Image generator for MNIST.

  Args:
    tmp_dir: path to temporary storage directory.
    training: a Boolean; if true, we use the train set, otherwise the test set.
    how_many: how many images and labels to generate.
    data_filename: file that contains features data.
    label_filename: file that contains labels.
    start_from: from which image to start.

  Returns:
    An instance of image_generator that produces MNIST images.

def mnist_common_generator(tmp_dir,
                           training,
                           how_many,
                           data_filename,
                           label_filename,
                           start_from=0):
  """Image generator for MNIST.

  Args:
    tmp_dir: path to temporary storage directory.
    training: a Boolean; if true, we use the train set, otherwise the test set.
    how_many: how many images and labels to generate.
    data_filename: file that contains features data.
    label_filename: file that contains labels.
    start_from: from which image to start.

  Returns:
    An instance of image_generator that produces MNIST images.
  """
  data_path = os.path.join(tmp_dir, data_filename)
  labels_path = os.path.join(tmp_dir, label_filename)
  images = _extract_mnist_images(data_path, 60000 if training else 10000)
  labels = _extract_mnist_labels(labels_path, 60000 if training else 10000)
  # Shuffle the data to make sure classes are well distributed.
  data = list(zip(images, labels))
  random.shuffle(data)
  images, labels = list(zip(*data))
  return image_utils.image_generator(images[start_from:start_from + how_many],
                                     labels[start_from:start_from + how_many])