tf.TypeSpec

TensorFlow 1 version

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Specifies a TensorFlow value type.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.TypeSpec, tf.compat.v1.data.experimental.Structure

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]]>

Attributes
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

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is_compatible_with(
    spec_or_value
)

Returns true if spec_or_value is compatible with this TypeSpec.

most_specific_compatible_type

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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__

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__eq__(
    other
)

Return self==value.

__ne__

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__ne__(
    other
)

Return self!=value.