TensorFlow 1 version | View source on GitHub |
Parses a single SequenceExample
proto.
tf.io.parse_single_sequence_example(
serialized, context_features=None, sequence_features=None, example_name=None,
name=None
)
Parses a single serialized SequenceExample
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.
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 or RaggedFeature values. These features are associated
with a SequenceExample as a whole.
|
sequence_features
|
A dict mapping 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). |
Returns | |
---|---|
A tuple of two dict s, each mapping keys to Tensor s and SparseTensor s
and RaggedTensor s.
|
Raises | |
---|---|
ValueError
|
if any feature is invalid. |