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enuSpace-tensorflow Manual

ApplyFtrl

tensorflow C++ API

tensorflow::ops::ApplyFtrl

Update ‘*var’ according to the Ftrl-proximal scheme.

Summary

accum_new = accum + grad * grad linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new

Arguments:

  • scope: A Scope object
  • var: Should be from a Variable().
  • accum: Should be from a Variable().
  • linear: Should be from a Variable().
  • grad: The gradient.
  • lr: Scaling factor. Must be a scalar.
  • l1: L1 regulariation. Must be a scalar.
  • l2: L2 regulariation. Must be a scalar.
  • lr_power: Scaling factor. Must be a scalar.

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:

ApplyFtrl 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 linear: connect Input node.
  • Input grad: connect Input node.
  • Input lr: connect Input node.
  • Input l1: connect Input node.
  • Input l2: connect Input node.
  • Input lr_power: connect Input node.
  • ApplyFtrl ::Attrs attrs : Input attrs in value. ex) use_locking_ = false;

Return:

  • Output output : Output object of ApplyFtrl class object.

Result:

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

Using Method