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tf.data.experimental.parse_example_dataset

TensorFlow 1 version View source on GitHub

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.

Returns:

A dataset transformation function, which can be passed to tf.data.Dataset.apply.

Raises:

  • ValueError: if features argument is None.