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tf.OptionalSpec

Type specification for tf.experimental.Optional.

Inherits From: TypeSpec

tf.OptionalSpec(
    element_spec
)

For instance, tf.OptionalSpec can be used to define a tf.function that takes tf.experimental.Optional as an input argument:

@tf.function(input_signature=[tf.OptionalSpec(
  tf.TensorSpec(shape=(), dtype=tf.int32, name=None))])
def maybe_square(optional):
  if optional.has_value():
    x = optional.get_value()
    return x * x
  return -1
optional = tf.experimental.Optional.from_value(5)
print(maybe_square(optional))
tf.Tensor(25, shape=(), dtype=int32)
Attributes
element_spec A nested structure of TypeSpec objects that represents the type specification of the optional element.
value_type The Python type for values that are compatible with this TypeSpec.

In particular, all values that are compatible with this TypeSpec must be an instance of this type.

Methods

from_value

View source

@staticmethod
from_value(
    value
)

is_compatible_with

View source

is_compatible_with(
    spec_or_value
)

Returns true if spec_or_value is compatible with this TypeSpec.

most_specific_compatible_type

View source

most_specific_compatible_type(
    other
)

Returns the most specific TypeSpec compatible with self and other.

Args
other A TypeSpec.
Raises
ValueError If there is no TypeSpec that is compatible with both self and other.

__eq__

View source

__eq__(
    other
)

Return self==value.

__ne__

View source

__ne__(
    other
)

Return self!=value.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/OptionalSpec