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

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

Parses a single SequenceExample proto.

Parses a single serialized SequenceExample proto given in serialized.

This op parses a serialize sequence example into a tuple of dictionaries mapping keys to Tensor and SparseTensor objects respectively. 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.

#### Args:

• serialized: A scalar (0-D Tensor) of type string, a single binary serialized SequenceExample proto.
• context_features: A dict mapping feature keys to FixedLenFeature or VarLenFeature values. These features are associated with a SequenceExample as a whole.
• sequence_features: A dict mapping feature keys to FixedLenSequenceFeature or VarLenFeature 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).

#### Returns:

A tuple of two dicts, each mapping keys to Tensors and SparseTensors. The first dict contains the context key/values. The second dict contains the feature_list key/values.

#### Raises:

• ValueError: if any feature is invalid.