A transformation that parses Example protos into a dict of tensors.
tf.data.experimental.parse_example_dataset(
features, num_parallel_calls=1, deterministic=None
)
Parses a number of serialized Example protos given in serialized. We refer
to serialized as a batch with batch_size many entries of individual
Example protos.
This op parses serialized examples into a dictionary mapping keys to Tensor,
SparseTensor, and RaggedTensor objects. features is a dict from keys to
VarLenFeature, RaggedFeature, SparseFeature, and FixedLenFeature
objects. Each VarLenFeature and SparseFeature is mapped to a
SparseTensor; each RaggedFeature is mapped to a RaggedTensor; and each
FixedLenFeature is mapped to a Tensor. See tf.io.parse_example for more
details about feature dictionaries.
Args |
|---|
features
|
A dict mapping feature keys to FixedLenFeature,
VarLenFeature, RaggedFeature, and SparseFeature values.
|
num_parallel_calls
|
(Optional.) A tf.int32 scalar tf.Tensor,
representing the number of parsing processes to call in parallel.
|
deterministic
|
(Optional.) A boolean controlling whether determinism
should be traded for performance by allowing elements to be produced out
of order if some parsing calls complete faster than others. If
deterministic is None, the
tf.data.Options.experimental_deterministic dataset option (True by
default) is used to decide whether to produce elements
deterministically.
|
Raises |
|---|
ValueError
|
if features argument is None.
|
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TensorFlow 1 version
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