Link Search Menu Expand Document

ApplyAdagradDA


tensorflow C++ API

tensorflow::ops::ApplyAdagradDA

Update ‘*var’ according to the proximal adagrad scheme.


Summary

Arguments:

  • scope: A Scope object
  • var: Should be from a Variable().
  • gradient_accumulator: Should be from a Variable().
  • gradient_squared_accumulator: Should be from a Variable().
  • grad: The gradient.
  • lr: Scaling factor. Must be a scalar.
  • l1: L1 regularization. Must be a scalar.
  • l2: L2 regularization. Must be a scalar.
  • global_step: Training step number. 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:


ApplyAdagradDA 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 gradient_accumulator: connect Input node.
  • Input gradient_squared_accumulator: connect Input node.
  • Input grad: connect Input node.
  • Input lr: connect Input node.
  • Input l1: connect Input node.
  • Input l2: connect Input node.
  • Input global_step: connect Input node.
  • ApplyAdagradDA::Attrs attrs : Input attrs in value. ex) use_locking_ = false;

Return:

  • Output output : Output object of ApplyAdagradDA class object.

Result:

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

Using Method