Function parse_single_example_v2

tensorflow/python/ops/parsing_ops.py:2031–2094

Parses an `Example` proto into a `dict` of tensors. Parses a serialized [`Example`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto) proto given in `serialized`. This op parses serialized examples into a dictionary mapping keys to `Tensor` and `SparseTensor` objects

(serialized, features, name=None)

# TODO(b/70890287): Combine the implementation of this op and
# `parse_single_example()` after 1/10/2018.
def parse_single_example_v2(serialized, features, name=None):
  # pylint: disable=line-too-long
  """Parses an `Example` proto into a `dict` of tensors.

  Parses a serialized
  [`Example`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
  proto given in `serialized`.

  This op parses serialized examples into a dictionary mapping keys to `Tensor`
  and `SparseTensor` objects. `features` is a dict from keys to `VarLenFeature`,
  `SparseFeature`, and `FixedLenFeature` objects. Each `VarLenFeature`
  and `SparseFeature` is mapped to a `SparseTensor`, and each
  `FixedLenFeature` is mapped to a `Tensor`.

  Each `VarLenFeature` maps to a `SparseTensor` of the specified type
  representing a ragged matrix. Its indices are `[index]` where
  `index` is the value's index in the list of values associated with
  that feature and example.

  Each `SparseFeature` maps to a `SparseTensor` of the specified type
  representing a Tensor of `dense_shape` `SparseFeature.size`.
  Its `values` come from the feature in the examples with key `value_key`.
  A `values[i]` comes from a position `k` in the feature of an example at batch
  entry `batch`. This positional information is recorded in `indices[i]` as
  `[batch, index_0, index_1, ...]` where `index_j` is the `k-th` value of
  the feature in the example at with key `SparseFeature.index_key[j]`.
  In other words, we split the indices (except the first index indicating the
  batch entry) of a `SparseTensor` by dimension into different features of the
  `Example`. Due to its complexity a `VarLenFeature` should be preferred over a
  `SparseFeature` whenever possible.

  Each `FixedLenFeature` `df` maps to a `Tensor` of the specified type (or
  `tf.float32` if not specified) and shape `df.shape`.

  `FixedLenFeature` entries with a `default_value` are optional. With no default
  value, we will fail if that `Feature` is missing from any example in
  `serialized`.

  Each `FixedLenSequenceFeature` `df` maps to a `Tensor` of the specified type
  (or `tf.float32` if not specified) and shape `(None,) + df.shape`.

  Args:
    serialized: A scalar (0-D Tensor) string, a serialized `Example` proto.
    features: A `dict` mapping feature keys to `FixedLenFeature`,
      `VarLenFeature`, and `SparseFeature` values.
    name: A name for this operation (optional).

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

  Raises:
    ValueError: if any feature is invalid.
  """
  if not features:
    raise ValueError("Missing: features was %s." % features)
  features = _prepend_none_dimension(features)
  (sparse_keys, sparse_types, dense_keys, dense_types,
   dense_defaults, dense_shapes) = _features_to_raw_params(