tf.IndexedSlicesSpec

Type specification for a tf.IndexedSlices.

Inherits From: TypeSpec

Compat aliases for migration

tf.compat.v1.IndexedSlicesSpec

tf.IndexedSlicesSpec(
    shape=None, dtype=tf.dtypes.float32, indices_dtype=tf.dtypes.int64,
    dense_shape_dtype=None, indices_shape=None
)

Args
`shape`	The dense shape of the `IndexedSlices`, or `None` to allow any dense shape.
`dtype`	`tf.DType` of values in the `IndexedSlices`.
`indices_dtype`	`tf.DType` of the `indices` in the `IndexedSlices`. One of `tf.int32` or `tf.int64`.
`dense_shape_dtype`	`tf.DType` of the `dense_shape` in the `IndexedSlices`. One of `tf.int32`, `tf.int64`, or `None` (if the `IndexedSlices` has no `dense_shape` tensor).
`indices_shape`	The shape of the `indices` component, which indicates how many slices are in the `IndexedSlices`.

Attributes
`value_type`

Methods

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.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/IndexedSlicesSpec