tensorflow::ops::ScatterNdAdd

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

Summary

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 add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition 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])
add = tf.scatter_nd_add(ref, indices, updates)
with tf.Session() as sess:
  print sess.run(add)

The resulting update to ref 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.

Arguments:

• scope: A Scope object
• ref: A mutable Tensor. Should be from a Variable node.
• 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 of updated values to add to ref.

Optional attributes (see Attrs):

• use_locking: 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.

Returns:

• Output: Same as ref. Returned as a convenience for operations that want to use the updated values after the update is done.

Public attributes

Operation operation

::tensorflow::Output output_ref

 ScatterNdAdd(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input ref,
  ::tensorflow::Input indices,
  ::tensorflow::Input updates
)

 ScatterNdAdd(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input ref,
  ::tensorflow::Input indices,
  ::tensorflow::Input updates,
  const ScatterNdAdd::Attrs & attrs
)

::tensorflow::Node * node() const 

 operator::tensorflow::Input() const 

 operator::tensorflow::Output() const 

Attrs UseLocking(
  bool x
)