diff --git a/example/mnist_cnn_one_iteration.py b/example/mnist_cnn_one_iteration.py
index e9c9031..a575dd6 100644
--- a/example/mnist_cnn_one_iteration.py
+++ b/example/mnist_cnn_one_iteration.py
@@ -23,7 +23,7 @@
 # number of convolutional filters to use
 nb_filters = 4
 # size of pooling area for max pooling
-nb_pool = 2
+nb_pool = 1
 # convolution kernel size
 nb_conv = 3
 
@@ -46,10 +46,10 @@
 
 model = Sequential()
 
-model.add(Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='same',
+model.add(Convolution2D(nb_filters, (nb_conv, nb_conv), padding='same',
                         input_shape=(1, img_rows, img_cols)))
 model.add(Activation('relu'))
-model.add(Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='same'))
+model.add(Convolution2D(nb_filters, (nb_conv, nb_conv), padding='same'))
 model.add(Activation('relu'))
 model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
 model.add(Dropout(0.25))