tf.experimental.Optional

Represents a value that may or may not be present.

A tf.experimental.Optional can represent the result of an operation that may fail as a value, rather than raising an exception and halting execution. For example, tf.data.Iterator.get_next_as_optional() returns a tf.experimental.Optional that either contains the next element of an iterator if one exists, or an "empty" value that indicates the end of the sequence has been reached.

tf.experimental.Optional can only be used with values that are convertible to tf.Tensor or tf.CompositeTensor.

One can create a tf.experimental.Optional from a value using the from_value() method:

optional = tf.experimental.Optional.from_value(42)
print(optional.has_value())
tf.Tensor(True, shape=(), dtype=bool)
print(optional.get_value())
tf.Tensor(42, shape=(), dtype=int32)

or without a value using the empty() method:

optional = tf.experimental.Optional.empty(
  tf.TensorSpec(shape=(), dtype=tf.int32, name=None))
print(optional.has_value())
tf.Tensor(False, shape=(), dtype=bool)

optional = tf.experimental.Optional.from_value(42)
print(optional.element_spec)
tf.TensorSpec(shape=(), dtype=tf.int32, name=None)

Methods

empty

View source

@staticmethod
empty(
    element_spec
)

Returns an Optional that has no value.

Note: This method takes an argument that defines the structure of the value that would be contained in the returned Optional if it had a value.

optional = tf.experimental.Optional.empty(
  tf.TensorSpec(shape=(), dtype=tf.int32, name=None))
print(optional.has_value())
tf.Tensor(False, shape=(), dtype=bool)

from_value

View source

@staticmethod
from_value(
    value
)

Returns a tf.experimental.Optional that wraps the given value.

optional = tf.experimental.Optional.from_value(42)
print(optional.has_value())
tf.Tensor(True, shape=(), dtype=bool)
print(optional.get_value())
tf.Tensor(42, shape=(), dtype=int32)

get_value

View source

@abc.abstractmethod
get_value(
    name=None
)

Returns the value wrapped by this optional.

If this optional does not have a value (i.e. self.has_value() evaluates to False), this operation will raise tf.errors.InvalidArgumentError at runtime.

optional = tf.experimental.Optional.from_value(42)
print(optional.get_value())
tf.Tensor(42, shape=(), dtype=int32)

has_value

View source

@abc.abstractmethod
has_value(
    name=None
)

Returns a tensor that evaluates to True if this optional has a value.

optional = tf.experimental.Optional.from_value(42)
print(optional.has_value())
tf.Tensor(True, shape=(), dtype=bool)