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: accumnew=accum+gradgrad

linear+=grad+(accumlrpowernewaccumlrpower/lrvar
quadratic=1.0/(accumlrpowernewlr)+2l2
var=(sign(linear)l1linear)/quadratic if |linear|>l1 else 0.0
accum=accumnew

Args:

  • 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:

Public functions

node() const
::tensorflow::Node *
operator::tensorflow::Input() const
operator::tensorflow::Output() const

Public static functions

MultiplyLinearByLr(bool x)
UseLocking(bool x)

Structs

tensorflow::ops::SparseApplyFtrl::Attrs

Optional attribute setters for SparseApplyFtrl.

Public attributes

operation

Operation operation

out

::tensorflow::Output out

Public functions

SparseApplyFtrl

 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

 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
)

node

::tensorflow::Node * node() const 

operator::tensorflow::Input

 operator::tensorflow::Input() const 

operator::tensorflow::Output

 operator::tensorflow::Output() const 

Public static functions

MultiplyLinearByLr

Attrs MultiplyLinearByLr(
  bool x
)

UseLocking

Attrs UseLocking(
  bool x
)