ApplyProximalGradientDescent

tensorflow C++ API

tensorflow::ops::ApplyProximalGradientDescent

Update '*var' as FOBOS algorithm with fixed learning rate.

Summary

prox_v = var - alpha * delta var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}

Arguments:

  • scope: A Scope object
  • var: Should be from a Variable().
  • alpha: Scaling factor. Must be a scalar.
  • l1: L1 regularization. Must be a scalar.
  • l2: L2 regularization. Must be a scalar.
  • delta: The change.

Optional attributes (seeAttrs):

  • use_locking: If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.

Returns:

ApplyProximalGradientDescent 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 alpha: connect Input node.
  • Input l1: connect Input node.
  • Input l2: connect Input node.
  • Input delta: connect Input node.
  • ApplyProximalGradientDescent ::Attrs attrs : Input attrs in value. ex) use_locking_ = false;

Return:

  • Output output : Output object of ApplyProximalGradientDescent class object.

Result:

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

Using Method

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