ScatterNdAdd

Applies sparse addition to individual values or slices in a Variable.

`ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.

`indices` must be integer tensor, containing indices into `ref`. 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 slices (if `K < P`) along the `K`th dimension of `ref`.

`updates` is `Tensor` of rank `Q-1+P-K` with shape:

[d_0, ..., d_{Q-2, ref.shape[K], ..., ref.shape[P-1]]
 }

ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
 indices = tf.constant([[4], [3], [1], [7]])
 updates = tf.constant([9, 10, 11, 12])
 add = tf.scatter_nd_add(ref, indices, updates)
 with tf.Session() as sess:
   print sess.run(add)
 

[1, 13, 3, 14, 14, 6, 7, 20]

See tf.scatter_nd for more details about how to make updates to slices.