Adds sparse updates
to an existing tensor according to indices
.
tf.tensor_scatter_nd_add(
tensor: Annotated[Any, TV_TensorScatterAdd_T],
indices: Annotated[Any, TV_TensorScatterAdd_Tindices],
updates: Annotated[Any, TV_TensorScatterAdd_T],
name=None
) > Annotated[Any, 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 reused, 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 rank1
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 rank3 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)>
Returns  

A Tensor . Has the same type as tensor .
