tensorflow::ops::ScatterNd
#include <array_ops.h>
Creates a new tensor by applying sparse updates
to individual.
Summary
values or slices within a zero tensor of the given shape
tensor according to indices. This operator is the inverse of the tf.gather_nd operator which extracts values or slices from a given tensor.
TODO(simister): Add a link to Variable.__getitem__ documentation on slice syntax.
shape
is a TensorShape
with rank P
and indices
is a Tensor
of rank Q
.
indices
must be integer tensor, containing indices into shape
. It must be shape [d_0, ..., d_{Q2}, K]
where 0 < K <= P
.
The innermost dimension of indices
(with length K
) corresponds to indices into elements (if K = P
) or slices (if K < P
) along the K
th dimension of shape
.
updates
is Tensor of rank Q1+PK
with shape:
``` [d_0, ..., d_{Q2}, shape[K], ..., shape[P1]]. ```
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 rank1 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 rank3 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]]]
Arguments:
 scope: A Scope object
 indices: A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.
 updates: A Tensor. Must have the same type as tensor. A tensor of updated values to store in ref.
 shape: A vector. 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 


output

Public functions 


node() const

::tensorflow::Node *

operator::tensorflow::Input() const


operator::tensorflow::Output() const


Public attributes
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