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
