model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
train_generator = train_datagen.flow_from_directory(train_dir, target_size=(224, 224), batch_size=32, class_mode='categorical')
# Data augmentation train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
model = Model(inputs=base_model.input, outputs=predictions)
# Training history = model.fit(train_generator, steps_per_epoch=train_generator.samples // 32, validation_data=validation_generator, validation_steps=validation_generator.samples // 32, epochs=10)
# Fine-tune. Make all layers trainable. for layer in model.layers: layer.trainable = True
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])