diff --git a/Library/Build_Model.py b/Library/Build_Model.py
index 195b549..c2ca2f9 100644
--- a/Library/Build_Model.py
+++ b/Library/Build_Model.py
@@ -42,12 +42,10 @@
 from keras.layers import Input, Dense, LSTM, Dropout, Flatten, Activation
 from keras.layers import Lambda, Reshape, multiply
 from keras.layers import concatenate, add, subtract, dot
-from keras.wrappers.scikit_learn import KerasRegressor
-from keras.layers.core import Activation
-from keras.utils.generic_utils import get_custom_objects
-from keras.utils.generic_utils import CustomObjectScope
+from keras.utils import get_custom_objects, CustomObjectScope
 from keras.callbacks import EarlyStopping
 
+from scikeras.wrappers import KerasRegressor
 from sklearn import linear_model
 from sklearn.model_selection import cross_val_score, KFold
 from sklearn.pipeline import Pipeline
@@ -156,14 +154,13 @@ def Loss_SV(V, S, gradient=False):
         dLoss =  0 * V
     return Loss_norm, dLoss
 
-def Loss_Vin(V, Pin, Vin, bound, parameter, gradient=False):
+def Loss_Vin(V, Pin, Vin, bound, gradient=False):
     # Gradient for input boundary constraint
     # Loss = ReLU(Pin . V - Vin)
     # dLoss = ∂(ReLU(Pin . V - Vin)^2/∂V
     # Input: Cf. Gradient_Descent
     Pin  = tf.convert_to_tensor(np.float32(Pin))
     Loss = tf.linalg.matmul(V, tf.transpose(Pin), b_is_sparse=True) - Vin 
-    # tf.cast(tf.multiply(Vin, parameter.scaler), tf.float32)
     Loss = tf.keras.activations.relu(Loss) if bound == 'UB' else Loss 
     Loss_norm = tf.norm(Loss, axis=1, keepdims=True)/Pin.shape[0] # rescaled
     if gradient:
@@ -193,7 +190,7 @@ def Loss_constraint(V, Vin, parameter, gradient=False):
     # mean squared sum L2+L3+L4
     L2, dL2 = Loss_SV(V, parameter.S, gradient=gradient)
     L3, dL3 = Loss_Vin(V, parameter.Pin, Vin,
-                       parameter.mediumbound, parameter, gradient=gradient)
+                       parameter.mediumbound, gradient=gradient)
     L4, dL4 = Loss_Vpos(V, parameter, gradient=gradient)
     # square sum of L2, L3, L4
     L2 = tf.math.square(L2)
@@ -213,7 +210,7 @@ def Loss_all(V, Vin, Vout, parameter, gradient=False):
     L1, dL1 = Loss_Vout(V, parameter.Pout, Vout, gradient=gradient)
     L2, dL2 = Loss_SV(V, parameter.S, gradient=gradient)
     L3, dL3 = Loss_Vin(V, parameter.Pin, Vin,
-                       parameter.mediumbound, parameter, gradient=gradient)
+                       parameter.mediumbound, gradient=gradient)
     L4, dL4 = Loss_Vpos(V, parameter, gradient=gradient)
     # square sum of L1, L2, L3, L4
     L1 = tf.math.square(L1)
diff --git a/Tutorial.ipynb b/Tutorial.ipynb
index a8f958f..1ca019c 100644
--- a/Tutorial.ipynb
+++ b/Tutorial.ipynb
@@ -611,9 +611,9 @@
    "source": [
     "# Runs the appropriate method\n",
     "\n",
-    "if model.model_type is 'MM_QP':\n",
+    "if model.model_type == 'MM_QP':\n",
     "    Ypred, Stats = MM_QP(model, verbose=True)\n",
-    "if model.model_type is 'MM_LP':\n",
+    "if model.model_type == 'MM_LP':\n",
     "    Ypred, Stats = MM_LP(model, verbose=True)\n",
     "\n",
     "# Printing results\n",
diff --git a/environment_amn.yml b/environment_amn.yml
index e821ecb..9b5ca6d 100644
--- a/environment_amn.yml
+++ b/environment_amn.yml
@@ -16,4 +16,5 @@ dependencies:
   - pip
   - ipykernel
   - pip:
-    - silence-tensorflow
\ No newline at end of file
+    - silence-tensorflow
+    - scikeras