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tf.RaggedTensorSpec

Type specification for a tf.RaggedTensor.

Inherits From: TypeSpec, TraceType

View aliases

Compat aliases for migration

tf.compat.v1.RaggedTensorSpec

tf.RaggedTensorSpec(
    shape=None,
    dtype=tf.dtypes.float32,
    ragged_rank=None,
    row_splits_dtype=tf.dtypes.int64,
    flat_values_spec=None
)

Used in the notebooks

Used in the guide
Ragged tensors Subword tokenizers

Args
`shape`	The shape of the RaggedTensor, or `None` to allow any shape. If a shape is specified, then all ragged dimensions must have size `None`.
`dtype`	`tf.DType` of values in the RaggedTensor.
`ragged_rank`	Python integer, the number of times the RaggedTensor's flat_values is partitioned. Defaults to `shape.ndims - 1`.
`row_splits_dtype`	`dtype` for the RaggedTensor's `row_splits` tensor. One of `tf.int32` or `tf.int64`.
`flat_values_spec`	TypeSpec for flat_value of the RaggedTensor. It shall be provided when the flat_values is a CompositeTensor rather then Tensor. If both `dtype` and `flat_values_spec` and are provided, `dtype` must be the same as `flat_values_spec.dtype`. (experimental)

Attributes
`dtype`	The `tf.dtypes.DType` specified by this type for the RaggedTensor. rt = tf.ragged.constant([["a"], ["b", "c"]], dtype=tf.string) tf.type_spec_from_value(rt).dtype tf.string
`flat_values_spec`	The `TypeSpec` of the flat_values of RaggedTensor.
`ragged_rank`	The number of times the RaggedTensor's flat_values is partitioned. Defaults to `shape.ndims - 1`. values = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10]]) tf.type_spec_from_value(values).ragged_rank 1 rt1 = tf.RaggedTensor.from_uniform_row_length(values, 2) tf.type_spec_from_value(rt1).ragged_rank 2
`row_splits_dtype`	The `tf.dtypes.DType` of the RaggedTensor's `row_splits`. rt = tf.ragged.constant([[1, 2, 3], [4]], row_splits_dtype=tf.int64) tf.type_spec_from_value(rt).row_splits_dtype tf.int64
`shape`	The statically known shape of the RaggedTensor. rt = tf.ragged.constant([[0], [1, 2]]) tf.type_spec_from_value(rt).shape TensorShape([2, None]) rt = tf.ragged.constant([[[0, 1]], [[1, 2], [3, 4]]], ragged_rank=1) tf.type_spec_from_value(rt).shape TensorShape([2, None, 2])
`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

`experimental_as_proto`

View source

experimental_as_proto() -> struct_pb2.TypeSpecProto

Returns a proto representation of the TypeSpec instance.

Do NOT override for custom non-TF types.

`experimental_from_proto`

View source

@classmethod
experimental_from_proto(
    proto: struct_pb2.TypeSpecProto
) -> 'TypeSpec'

Returns a TypeSpec instance based on the serialized proto.

Do NOT override for custom non-TF types.

Args
`proto`	Proto generated using 'experimental_as_proto'.

`experimental_type_proto`

View source

@classmethod
experimental_type_proto() -> Type[struct_pb2.TypeSpecProto]

Returns the type of proto associated with TypeSpec serialization.

Do NOT override for custom non-TF types.

`from_value`

View source

@classmethod
from_value(
    value
)

`is_compatible_with`

View source

is_compatible_with(
    spec_or_value
)

Returns true if spec_or_value is compatible with this TypeSpec.

Prefer using "is_subtype_of" and "most_specific_common_supertype" wherever possible.

Args
`spec_or_value`	A TypeSpec or TypeSpec associated value to compare against.

`is_subtype_of`

View source

is_subtype_of(
    other: tf.types.experimental.TraceType
) -> bool

Returns True if self is a subtype of other.

Implements the tf.types.experimental.func.TraceType interface.

If not overridden by a subclass, the default behavior is to assume the TypeSpec is covariant upon attributes that implement TraceType and invariant upon rest of the attributes as well as the structure and type of the TypeSpec.

Args
`other`	A TraceType object.

`most_specific_common_supertype`

View source

most_specific_common_supertype(
    others: Sequence[tf.types.experimental.TraceType]
) -> Optional['TypeSpec']

Returns the most specific supertype TypeSpec of self and others.

Implements the tf.types.experimental.func.TraceType interface.

Args
`others`	A sequence of TraceTypes.

`most_specific_compatible_type`

View source

most_specific_compatible_type(
    other: 'TypeSpec'
) -> 'TypeSpec'

Returns the most specific TypeSpec compatible with self and other. (deprecated)

Deprecated. Please use most_specific_common_supertype instead. Do not override this function.

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.

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/RaggedTensorSpec