(config_name, exp_uid)
return (dataset_blob, train_data, train_label, train_mu, train_sigma,
          index_grouped_by_label)
Load a dataset from a config's name.

  The loaded dataset consists of:
    - original data (dataset_blob, train_data, train_label),
    - encoded data from a pretrained model (train_mu, train_sigma), and
    - index grouped by label (index_grouped_by_label).

  Args:
    config_name: A string indicating the name of config to parameterize the
        model that associates with the dataset.
    exp_uid: A string representing the unique id of experiment to be used in
        model that associates with the dataset.

  Returns:
    An tuple of abovementioned components in the dataset.
Load a dataset from a config's name.
Load a dataset from a config s name .
def load_dataset(config_name, exp_uid):
  """Load a dataset from a config's name.

  The loaded dataset consists of:
    - original data (dataset_blob, train_data, train_label),
    - encoded data from a pretrained model (train_mu, train_sigma), and
    - index grouped by label (index_grouped_by_label).

  Args:
    config_name: A string indicating the name of config to parameterize the
        model that associates with the dataset.
    exp_uid: A string representing the unique id of experiment to be used in
        model that associates with the dataset.

  Returns:
    An tuple of abovementioned components in the dataset.
  """

  config = load_config(config_name)
  if config_is_wavegan(config):
    return load_dataset_wavegan()

  model_uid = common.get_model_uid(config_name, exp_uid)

  dataset = common.load_dataset(config)
  train_data = dataset.train_data
  attr_train = dataset.attr_train
  path_train = os.path.join(dataset.basepath, 'encoded', model_uid,
                            'encoded_train_data.npz')
  train = np.load(path_train)
  train_mu = train['mu']
  train_sigma = train['sigma']
  train_label = np.argmax(attr_train, axis=-1)  # from one-hot to label
  index_grouped_by_label = common.get_index_grouped_by_label(train_label)

  tf.logging.info('index_grouped_by_label size: %s',
                  [len(_) for _ in index_grouped_by_label])

  tf.logging.info('train loaded from %s', path_train)
  tf.logging.info('train shapes: mu = %s, sigma = %s', train_mu.shape,
                  train_sigma.shape)
  dataset_blob = dataset
  return (dataset_blob, train_data, train_label, train_mu, train_sigma,
          index_grouped_by_label)
def load_dataset ( config_name , exp_uid ) : config = load_config ( config_name ) if config_is_wavegan ( config ) : return load_dataset_wavegan ( ) model_uid = common . get_model_uid ( config_name , exp_uid ) dataset = common . load_dataset ( config ) train_data = dataset . train_data attr_train = dataset . attr_train path_train = os . path . join ( dataset . basepath , 'encoded' , model_uid , 'encoded_train_data.npz' ) train = np . load ( path_train ) train_mu = train [ 'mu' ] train_sigma = train [ 'sigma' ] train_label = np . argmax ( attr_train , axis = - 1 ) # from one-hot to label index_grouped_by_label = common . get_index_grouped_by_label ( train_label ) tf . logging . info ( 'index_grouped_by_label size: %s' , [ len ( _ ) for _ in index_grouped_by_label ] ) tf . logging . info ( 'train loaded from %s' , path_train ) tf . logging . info ( 'train shapes: mu = %s, sigma = %s' , train_mu . shape , train_sigma . shape ) dataset_blob = dataset return ( dataset_blob , train_data , train_label , train_mu , train_sigma , index_grouped_by_label )