Fine-tuning Inception V3 network by making base layers trainable and setting a lower learning rate.

finetune_inceptionv3.py

# Un-freeze the top layers of the model
base_model.trainable = True
# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))

# Fine tune from this layer onwards
fine_tune_at = 249

# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:
    layer.trainable =  False
    
# Compile the model using a much lower learning rate.
inception_model.compile(optimizer = tf.keras.optimizers.RMSprop(lr=0.0001),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

history_fine = inception_model.fit(train.repeat(), 
                                   steps_per_epoch = steps_per_epoch,
                                   epochs = finetune_epochs, 
                                   initial_epoch = initial_epoch,
                                   validation_data = validation.repeat(), 
                                   validation_steps = validation_steps)