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

TensorScatterAdd

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public final class TensorScatterAdd

Adds sparse `updates` to an existing tensor according to `indices`.

This operation creates a new tensor by adding sparse `updates` to the passed in `tensor`. This operation is very similar to tf.compat.v1.scatter_nd_add, except that the updates are added onto an existing tensor (as opposed to a variable). If the memory for the existing tensor cannot be re-used, a copy is made and updated.

`indices` is an integer tensor containing indices into a new tensor of shape `tensor.shape`. The last dimension of `indices` can be at most the rank of `tensor.shape`:

indices.shape[-1] <= tensor.shape.rank
 
The last dimension of `indices` corresponds to indices into elements (if `indices.shape[-1] = tensor.shape.rank`) or slices (if `indices.shape[-1] < tensor.shape.rank`) along dimension `indices.shape[-1]` of `tensor.shape`. `updates` is a tensor with shape
indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
 
The simplest form of `tensor_scatter_nd_add` is to add individual elements to a tensor by index. For example, say we want to add 4 elements in a rank-1 tensor with 8 elements.

In Python, this scatter add operation would look like this:

>>> indices = tf.constant([[4], [3], [1], [7]]) >>> updates = tf.constant([9, 10, 11, 12]) >>> tensor = tf.ones([8], dtype=tf.int32) >>> updated = tf.tensor_scatter_nd_add(tensor, indices, updates) >>> updated

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 add 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]]]) >>> tensor = tf.ones([4, 4, 4],dtype=tf.int32) >>> updated = tf.tensor_scatter_nd_add(tensor, indices, updates) >>> updated

Note: 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<T>
asOutput()
Returns the symbolic handle of a tensor.
static <T, U extends Number> TensorScatterAdd<T>
create(Scope scope, Operand<T> tensor, Operand<U> indices, Operand<T> updates)
Factory method to create a class wrapping a new TensorScatterAdd operation.
Output<T>
output()
A new tensor copied from tensor and updates added according to the indices.

Inherited Methods

Public Methods

public Output<T> 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 TensorScatterAdd<T> create (Scope scope, Operand<T> tensor, Operand<U> indices, Operand<T> updates)

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

Parameters
scope current scope
tensor Tensor to copy/update.
indices Index tensor.
updates Updates to scatter into output.
Returns
  • a new instance of TensorScatterAdd

public Output<T> output ()

A new tensor copied from tensor and updates added according to the indices.