a2g2.py

initial_model = VGG16(weights="imagenet", include_top=True)
x = Dense(batches.nb_class, activation='softmax')(initial_model.layers[-2].output)
model = Model(initial_model.input, x)


# we freeze the other layers 
for layer in initial_model.layers: layer.trainable=False

opt = Adam(lr=0.001)
model.compile(optimizer=opt,
              loss='categorical_crossentropy', 
              metrics=['accuracy'])

model.fit_generator(batches, samples_per_epoch=batches.nb_sample, 
                    nb_epoch=3, validation_data=valid_batches, 
                    nb_val_samples=valid_batches.nb_sample)