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Base class for TensorFlow ExtensionType
classes.
tf.experimental.ExtensionType(
*args, **kwargs
)
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 |
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: ops.Tensor
features: typing.Mapping[str, tf.Tensor]
class ToyStore(ExtensionType):
name: str
toys: typing.Tuple[Toy, ...]
Methods
__eq__
__eq__(
other
)
Return self==value.
__ne__
__ne__(
other
)
Return self!=value.