def main(argv=None): model = CNN( train_images_dir='data/train/', val_images_dir='data/val/', test_images_dir='data/test/', num_epochs=40, train_batch_size=1000, val_batch_size=1000, test_batch_size=10000, height_of_image=28, width_of_image=28, num_channels=1, num_classes=10, learning_rate=0.001, base_dir='results', max_to_keep=2, model_name="CNN", model='CNN' ) model.create_network() model.initialize_network() if True: model.train_model(1, 1, 1, 4) else: model.test_model()
def main(argv=None): model = CNN(train_images_dir=FLAGS.train_images_dir, val_images_dir=FLAGS.val_images_dir, test_images_dir=FLAGS.test_images_dir, inference_dir=FLAGS.inference_dir, num_epochs=FLAGS.num_epochs, train_batch_size=FLAGS.train_batch_size, val_batch_size=FLAGS.val_batch_size, height_of_image=FLAGS.height_of_image, width_of_image=FLAGS.width_of_image, num_channels=FLAGS.num_channels, num_classes=FLAGS.num_classes, learning_rate=FLAGS.learning_rate, base_dir=FLAGS.base_dir, max_to_keep=FLAGS.max_to_keep, model_name=FLAGS.model_name, keep_prob=FLAGS.keep_prob) model.create_network() model.initialize_network() if FLAGS.train: model.train_model(FLAGS.display_step, FLAGS.validation_step, FLAGS.checkpoint_step, FLAGS.summary_step) else: model.test_model()