svrg.cc File Reference

#include <cassert>
#include <iostream>
#include "gd.h"
#include "vw.h"
#include "reductions.h"

Go to the source code of this file.

Classes
struct	SVRG::svrg
struct	SVRG::update

Namespaces
	SVRG

Macros
#define	W_INNER   0
#define	W_STABLE   1
#define	W_STABLEGRAD   2

Functions
template<int offset>
void	SVRG::vec_add (float &p, const float x, float &w)
template<int offset>
float	SVRG::inline_predict (vw &all, example &ec)
float	SVRG::predict_stable (const svrg &s, example &ec)
void	SVRG::predict (svrg &s, single_learner &, example &ec)
float	SVRG::gradient_scalar (const svrg &s, const example &ec, float pred)
void	SVRG::update_inner_feature (update &u, float x, float &w)
void	SVRG::update_stable_feature (float &g_scalar, float x, float &w)
void	SVRG::update_inner (const svrg &s, example &ec)
void	SVRG::update_stable (const svrg &s, example &ec)
void	SVRG::learn (svrg &s, single_learner &base, example &ec)
void	SVRG::save_load (svrg &s, io_buf &model_file, bool read, bool text)
base_learner *	svrg_setup (options_i &options, vw &all)

Macro Definition Documentation

W_INNER

#define W_INNER   0

Definition at line 14 of file svrg.cc.

Referenced by SVRG::learn().

W_STABLE

#define W_STABLE   1

Definition at line 15 of file svrg.cc.

Referenced by SVRG::learn().

W_STABLEGRAD

#define W_STABLEGRAD   2

Definition at line 16 of file svrg.cc.

Referenced by SVRG::learn(), SVRG::update_inner_feature(), and SVRG::update_stable_feature().

Function Documentation

svrg_setup()

base_learner* svrg_setup	(	options_i &	options,
		vw &	all
	)

Definition at line 168 of file svrg.cc.

References VW::config::option_group_definition::add(), VW::config::options_i::add_and_parse(), LEARNER::init_learner(), SVRG::learn(), LEARNER::make_base(), VW::config::make_option(), SVRG::predict(), SVRG::save_load(), LEARNER::learner< T, E >::set_save_load(), parameters::stride_shift(), UINT64_ONE, and vw::weights.

Referenced by parse_reductions().

{
  auto s = scoped_calloc_or_throw<svrg>();

  bool svrg_option = false;
  option_group_definition new_options("Stochastic Variance Reduced Gradient");
  new_options.add(make_option("svrg", svrg_option).keep().help("Streaming Stochastic Variance Reduced Gradient"))
      .add(make_option("stage_size", s->stage_size).default_value(1).help("Number of passes per SVRG stage"));
  options.add_and_parse(new_options);

  if (!svrg_option)
  {
    return nullptr;
  }

  s->all = &all;
  s->prev_pass = -1;
  s->stable_grad_count = 0;

  // Request more parameter storage (4 floats per feature)
  all.weights.stride_shift(2);
  learner<svrg, example>& l = init_learner(s, learn, predict, UINT64_ONE << all.weights.stride_shift());
  l.set_save_load(save_load);
  return make_base(l);
}