ApplyAdagrad

tensorflow C++ API

tensorflow::ops::ApplyAdagrad

Update ‘*var’ according to the adagrad scheme.

Summary

accum += grad * grad var -= lr * grad * (1 / sqrt(accum))

Arguments:

• scope: A Scope object
• var: Should be from a Variable().
• accum: Should be from a Variable().
• lr: Scaling factor. Must be a scalar.
• grad: The gradient.

Optional attributes (seeAttrs):

• use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.

Returns:

• Output: Same as “var”.

ApplyAdagrad block

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

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input var: connect Input node.
• Input accum: connect Input node.
• Input lr: connect Input node.
• Input grad: connect Input node.
• ApplyAdagrad ::Attrs attrs : Input attrs in value. ex) use_locking_ = false;

Return:

• Output output : Output object of ApplyAdagrad class object.

Result:

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

Using Method