Applies sparse addition to input
using individual values or slices
tf.raw_ops.ScatterNdNonAliasingAdd(
input, indices, updates, name=None
)
from updates
according to indices indices
. The updates are nonaliasing:
input
is only modified inplace if no other operations will use it.
Otherwise, a copy of input
is made. This operation has a gradient with
respect to both input
and updates
.
input
is a Tensor
with rank P
and indices
is a Tensor
of rank Q
.
indices
must be integer tensor, containing indices into input
.
It must be shape \([d_0, ..., d_{Q2}, K]\) where 0 < K <= P
.
The innermost dimension of indices
(with length K
) corresponds to
indices into elements (if K = P
) or (PK)
dimensional slices
(if K < P
) along the K
th dimension of input
.
updates
is Tensor
of rank Q1+PK
with shape:
\[[d_0, ..., d_{Q2}, input.shape[K], ..., input.shape[P1]].\]
For example, say we want to add 4 scattered elements to a rank1 tensor to 8 elements. In Python, that addition would look like this:
input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
with tf.Session() as sess:
print(sess.run(output))
The resulting value output
would look like this:
[1, 13, 3, 14, 14, 6, 7, 20]
See tf.scatter_nd
for more details about how to make updates to slices.
Returns  

A Tensor . Has the same type as input .
