tensorflow::ops::ScatterUpdate

#include <state_ops.h>

Applies sparse updates to a variable reference.

Summary

This operation computes

```python Scalar indices

ref[indices, ...] = updates[...]

Vector indices (for each i)

ref[indices[i], ...] = updates[i, ...]

High rank indices (for each i, ..., j)

ref[indices[i, ..., j], ...] = updates[i, ..., j, ...] ```

This operation outputs ref after the update is done. This makes it easier to chain operations that need to use the reset value.

If values in ref is to be updated more than once, because there are duplicate entries in indices, the order at which the updates happen for each value is undefined.

Requires updates.shape = indices.shape + ref.shape[1:].

Arguments:

• scope: A Scope object
• ref: Should be from a Variable node.
• indices: A tensor of indices into the first dimension of ref.
• updates: A tensor of updated values to store in ref.

Optional attributes (see Attrs):

• use_locking: 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

::tensorflow::Output output_ref

Public functions

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

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

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const 

Public static functions

Attrs UseLocking(
  bool x
)