tf.raw_ops.ScatterNdUpdate

Applies sparse updates to individual values or slices within a given

variable according to indices.

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 Kth dimension of ref.

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

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

For example, say we want to update 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:

    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])
with tf.Session() as sess:
print sess.run(update)


The resulting update to ref would look like this:

[1, 11, 3, 10, 9, 6, 7, 12]


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

See also tf.scatter_update and tf.batch_scatter_update.

ref A mutable Tensor. A mutable Tensor. Should be from a Variable node.
indices A Tensor. Must be one of the following types: int32, int64. A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref.
updates A Tensor. Must have the same type as ref. A Tensor. Must have the same type as ref. A tensor of updated values to add to ref.
use_locking An optional bool. Defaults to True. An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
name A name for the operation (optional).

A mutable Tensor. Has the same type as ref.

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