f96176e2ebabf3a976e0f890b93f10ace7c14f5a,train.py,,,#,25

Before Change

maintain_averages_op = tf.group(ema.apply(all_params))

// sample from the model
x_sample = tf.placeholder(tf.float32, shape=(args.sample_batch_size, 32, 32, 3))
gen_par = model(x_sample, ema=ema, dropout_p=0, **model_opt)
new_x_gen = nn.sample_from_discretized_mix_logistic(gen_par, args.nr_logistic_mix)
def sample_from_model(sess):

After Change

// initialize data loaders for train/test splits
DataLoader = {"cifar":cifar10_data.DataLoader, "imagenet":imagenet_data.DataLoader}[args.data_set]
train_data = DataLoader(args.data_dir, "train", args.batch_size * args.nr_gpu, rng=rng, shuffle=True)
test_data = DataLoader(args.data_dir, "test", args.batch_size * args.nr_gpu, shuffle=False)
obs_shape = train_data.get_observation_size() // e.g. a tuple (32,32,3)

// create the model

Instances