tensorflow::ops::ScatterNd

#include <array_ops.h>

Scatter updates into a new tensor according to indices.

Summary

Creates a new tensor by applying sparse updates to individual values or slices within a tensor (initially zero for numeric, empty for string) of the given shape according to indices. This operator is the inverse of the tf.gather_nd operator which extracts values or slices from a given tensor.

If indices contains duplicates, then their updates are accumulated (summed).

WARNING: The order in which updates are applied is nondeterministic, so the output will be nondeterministic if indices contains duplicates because of some numerical approximation issues, numbers summed in different order may yield different results.

indices is an integer tensor containing indices into a new tensor of shape shape. The last dimension of indices can be at most the rank of shape:

indices.shape[-1] <= shape.rank

The last dimension of indices corresponds to indices into elements (if indices.shape[-1] = shape.rank) or slices (if indices.shape[-1] < shape.rank) along dimension indices.shape[-1] of shape. updates is a tensor with shape

indices.shape[:-1] + shape[indices.shape[-1]:]

The simplest form of scatter is to insert individual elements in a tensor by index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements.

In Python, this scatter operation would look like this:

    indices = tf.constant([[4], [3], [1], [7]])
    updates = tf.constant([9, 10, 11, 12])
    shape = tf.constant([8])
    scatter = tf.scatter_nd(indices, updates, shape)
    with tf.Session() as sess:
      print(sess.run(scatter))

The resulting tensor would look like this:

[0, 11, 0, 10, 9, 0, 0, 12]

We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values.

In Python, this scatter operation would look like this:

    indices = tf.constant([[0], [2]])
    updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
                            [7, 7, 7, 7], [8, 8, 8, 8]],
                           [[5, 5, 5, 5], [6, 6, 6, 6],
                            [7, 7, 7, 7], [8, 8, 8, 8]]])
    shape = tf.constant([4, 4, 4])
    scatter = tf.scatter_nd(indices, updates, shape)
    with tf.Session() as sess:
      print(sess.run(scatter))

The resulting tensor would look like this:

[[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
 [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
 [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
 [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]

Note that on CPU, if an out of bound index is found, an error is returned. On GPU, if an out of bound index is found, the index is ignored.

Arguments:

  • scope: A Scope object
  • indices: Index tensor.
  • updates: Updates to scatter into output.
  • shape: 1-D. The shape of the resulting tensor.

Returns:

  • Output: A new tensor with the given shape and updates applied according to the indices.

Constructors and Destructors

ScatterNd(const ::tensorflow::Scope & scope, ::tensorflow::Input indices, ::tensorflow::Input updates, ::tensorflow::Input shape)

Public attributes

operation
output

Public functions

node() const
::tensorflow::Node *
operator::tensorflow::Input() const
operator::tensorflow::Output() const

Public attributes

operation

Operation operation

output

::tensorflow::Output output

Public functions

ScatterNd

 ScatterNd(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input indices,
  ::tensorflow::Input updates,
  ::tensorflow::Input shape
)

node

::tensorflow::Node * node() const 

operator::tensorflow::Input

 operator::tensorflow::Input() const 

operator::tensorflow::Output

 operator::tensorflow::Output() const