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
