tf.tensor_scatter_nd_add

Adds sparse updates to an existing tensor according to indices.

tf.tensor_scatter_nd_add(
    tensor: _atypes.TensorFuzzingAnnotation[TV_TensorScatterAdd_T],
    indices: _atypes.TensorFuzzingAnnotation[TV_TensorScatterAdd_Tindices],
    updates: _atypes.TensorFuzzingAnnotation[TV_TensorScatterAdd_T],
    name=None
) -> _atypes.TensorFuzzingAnnotation[TV_TensorScatterAdd_T]

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
<tf.Tensor: shape=(8,), dtype=int32,
numpy=array([ 1, 12,  1, 11, 10,  1,  1, 13], dtype=int32)>

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
<tf.Tensor: shape=(4, 4, 4), dtype=int32,
numpy=array([[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
             [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
             [[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
             [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int32)>

tensor A Tensor. Tensor to copy/update.
indices A Tensor. Must be one of the following types: int32, int64. Index tensor.
updates A Tensor. Must have the same type as tensor. Updates to scatter into output.
name A name for the operation (optional).

A Tensor. Has the same type as tensor.