Function parse_single_sequence_example

tensorflow/python/ops/parsing_ops.py:1513–1610

Parses a single `SequenceExample` proto. Parses a single serialized [`SequenceExample`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto) proto given in `serialized`. This op parses a serialized sequence example into a tuple of dictionaries, each mapping keys to `Tenso

(
    serialized, context_features=None, sequence_features=None,
    example_name=None, name=None)

           v1=["io.parse_single_sequence_example",
               "parse_single_sequence_example"])
def parse_single_sequence_example(
    serialized, context_features=None, sequence_features=None,
    example_name=None, name=None):
  # pylint: disable=line-too-long
  """Parses a single `SequenceExample` proto.

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

  This op parses a serialized sequence example into a tuple of dictionaries,
  each mapping keys to `Tensor` and `SparseTensor` objects.
  The first dictionary contains mappings for keys appearing in
  `context_features`, and the second dictionary contains mappings for keys
  appearing in `sequence_features`.

  At least one of `context_features` and `sequence_features` must be provided
  and non-empty.

  The `context_features` keys are associated with a `SequenceExample` as a
  whole, independent of time / frame.  In contrast, the `sequence_features` keys
  provide a way to access variable-length data within the `FeatureList` section
  of the `SequenceExample` proto.  While the shapes of `context_features` values
  are fixed with respect to frame, the frame dimension (the first dimension)
  of `sequence_features` values may vary between `SequenceExample` protos,
  and even between `feature_list` keys within the same `SequenceExample`.

  `context_features` contains `VarLenFeature` and `FixedLenFeature` objects.
  Each `VarLenFeature` is mapped to a `SparseTensor`, and each `FixedLenFeature`
  is mapped to a `Tensor`, of the specified type, shape, and default value.

  `sequence_features` contains `VarLenFeature` and `FixedLenSequenceFeature`
  objects. Each `VarLenFeature` is mapped to a `SparseTensor`, and each
  `FixedLenSequenceFeature` is mapped to a `Tensor`, each of the specified type.
  The shape will be `(T,) + df.dense_shape` for `FixedLenSequenceFeature` `df`, where
  `T` is the length of the associated `FeatureList` in the `SequenceExample`.
  For instance, `FixedLenSequenceFeature([])` yields a scalar 1-D `Tensor` of
  static shape `[None]` and dynamic shape `[T]`, while
  `FixedLenSequenceFeature([k])` (for `int k >= 1`) yields a 2-D matrix `Tensor`
  of static shape `[None, k]` and dynamic shape `[T, k]`.

  Each `SparseTensor` corresponding to `sequence_features` represents a ragged
  vector.  Its indices are `[time, index]`, where `time` is the `FeatureList`
  entry and `index` is the value's index in the list of values associated with
  that time.

  `FixedLenFeature` entries with a `default_value` and `FixedLenSequenceFeature`
  entries with `allow_missing=True` are optional; otherwise, we will fail if
  that `Feature` or `FeatureList` is missing from any example in `serialized`.

  `example_name` may contain a descriptive name for the corresponding serialized
  proto. This may be useful for debugging purposes, but it has no effect on the
  output. If not `None`, `example_name` must be a scalar.

  Note that the batch version of this function, `tf.parse_sequence_example`,
  is written for better memory efficiency and will be faster on large
  `SequenceExample`s.

  Args: