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:

$$accum_new = accum + grad * grad$$
$$linear += grad + (accum_{new}^{-lr_{power} } - accum^{-lr_{power} } / lr * var$$
$$quadratic = 1.0 / (accum_{new}^{lr_{power} } * lr) + 2 * l2$$
$$var = (sign(linear) * l1 - linear) / quadratic\ if\ |linear| > l1\ else\ 0.0$$
$$accum = accum_{new}$$

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:

Constructors and Destructors

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)

Public attributes

operation
out

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
)