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)
