(split_name, dataset_dir, file_pattern=None, reader=None)
return slim.dataset.Dataset(
      data_sources=file_pattern,
      reader=reader,
      decoder=decoder,
      num_samples=_SPLITS_TO_SIZES[split_name],
      num_classes=_NUM_CLASSES,
      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
      labels_to_names=labels_to_names)
Gets a dataset tuple with instructions for reading MNIST.

  Args:
    split_name: A train/test split name.
    dataset_dir: The base directory of the dataset sources.
    file_pattern: The file pattern to use when matching the dataset sources.
      It is assumed that the pattern contains a '%s' string so that the split
      name can be inserted.
    reader: The TensorFlow reader type.

  Returns:
    A `Dataset` namedtuple.

  Raises:
    ValueError: if `split_name` is not a valid train/test split.
Gets a dataset tuple with instructions for reading MNIST.
Gets a dataset tuple with instructions for reading MNIST .
def get_split(split_name, dataset_dir, file_pattern=None, reader=None):
  """Gets a dataset tuple with instructions for reading MNIST.

  Args:
    split_name: A train/test split name.
    dataset_dir: The base directory of the dataset sources.
    file_pattern: The file pattern to use when matching the dataset sources.
      It is assumed that the pattern contains a '%s' string so that the split
      name can be inserted.
    reader: The TensorFlow reader type.

  Returns:
    A `Dataset` namedtuple.

  Raises:
    ValueError: if `split_name` is not a valid train/test split.
  """
  if split_name not in _SPLITS_TO_SIZES:
    raise ValueError('split name %s was not recognized.' % split_name)

  if not file_pattern:
    file_pattern = _FILE_PATTERN
  file_pattern = os.path.join(dataset_dir, file_pattern % split_name)

  # Allowing None in the signature so that dataset_factory can use the default.
  if reader is None:
    reader = tf.TFRecordReader

  keys_to_features = {
      'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
      'image/format': tf.FixedLenFeature((), tf.string, default_value='raw'),
      'image/class/label': tf.FixedLenFeature(
          [1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
  }

  items_to_handlers = {
      'image': slim.tfexample_decoder.Image(shape=[28, 28, 1], channels=1),
      'label': slim.tfexample_decoder.Tensor('image/class/label', shape=[]),
  }

  decoder = slim.tfexample_decoder.TFExampleDecoder(
      keys_to_features, items_to_handlers)

  labels_to_names = None
  if dataset_utils.has_labels(dataset_dir):
    labels_to_names = dataset_utils.read_label_file(dataset_dir)

  return slim.dataset.Dataset(
      data_sources=file_pattern,
      reader=reader,
      decoder=decoder,
      num_samples=_SPLITS_TO_SIZES[split_name],
      num_classes=_NUM_CLASSES,
      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS,
      labels_to_names=labels_to_names)
def get_split ( split_name , dataset_dir , file_pattern = None , reader = None ) : if split_name not in _SPLITS_TO_SIZES : raise ValueError ( 'split name %s was not recognized.' % split_name ) if not file_pattern : file_pattern = _FILE_PATTERN file_pattern = os . path . join ( dataset_dir , file_pattern % split_name ) # Allowing None in the signature so that dataset_factory can use the default. if reader is None : reader = tf . TFRecordReader keys_to_features = { 'image/encoded' : tf . FixedLenFeature ( ( ) , tf . string , default_value = '' ) , 'image/format' : tf . FixedLenFeature ( ( ) , tf . string , default_value = 'raw' ) , 'image/class/label' : tf . FixedLenFeature ( [ 1 ] , tf . int64 , default_value = tf . zeros ( [ 1 ] , dtype = tf . int64 ) ) , } items_to_handlers = { 'image' : slim . tfexample_decoder . Image ( shape = [ 28 , 28 , 1 ] , channels = 1 ) , 'label' : slim . tfexample_decoder . Tensor ( 'image/class/label' , shape = [ ] ) , } decoder = slim . tfexample_decoder . TFExampleDecoder ( keys_to_features , items_to_handlers ) labels_to_names = None if dataset_utils . has_labels ( dataset_dir ) : labels_to_names = dataset_utils . read_label_file ( dataset_dir ) return slim . dataset . Dataset ( data_sources = file_pattern , reader = reader , decoder = decoder , num_samples = _SPLITS_TO_SIZES [ split_name ] , num_classes = _NUM_CLASSES , items_to_descriptions = _ITEMS_TO_DESCRIPTIONS , labels_to_names = labels_to_names )