tf.compat.v1.scatter_nd_update

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

tf.compat.v1.scatter_nd_update(
    ref, indices, updates, use_locking=True, name=None
)

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])
    update = tf.compat.v1.scatter_nd_update(ref, indices, updates)
    with tf.compat.v1.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.

Args:

• ref: A Variable.
• indices: 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).

Returns:

The value of the variable after the update.