TypeError: 'NoneType' object is not iterable about compare_gan HOT 2 OPEN

  def train_input_fn(self, params=None, preprocess_fn=None):
    """Input function for reading data.

    Args:
      params: Python dictionary with parameters. Must contain the key
        "batch_size". TPUEstimator will set this for you!
      preprocess_fn: Function to process single examples. This is allowed to
        have a `seed` argument.

    Returns:
      `tf.data.Dataset` with preprocessed and batched examples.
    """
    if params is None:
      params = {}
    seed = self._get_per_host_random_seed(params.get("context", None))
    logging.info("train_input_fn(): params=%s seed=%s", params, seed)

    ds = self._load_dataset(split=self._train_split)
    # ds = ds.filter(self._train_filter_fn)
    ds = ds.repeat()
    def one_function(image, label):
      images, labels = self._train_transform_fn(image, label, seed=seed)
      features = {
          "images": images,
          "z": np.random.randn(120).astype(np.float32),
      }
      features["sampled_labels"] = labels
      
      return features, labels
    ds = ds.map(one_function)
      
    # ds = ds.map(functools.partial(self._train_transform_fn, seed=seed))
    # if preprocess_fn is not None:
      # if "seed" in inspect.getargspec(preprocess_fn).args:
      #   preprocess_fn = functools.partial(preprocess_fn, seed=seed)
      # ds = ds.map(one_function)
      # Add a feature for the random offset of operations in tpu_random.py.
    ds = tpu_random.add_random_offset_to_features(ds)
    # ds = ds.shuffle(FLAGS.data_shuffle_buffer_size, seed=seed)
    if "batch_size" in params:
      ds = ds.batch(params["batch_size"], drop_remainder=True)
    return ds.prefetch(tf.contrib.data.AUTOTUNE)