model = Sequential()
model.add(Convolution2D(64, 3, 3, input_shape=(1,img_dims, img_dims), border_mode='same', activation='relu'))
model.add(Dropout(0.2))
model.add(Convolution2D(64, 3, 3, activation='relu', border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
#model.add(Convolution2D(64, 3, 3, input_shape=(1,img_dims, img_dims), border_mode='same', activation='relu'))
#model.add(Dropout(0.2))
#model.add(Convolution2D(64, 3, 3, activation='relu', border_mode='same'))
#model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(32, activation='relu', W_constraint=maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))