#include <array_ops.h>
Scatter updates
into a new (initially zero) tensor according to indices
.
Creates a new tensor by applying sparse updates
to individual values or slices within a zero tensor 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.
WARNING: The order in which updates are applied is nondeterministic, so the output will be nondeterministic if indices
contains duplicates.
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:
Returns:
Output
: A new tensor with the given shape and updates applied according to the indices. Constructors and Destructors | |
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ScatterNd(const ::tensorflow::Scope & scope, ::tensorflow::Input indices, ::tensorflow::Input updates, ::tensorflow::Input shape) |
Public attributes | |
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output |
Public functions | |
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node() const | ::tensorflow::Node * |
operator::tensorflow::Input() const | |
operator::tensorflow::Output() const |
::tensorflow::Output output
ScatterNd( const ::tensorflow::Scope & scope, ::tensorflow::Input indices, ::tensorflow::Input updates, ::tensorflow::Input shape )
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output() const
© 2018 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/api_docs/cc/class/tensorflow/ops/scatter-nd.html