Scatter `updates`

into a new tensor according to `indices`

.

tf.scatter_nd( indices, updates, shape, name=None )

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.

This operation is similar to tensor_scatter_add, except that the tensor is zero-initialized. Calling `tf.scatter_nd(indices, values, shape)`

is identical to `tensor_scatter_add(tf.zeros(shape, values.dtype), indices, values)`

If `indices`

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

`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.

Args | |
---|---|

`indices` | A `Tensor` . Must be one of the following types: `int32` , `int64` . Index tensor. |

`updates` | A `Tensor` . Updates to scatter into output. |

`shape` | A `Tensor` . Must have the same type as `indices` . 1-D. The shape of the resulting tensor. |

`name` | A name for the operation (optional). |

Returns | |
---|---|

A `Tensor` . Has the same type as `updates` . |

© 2020 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/versions/r1.15/api_docs/python/tf/scatter_nd