ScatterMul

tensorflow C++ API

tensorflow::ops::ScatterMul

Multiplies sparse updates into 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.

Duplicate entries are handled correctly: if multiple indices reference the same location, their contributions multiply.

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 multiply to ref.

Optional attributes (see Attrs):

• use_locking: If True, the operation 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.

ScatterMul 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.
• ScatterMul ::Attrs attrs : Input attrs in value. ex)use_locking_ = true;

Return:

• Output output : Output object of ScatterMul 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