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Parses a batch of SequenceExample protos.
tf.io.parse_sequence_example(
serialized,
context_features=None,
sequence_features=None,
example_names=None,
name=None
)
Parses a vector of serialized
SequenceExample
protos given in serialized.
This op parses serialized sequence examples 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 (B,T,) + df.dense_shape for
FixedLenSequenceFeature df, where B is the batch size, and T is the
length of the associated FeatureList in the SequenceExample. For instance,
FixedLenSequenceFeature([]) yields a scalar 2-D Tensor of static shape
[None, None] and dynamic shape [B, T], while
FixedLenSequenceFeature([k]) (for int k >= 1) yields a 3-D matrix Tensor
of static shape [None, None, k] and dynamic shape [B, T, k].
Like the input, the resulting output tensors have a batch dimension. This
means that the original per-example shapes of VarLenFeatures and
FixedLenSequenceFeatures can be lost. To handle that situation, this op also
provides dicts of shape tensors as part of the output. There is one dict for
the context features, and one for the feature_list features. Context features
of type FixedLenFeatures will not be present, since their shapes are already
known by the caller. In situations where the input FixedLenSequenceFeatures
are of different sequence lengths across examples, the shorter examples will
be padded with default datatype values: 0 for numeric types, and the empty
string for string types.
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.
Returns | |
|---|---|
A tuple of three dicts, each mapping keys to Tensors,
SparseTensors, and RaggedTensor. The first dict contains the context
key/values, the second dict contains the feature_list key/values, and the
final dict contains the lengths of any dense feature_list features.
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Raises | |
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ValueError
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if any feature is invalid. |