verbose=2, steps_per_epoch=train_generator.samples // batch_size, epochs=epoch_number, validation_data=validation_generator, validation_steps=validation_generator.samples // batch_size) print("Model saved to file: {}".format(output_model_file)) model.save(models_dir + output_model_file) if __name__ == "__main__": if not os.path.exists(train_data_dir): print("Train data directory does not exist, exiting") exit(1) args = parse_args() models_class = MyModel(size=image_size) if args.model == 'CLS': output_model_file = 'simple_model.h5' model = models_class.get_simple_model() elif args.model == 'CNV': output_model_file = 'cnv_model.h5' model = models_class.get_conv_learn_model() elif args.model == 'TRM': output_model_file = 'all_untrim_model.h5' model = models_class.get_all_net_trim_model() else: print("Wrong model, use one of following: 'CLS', 'CNV', 'TRM'") exit(1) train_model(model)