tf.compat.v1.scatter

Applies sparse updates to a variable reference.

tf.compat.v1.scatter_update(
    ref, indices, updates, use_locking=True, name=None
)

This operation computes

    # Scalar indices
    ref[indices, ...] = updates[...]

    # Vector indices (for each i)
    ref[indices[i], ...] = updates[i, ...]

    # High rank indices (for each i, ..., j)
    ref[indices[i, ..., j], ...] = updates[i, ..., j, ...]

This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value.

If values in ref is to be updated more than once, because there are duplicate entries in indices, the order at which the updates happen for each value is undefined.

Requires updates.shape = indices.shape + ref.shape[1:].

Args
`ref`	A `Variable`.
`indices`	A `Tensor`. Must be one of the following types: `int32`, `int64`. A tensor of indices into the first dimension of `ref`.
`updates`	A `Tensor`. Must have the same type as `ref`. A tensor of updated values to store in `ref`.
`use_locking`	An optional `bool`. Defaults to `True`. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
`name`	A name for the operation (optional).

Returns
Same as `ref`. Returned as a convenience for operations that want to use the updated values after the update is done.

TensorFlow

tf.compat / v1 / v1.scatter_sub

Args

Returns

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.scatter_sub

Args

Returns