", - " 'testMAPE': 0.03852048017983777}" + "{'Loss': [0.00828489969507693,\n", + " 0.004136542220777458,\n", + " 0.0034588238296976675,\n", + " 0.0031144267807767605,\n", + " 0.0028496974581269006,\n", + " 0.0026915336654038365,\n", + " 0.002564426893895588,\n", + " 0.0024713229856310154,\n", + " 0.002391322827202215,\n", + " 0.002278872424158897,\n", + " 0.002219806186929776,\n", + " 0.002150637478210458,\n", + " 0.0020954079746401447,\n", + " 0.002052621989421208,\n", + " 0.001995195306968414,\n", + " 0.001964645971722809,\n", + " 0.0019075027098691877,\n", + " 0.0018852346998479538,\n", + " 0.0018389220900815843,\n", + " 0.0018096223448300364,\n", + " 0.0017915446650011754,\n", + " 0.0017693798549019055,\n", + " 0.0017443429152237675,\n", + " 0.0017261701835313817,\n", + " 0.0017015754092010817,\n", + " 0.0016880552035095416,\n", + " 0.0016621246432036668,\n", + " 0.0016454236919215969,\n", + " 0.001628912583543553,\n", + " 0.0016216333113423323,\n", + " 0.00160095536015865,\n", + " 0.001578251617845031,\n", + " 0.0015633399822259724,\n", + " 0.0015564324238287039,\n", + " 0.0015432167530753582,\n", + " 0.0015338566808518227,\n", + " 0.0015159097882403644,\n", + " 0.0015120560548694288,\n", + " 0.0015056484845395416,\n", + " 0.0014921563051374907],\n", + " 'validationLoss': 0.0014064463502210048,\n", + " 'testMAPE': 0.038476563486814615}" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -758,14 +727,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['azureml-logs/55_batchai_execution.txt', 'azureml-logs/55_batchai_stdout-job_post.txt', 'azureml-logs/55_batchai_stdout-job_prep.txt', 'azureml-logs/80_driver_log.txt', 'logs/azureml/137_azureml.log', 'logs/azureml/azureml.log', 'outputs/model/bestmodel.h5', 'outputs/model/bestmodel.json']\n" + "['azureml-logs/55_azureml-execution-tvmps_f73dd0bad9aec836a2a78ef5afa21354100de2fea34deac509033bbc16248f65_d.txt', 'azureml-logs/65_job_prep-tvmps_f73dd0bad9aec836a2a78ef5afa21354100de2fea34deac509033bbc16248f65_d.txt', 'azureml-logs/70_driver_log.txt', 'azureml-logs/process_info.json', 'azureml-logs/process_status.json']\n" ] } ], @@ -782,9 +751,23 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModelPathNotFoundException", + "evalue": "ModelPathNotFoundException:\n\tMessage: Could not locate the provided model_path outputs/model in the set of files uploaded to the run: ['azureml-logs/55_azureml-execution-tvmps_f73dd0bad9aec836a2a78ef5afa21354100de2fea34deac509033bbc16248f65_d.txt', 'azureml-logs/65_job_prep-tvmps_f73dd0bad9aec836a2a78ef5afa21354100de2fea34deac509033bbc16248f65_d.txt', 'azureml-logs/70_driver_log.txt', 'azureml-logs/process_info.json', 'azureml-logs/process_status.json']\n See https://aka.ms/run-logging for more details.\n\tInnerException None\n\tErrorResponse \n{\n \"error\": {\n \"message\": \"Could not locate the provided model_path outputs/model in the set of files uploaded to the run: ['azureml-logs/55_azureml-execution-tvmps_f73dd0bad9aec836a2a78ef5afa21354100de2fea34deac509033bbc16248f65_d.txt', 'azureml-logs/65_job_prep-tvmps_f73dd0bad9aec836a2a78ef5afa21354100de2fea34deac509033bbc16248f65_d.txt', 'azureml-logs/70_driver_log.txt', 'azureml-logs/process_info.json', 'azureml-logs/process_status.json']\\n See https://aka.ms/run-logging for more details.\"\n }\n}", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModelPathNotFoundException\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbest_run\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"MODEL_NAME\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'outputs/model'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/anaconda3/envs/dftf2/lib/python3.6/site-packages/azureml/core/run.py\u001b[0m in \u001b[0;36mregister_model\u001b[0;34m(self, model_name, model_path, tags, properties, model_framework, model_framework_version, description, datasets, sample_input_dataset, sample_output_dataset, resource_configuration, **kwargs)\u001b[0m\n\u001b[1;32m 1988\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproperties\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_framework\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_framework_version\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1989\u001b[0m \u001b[0mdescription\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdescription\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munpack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_input_dataset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_input_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1990\u001b[0;31m sample_output_dataset=sample_output_dataset, resource_configuration=resource_configuration, **kwargs)\n\u001b[0m\u001b[1;32m 1991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1992\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_update_dataset_lineage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/dftf2/lib/python3.6/site-packages/azureml/_run_impl/run_history_facade.py\u001b[0m in \u001b[0;36mregister_model\u001b[0;34m(self, model_name, model_path, tags, properties, model_framework, model_framework_version, asset_id, sample_input_dataset, sample_output_dataset, resource_configuration, **kwargs)\u001b[0m\n\u001b[1;32m 379\u001b[0m raise ModelPathNotFoundException(\n\u001b[1;32m 380\u001b[0m \"\"\"Could not locate the provided model_path {} in the set of files uploaded to the run: {}\n\u001b[0;32m--> 381\u001b[0;31m See https://aka.ms/run-logging for more details.\"\"\".format(model_path, str(run_files)))\n\u001b[0m\u001b[1;32m 382\u001b[0m \u001b[0martifacts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"prefix\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0martifact_prefix_id\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0mmetadata_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModelPathNotFoundException\u001b[0m: ModelPathNotFoundException:\n\tMessage: Could not locate the provided model_path outputs/model in the set of files uploaded to the run: ['azureml-logs/55_azureml-execution-tvmps_f73dd0bad9aec836a2a78ef5afa21354100de2fea34deac509033bbc16248f65_d.txt', 'azureml-logs/65_job_prep-tvmps_f73dd0bad9aec836a2a78ef5afa21354100de2fea34deac509033bbc16248f65_d.txt', 'azureml-logs/70_driver_log.txt', 'azureml-logs/process_info.json', 'azureml-logs/process_status.json']\n See https://aka.ms/run-logging for more details.\n\tInnerException None\n\tErrorResponse \n{\n \"error\": {\n \"message\": \"Could not locate the provided model_path outputs/model in the set of files uploaded to the run: ['azureml-logs/55_azureml-execution-tvmps_f73dd0bad9aec836a2a78ef5afa21354100de2fea34deac509033bbc16248f65_d.txt', 'azureml-logs/65_job_prep-tvmps_f73dd0bad9aec836a2a78ef5afa21354100de2fea34deac509033bbc16248f65_d.txt', 'azureml-logs/70_driver_log.txt', 'azureml-logs/process_info.json', 'azureml-logs/process_status.json']\\n See https://aka.ms/run-logging for more details.\"\n }\n}" + ] + } + ], "source": [ "model = best_run.register_model(model_name=params[\"MODEL_NAME\"], model_path='outputs/model')" ] @@ -799,9 +782,9 @@ ], "metadata": { "kernelspec": { - "display_name": "dnntutorial", + "display_name": "Python 3.6", "language": "python", - "name": "dnntutorial" + "name": "dftf2" }, "language_info": { "codemirror_mode": { @@ -813,7 +796,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.9" }, "widgets": { "application/vnd.jupyter.widget-state+json": {