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:

• Output: Same as “var”.

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