Warning: This API is deprecated and will be removed in a future version of TensorFlow after the replacement is stable.

ScatterNd

public final class ScatterNd

Scatters `updates` into a tensor of shape `shape` according to `indices`.

Update the input tensor by scattering sparse `updates` according to individual values at the specified `indices`. This op returns an `output` tensor with the `shape` you specify. This op is the inverse of the `tf.gather_nd` operator which extracts values or slices from a given tensor.

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

If `indices` contains duplicates, the duplicate `values` are accumulated (summed).

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

`indices` is an integer 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 of 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 the scatter op is to insert individual elements in a tensor by index. Consider an example where you 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)
     print(scatter)
 
The resulting tensor would look like this:

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

You can also insert entire slices of a higher rank tensor all at once. For example, you can 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)
     print(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.

Public Methods

Output<U>
asOutput()
Returns the symbolic handle of a tensor.
static <U, T extends Number> ScatterNd<U>
create(Scope scope, Operand<T> indices, Operand<U> updates, Operand<T> shape)
Factory method to create a class wrapping a new ScatterNd operation.
Output<U>
output()
A new tensor with the given shape and updates applied according to the indices.

Inherited Methods

Public Methods

public Output<U> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static ScatterNd<U> create (Scope scope, Operand<T> indices, Operand<U> updates, Operand<T> shape)

Factory method to create a class wrapping a new ScatterNd operation.

Parameters
scope current scope
indices Tensor of indices.
updates Values to scatter into the output tensor.
shape 1-D. The shape of the output tensor.
Returns
  • a new instance of ScatterNd

public Output<U> output ()

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