# saves the model weights after each epoch if the validation loss decreased
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(model_output_dir,'lane_navigation_check.h5'), verbose=1, save_best_only=True)

history = model.fit_generator(image_data_generator( X_train, y_train, batch_size=100, is_training=True),
                              steps_per_epoch=300,
                              epochs=10,
                              validation_data = image_data_generator( X_valid, y_valid, batch_size=100, is_training=False),
                              validation_steps=200,
                              verbose=1,
                              shuffle=1,
                              callbacks=[checkpoint_callback])
# always save model output as soon as model finishes training
model.save(os.path.join(model_output_dir,'lane_navigation_final.h5'))