tf.TypeSpec

Specifies a TensorFlow value type.

A tf.TypeSpec provides metadata describing an object accepted or returned by TensorFlow APIs. Concrete subclasses, such as tf.TensorSpec and tf.RaggedTensorSpec, are used to describe different value types.

For example, tf.function's input_signature argument accepts a list (or nested structure) of TypeSpecs.

Creating new subclasses of TypeSpec (outside of TensorFlow core) is not currently supported. In particular, we may make breaking changes to the private methods and properties defined by this base class.

Example:

spec = tf.RaggedTensorSpec(shape=[None, None], dtype=tf.int32)
@tf.function(input_signature=[spec])
def double(x):
  return x * 2
print(double(tf.ragged.constant([[1, 2], [3]])))
<tf.RaggedTensor [[2, 4], [6]]>

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

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: 'TypeSpec'
) -> 'TypeSpec'

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
) -> bool

Return self==value.

__ne__

View source

__ne__(
    other
) -> bool

Return self!=value.