Function _parse_single_example_v2_raw

tensorflow/python/ops/parsing_ops.py:2097–2220

Parses `Example` protos. Args: serialized: A scalar (0-D Tensor) string, containing a binary serialized `Example` proto. sparse_keys: A list of string keys in the examples' features. The results for these keys will be returned as `SparseTensor` objects. sparse_types: A lis

(serialized, sparse_keys, sparse_types,
                                 dense_keys, dense_types, dense_defaults,
                                 dense_shapes, name)

def _parse_single_example_v2_raw(serialized, sparse_keys, sparse_types,
                                 dense_keys, dense_types, dense_defaults,
                                 dense_shapes, name):
  """Parses `Example` protos.

  Args:
    serialized: A scalar (0-D Tensor) string, containing a binary
      serialized `Example` proto.
    sparse_keys: A list of string keys in the examples' features.
      The results for these keys will be returned as `SparseTensor` objects.
    sparse_types: A list of `DTypes` of the same length as `sparse_keys`.
      Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`),
      and `tf.string` (`BytesList`) are supported.
    dense_keys: A list of string keys in the examples' features.
      The results for these keys will be returned as `Tensor`s
    dense_types: A list of DTypes of the same length as `dense_keys`.
      Only `tf.float32` (`FloatList`), `tf.int64` (`Int64List`),
      and `tf.string` (`BytesList`) are supported.
    dense_defaults: A dict mapping string keys to `Tensor`s.
      The keys of the dict must match the dense_keys of the feature.
    dense_shapes: A list of tuples with the same length as `dense_keys`.
      The shape of the data for each dense feature referenced by `dense_keys`.
      Required for any input tensors identified by `dense_keys`.  Must be
      either fully defined, or may contain an unknown first dimension.
      An unknown first dimension means the feature is treated as having
      a variable number of blocks, and the output shape along this dimension
      is considered unknown at graph build time.  Padding is applied for
      minibatch elements smaller than the maximum number of blocks for the
      given feature along this dimension.
    name: A name for this operation (optional).

  Returns:
    A `dict` mapping keys to `Tensor`s and `SparseTensor`s.

  Raises:
    ValueError: If sparse and dense key sets intersect, or input lengths do not
      match up.
  """
  with ops.name_scope(name, "ParseSingleExample", [serialized]):
    serialized = ops.convert_to_tensor(serialized, name="serialized")
    dense_defaults = collections.OrderedDict(
    ) if dense_defaults is None else dense_defaults
    sparse_keys = [] if sparse_keys is None else sparse_keys
    sparse_types = [] if sparse_types is None else sparse_types
    dense_keys = [] if dense_keys is None else dense_keys
    dense_types = [] if dense_types is None else dense_types
    dense_shapes = ([[]] * len(dense_keys)
                    if dense_shapes is None else dense_shapes)

    num_dense = len(dense_keys)
    num_sparse = len(sparse_keys)

    if len(dense_shapes) != num_dense:
      raise ValueError("len(dense_shapes) != len(dense_keys): %d vs. %d" %
                       (len(dense_shapes), num_dense))
    if len(dense_types) != num_dense:
      raise ValueError("len(dense_types) != len(num_dense): %d vs. %d" %
                       (len(dense_types), num_dense))