tensorflow::ops::ScatterNdNonAliasingAdd

#include <array_ops.h>

Applies sparse addition to input using individual values or slices.

Summary

from updates according to indices indices. The updates are non-aliasing: input is only modified in-place if no other operations will use it. Otherwise, a copy of input is made. This operation has a gradient with respect to both input and updates.

input is a Tensor with rank P and indices is a Tensor of rank Q.

indices must be integer tensor, containing indices into input. It must be shape [d_0, ..., d_{Q-2}, K] where 0 < K <= P.

The innermost dimension of indices (with length K) corresponds to indices into elements (if K = P) or (P-K)-dimensional slices (if K < P) along the Kth dimension of input.

updates is Tensor of rank Q-1+P-K with shape:

[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].

For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition would look like this:

input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
with tf.Session() as sess:
  print(sess.run(output))

The resulting value output would look like this:

[1, 13, 3, 14, 14, 6, 7, 20]

See tf.scatter_nd for more details about how to make updates to slices.

Arguments:

• scope: A Scope object
• input: A Tensor.
• indices: A Tensor. Must be one of the following types: int32, int64. A tensor of indices into input.
• updates: A Tensor. Must have the same type as ref. A tensor of updated values to add to input.

Returns:

• Output: A Tensor with the same shape as input, containing values of input updated with updates.

Public attributes

::tensorflow::Output output

Public functions

 ScatterNdNonAliasingAdd(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input indices,
  ::tensorflow::Input updates
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const