def fit_model():
early_stop = EarlyStopping(monitor='val_loss', patience=patience)
return model.fit(train_generator,
validation_data=validation_generator,
steps_per_epoch=train_steps_per_epoch,
epochs=epochs,
validation_steps=val_steps_per_epoch,
verbose=2,
callbacks=[early_stop, tensorboard_callback])
# Run code
# Get and process data
DataHandler.extract_data_files()
num_images = DataHandler.calculate_num_images()
train_steps_per_epoch = np.ceil((num_images * 0.8 / batch_size) - 1)
val_steps_per_epoch = np.ceil((num_images * 0.2 / batch_size) - 1)
train_dir = './data/train/'
# Print image join plots
UtilProvider.print_join_plots(train_dir + 'cartoons/',
'images/joinplot_cartoons', 'C')
UtilProvider.print_join_plots(train_dir + 'photos/', 'images/joinplot_photos',
'P')
[train_generator, validation_generator
] = UtilProvider.create_data_generators(batch_size, image_size)
