ScatterUpdate

tensorflow C++ API

tensorflow::ops::ScatterUpdate

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.

ScatterUpdate block

Source link : https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_state.cpp

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input ref: connect Input node.
• Input indices: connect Input node.
• Input updates: connect Input node.
• ScatterUpdate::Attrs attrs : Input attrs in value. ex)use_locking_ = true;

Return:

• Output output : Output object of ScatterUpdate class object.

Result:

• std::vector(Tensor) product_result : Returned object of executed result by calling session.

Using Method

*Assign에 연결된 ClientSession은 Assign값을 확인하기 위해 작성된 temp입니다. ClientSession은 처리순서가 있으므로 테스트시 생성을 마지막에 작성하여 사용합니다