def train(args): tf.get_logger().setLevel(logging.ERROR) mnist = MNIST() stylealae = StyleMNIST() modelname = args.name summary_path = os.path.join(args.summarydir, modelname) if not os.path.exists(summary_path): os.makedirs(summary_path) ckpt_path = os.path.join(args.ckptdir, modelname) if not os.path.exists(ckpt_path): os.makedirs(ckpt_path) controller = LevelController(NUM_LAYERS, EPOCHS_PER_LEVEL) trainer = Trainer(summary_path, ckpt_path, callback=controller) trainer.train( stylealae, args.epochs, mnist.datasets( args.batch_size, padding=2, flatten=False), mnist.datasets( args.batch_size, padding=2, flatten=False, train=False), trainlen=len(mnist.x_train) // args.batch_size) return 0
def train(args): mnist = MNIST() mlpalae = MnistAlae() modelname = args.name summary_path = os.path.join(args.summarydir, modelname) if not os.path.exists(summary_path): os.makedirs(summary_path) ckpt_path = os.path.join(args.ckptdir, modelname) if not os.path.exists(ckpt_path): os.makedirs(ckpt_path) trainer = Trainer(summary_path, ckpt_path) trainer.train( mlpalae, args.epochs, mnist.datasets(bsize=args.batch_size, flatten=True, condition=True), mnist.datasets(bsize=args.batch_size, flatten=True, condition=True, train=False), len(mnist.x_train) // args.batch_size) return 0