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TensorFlow 1 version
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View source on GitHub
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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 * xreturn -1optional = 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.
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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
@staticmethodfrom_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.
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| Raises | |
|---|---|
ValueError
|
If there is no TypeSpec that is compatible with both self
and other.
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__eq__
__eq__(
other
)
Return self==value.
__ne__
__ne__(
other
)
Return self!=value.