#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:
```python 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:
```python 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:
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 | |
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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
© 2017 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