TensorFlow 1 version | 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.