tf.experimental.ExtensionType

Base class for TensorFlow ExtensionType classes.

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.experimental.ExtensionType

Tensorflow ExtensionType classes are specialized Python classes that can be used transparently with TensorFlow -- e.g., they can be used with ops such as tf.cond or tf.while_loop and used as inputs or outputs for tf.function and Keras layers.

New ExtensionType classes are defined by creating a subclass of tf.ExtensionType that contains type annotations for all instance variables. The following type annotations are supported:

Type Example
Python integers i: int
Python floats f: float
Python strings s: str
Python booleans b: bool
Python None n: None
Python tuple params: tuple[int, float, int, int]
Python tuple w/ Ellipsis lengths: tuple[int, ...]
Tensors t: tf.Tensor
Composite Tensors rt: tf.RaggedTensor
Extension Types m: MyMaskedTensor
Tensor shapes shape: tf.TensorShape
Tensor dtypes dtype: tf.DType
Type unions length: typing.Union[int, float]
Tuples params: typing.Tuple[int, float, int, int]
Tuples w/ Ellipsis lengths: typing.Tuple[int, ...]
Mappings tags: typing.Mapping[str, str]

Fields annotated with typing.Mapping will be stored using an immutable mapping type.

ExtensionType values are immutable -- i.e., once constructed, you can not modify or delete any of their instance members.

Examples

class MaskedTensor(ExtensionType):
  values: tf.Tensor
  mask: tf.Tensor
class Toy(ExtensionType):
  name: str
  price: tensor.Tensor
  features: typing.Mapping[str, tf.Tensor]
class ToyStore(ExtensionType):
  name: str
  toys: typing.Tuple[Toy, ...]

Methods

__eq__

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Return self==value.

__ne__

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Return self!=value.