description: Type specification for a tf.RaggedTensor.

tf.RaggedTensorSpec

Type specification for a tf.RaggedTensor.

Inherits From: TypeSpec

Compat aliases for migration

See Migration guide for more details.

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
)

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

@classmethod
from_value(
    value
)

is_compatible_with(
    spec_or_value
)

Returns true if spec_or_value is compatible with this TypeSpec.

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__(
    other
)

Return self==value.

__ne__(
    other
)

Return self!=value.