def train_cifar_gan(args): cifar_ds = CifarDataset(args.cifar_ds_path, 64, args.how_many_labeled) learning_options = adam_learning_options() learning_options['lr'] = args.learning_rate gan_cifar_trainer = SSGanTrainer(args.latent_dim, cifar_ds, 10, args.out_weights_dir, 64, learning_options) gan_cifar_trainer.train_ss(args.epochs_num)
def train_cifar_gan(args): cifar_ds = SupervisedCifarDataset(args.cifar_ds_path, 64, args.how_many_labeled) learning_options = adam_learning_options() learning_options['lr'] = args.learning_rate cifar_trainer = SupCnnTrainer(cifar_ds, 64, cifar_gan_discriminator, learning_options) cifar_trainer.train(args.epochs_num, args.testing_interval)
def __init__(self, dataset, batch_size, create_model_func, train_options=adam_learning_options()): self.dataset = dataset self.batch_size = batch_size self.train_options = train_options self.img_size = dataset.img_size() self.classes_num = dataset.classes_num() self.input_images = tf.placeholder( tf.float32, shape=[batch_size, self.img_size, self.img_size, 3], name="input_images") self.labels = tf.placeholder(tf.float32, shape=[batch_size, self.classes_num]) self.create_model_func = create_model_func self.logger.info("Initialization finished, dataset size = %d" % dataset.size())
def __init__(self, latent_dim, dataset, classes_num, out_weights_dir, batch_size=64, train_options=adam_learning_options()): super(SSGanTrainer, self).__init__(latent_dim, dataset, out_weights_dir, batch_size, train_options) self.dataset = dataset self.classes_num = classes_num self.testing_interval = config.TESTING_INTERVAL self.testing_iterations = config.TESTING_ITERATIONS self.labels = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, self.classes_num]) self.unlabeled_images = tf.placeholder( dtype=tf.float32, shape=[self.batch_size, self.img_size, self.img_size, 3], name='unlabeled_images')
def __init__(self, latent_dim, dataset, out_weights_dir, train_options=adam_learning_options(), kl_loss_weight=0.0005): self.out_weights_dir = out_weights_dir self.train_options = train_options self.latent_dim = latent_dim self.batch_size = dataset.batch_size self.logger.info("Creating output weights directory %s" % self.out_weights_dir) tl.files.exists_or_mkdir(out_weights_dir) self.img_size = dataset.img_size() self.input_images = tf.placeholder( tf.float32, shape=[self.batch_size, self.img_size, self.img_size, 3], name='input_images') self.kl_loss_weight = kl_loss_weight self.weights_dump_interval = config.WEIGHTS_DUMP_INTERVAL self.dataset = dataset
def __init__(self, latent_dim, transformer, out_weights_dir, batch_size=64, train_options=adam_learning_options()): self.out_weights_dir = out_weights_dir self.transformer = transformer self.latent_dim = latent_dim self.batch_size = batch_size self.logger.info("Creating output weights directory %s" % self.out_weights_dir) tl.files.exists_or_mkdir(out_weights_dir) self.img_size = transformer.img_size() self.train_options = train_options self.weights_dump_interval = config.WEIGHTS_DUMP_INTERVAL self.input_images = tf.placeholder(tf.float32, shape=[self.batch_size, self.img_size, self.img_size, 3], name='input_images') self.z = tf.placeholder(tf.float32, shape=[None, self.latent_dim], name='z')
def train_cifar_gan(args): cifar_ds = UnsupervisedCifarDataSet(args.cifar_ds_path, 64) learning_options = adam_learning_options() learning_options['lr'] = args.learning_rate vae_cifar_trainer = VaeTrainer(args.latent_dim, cifar_ds, args.out_weights_dir, learning_options) vae_cifar_trainer.train(args.epochs_num)