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 non-aliasing:
input is only modified in-place 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 \([d_0, ..., d_{Q-2}, K]\) where 0 < K <= P.
The innermost dimension of indices (with length K) corresponds to
indices into elements (if K = P) or (P-K)-dimensional slices
(if K < P) along the Kth dimension of input.
updates is Tensor of rank Q-1+P-K with shape:
\[[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].\]
For example, say we want to add 4 scattered elements to a rank-1 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.
|