clf.add(Convolution2D(64,64,64), activation = "relu") clf.add(MaxPooling2D(pool_size=(2,2))) clf.add(Convolution2D(128,64,64), activation = "relu") clf.add(MaxPooling2D(pool_size=(2,2))) clf.add(Convolution2D(256,64,64), activation = "relu") clf.add(MaxPooling2D(pool_size=(2,2))) # In[ ]: clf.Flatten() # In[ ]: from keras.preprocessing.image import ImageDataGenerator train_datagen = ImageDataGenerator( rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=30, featurewise_center=False, samplewise_center=False, # set each sample mean to 0