# tf.io.parse_single_sequence_example

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/parsing_ops.py#L696-L807) |

Parses a single `SequenceExample` proto.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.io.parse_single_sequence_example`](https://www.tensorflow.org/api_docs/python/tf/io/parse_single_sequence_example), [`tf.compat.v1.parse_single_sequence_example`](https://www.tensorflow.org/api_docs/python/tf/io/parse_single_sequence_example)

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    tf.io.parse_single_sequence_example(
        serialized,
        context_features=None,
        sequence_features=None,
        example_name=None,
        name=None
    )

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

`sequence_features` contains `VarLenFeature`, `RaggedFeature`, and
`FixedLenSequenceFeature` objects. Each `VarLenFeature` is mapped to a
`SparseTensor`; each `RaggedFeature` is mapped to a `RaggedTensor`; 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.

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| ## Args ||
|---|---|
| `serialized` | A scalar (0-D Tensor) of type string, a single binary serialized `SequenceExample` proto. |
| `context_features` | A mapping of feature keys to `FixedLenFeature` or `VarLenFeature` or `RaggedFeature` values. These features are associated with a `SequenceExample` as a whole. |
| `sequence_features` | A mapping of feature keys to `FixedLenSequenceFeature` or `VarLenFeature` or `RaggedFeature` values. These features are associated with data within the `FeatureList` section of the `SequenceExample` proto. |
| `example_name` | A scalar (0-D Tensor) of strings (optional), the name of the serialized proto. |
| `name` | A name for this operation (optional). |

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| ## Returns ||
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| A tuple of two `dict`s, each mapping keys to `Tensor`s and `SparseTensor`s and `RaggedTensor`s. <br /> - The first dict contains the context key/values. - The second dict contains the feature_list key/values. ||

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| ## Raises ||
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| `ValueError` | if any feature is invalid. |

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