tensorflow::ops::SparseApplyFtrl

#include <training_ops.h>

Update relevant entries in '*var' according to the Ftrl-proximal scheme.

Summary

That is for rows we have grad for, we update var, accum and linear as follows:

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.
• indices: A vector of indices into the first dimension of var and accum.
• lr: Scaling factor. Must be a scalar.
• l1: L1 regularization. Must be a scalar.
• l2: L2 regularization. Must be a scalar.
• lr_power: Scaling factor. Must be a scalar.

Optional attributes (see Attrs):

• 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".

Public attributes

Operation operation

::tensorflow::Output out

Public functions

 SparseApplyFtrl(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input linear,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  ::tensorflow::Input lr,
  ::tensorflow::Input l1,
  ::tensorflow::Input l2,
  ::tensorflow::Input lr_power
)

 SparseApplyFtrl(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input var,
  ::tensorflow::Input accum,
  ::tensorflow::Input linear,
  ::tensorflow::Input grad,
  ::tensorflow::Input indices,
  ::tensorflow::Input lr,
  ::tensorflow::Input l1,
  ::tensorflow::Input l2,
  ::tensorflow::Input lr_power,
  const SparseApplyFtrl::Attrs & attrs
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const 

Public static functions

Attrs MultiplyLinearByLr(
  bool x
)

Attrs UseLocking(
  bool x
)