tf.OptionalSpec
View source on GitHub |
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
@staticmethod from_value( value )
is_compatible_with
is_compatible_with( spec_or_value )
Returns true if spec_or_value
is compatible with this TypeSpec.
most_specific_compatible_type
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__
__eq__( other )
Return self==value.
__ne__
__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