ScatterNd

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

Scatter 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_nd_add, except that the tensor is zero-initialized. Calling tf.scatter_nd(indices, updates, shape) is identical to calling `tf.tensor_scatter_nd_add(tf.zeros(shape, updates.dtype), indices, updates)`

If `indices` contains duplicates, the associated `updates` are accumulated (summed) into the output tensor.

WARNING: For floating-point data types, the output may be nondeterministic. This is because the order in which the updates are applied is nondeterministic and when floating-point numbers are added in different orders the resulting numerical approximation error can be slightly different. However, the output will be deterministic if op determinism is enabled via tf.config.experimental.enable_op_determinism.

`indices` is an integer tensor containing indices into the output tensor. 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([[1], [3]])
     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:

[[[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]], [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]

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

From class org.tensorflow.op.PrimitiveOp

final boolean	equals(Object obj)
final int	hashCode()
Operation	op() Returns the underlying Operation
final String	toString()

From class java.lang.Object

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

From interface org.tensorflow.Operand

abstract Output<U>

asOutput() Returns the symbolic handle of a tensor.