/usr/include/mlptrain.h is in libalglib-dev 2.6.0-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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Copyright (c) 2007-2008, Sergey Bochkanov (ALGLIB project).
>>> SOURCE LICENSE >>>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation (www.fsf.org); either version 2 of the
License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
A copy of the GNU General Public License is available at
http://www.fsf.org/licensing/licenses
>>> END OF LICENSE >>>
*************************************************************************/
#ifndef _mlptrain_h
#define _mlptrain_h
#include "ap.h"
#include "ialglib.h"
#include "mlpbase.h"
#include "reflections.h"
#include "creflections.h"
#include "hqrnd.h"
#include "matgen.h"
#include "ablasf.h"
#include "ablas.h"
#include "trfac.h"
#include "trlinsolve.h"
#include "safesolve.h"
#include "rcond.h"
#include "matinv.h"
#include "linmin.h"
#include "minlbfgs.h"
#include "hblas.h"
#include "sblas.h"
#include "ortfac.h"
#include "blas.h"
#include "rotations.h"
#include "bdsvd.h"
#include "svd.h"
#include "xblas.h"
#include "densesolver.h"
/*************************************************************************
Training report:
* NGrad - number of gradient calculations
* NHess - number of Hessian calculations
* NCholesky - number of Cholesky decompositions
*************************************************************************/
struct mlpreport
{
int ngrad;
int nhess;
int ncholesky;
};
/*************************************************************************
Cross-validation estimates of generalization error
*************************************************************************/
struct mlpcvreport
{
double relclserror;
double avgce;
double rmserror;
double avgerror;
double avgrelerror;
};
/*************************************************************************
Neural network training using modified Levenberg-Marquardt with exact
Hessian calculation and regularization. Subroutine trains neural network
with restarts from random positions. Algorithm is well suited for small
and medium scale problems (hundreds of weights).
INPUT PARAMETERS:
Network - neural network with initialized geometry
XY - training set
NPoints - training set size
Decay - weight decay constant, >=0.001
Decay term 'Decay*||Weights||^2' is added to error
function.
If you don't know what Decay to choose, use 0.001.
Restarts - number of restarts from random position, >0.
If you don't know what Restarts to choose, use 2.
OUTPUT PARAMETERS:
Network - trained neural network.
Info - return code:
* -9, if internal matrix inverse subroutine failed
* -2, if there is a point with class number
outside of [0..NOut-1].
* -1, if wrong parameters specified
(NPoints<0, Restarts<1).
* 2, if task has been solved.
Rep - training report
-- ALGLIB --
Copyright 10.03.2009 by Bochkanov Sergey
*************************************************************************/
void mlptrainlm(multilayerperceptron& network,
const ap::real_2d_array& xy,
int npoints,
double decay,
int restarts,
int& info,
mlpreport& rep);
/*************************************************************************
Neural network training using L-BFGS algorithm with regularization.
Subroutine trains neural network with restarts from random positions.
Algorithm is well suited for problems of any dimensionality (memory
requirements and step complexity are linear by weights number).
INPUT PARAMETERS:
Network - neural network with initialized geometry
XY - training set
NPoints - training set size
Decay - weight decay constant, >=0.001
Decay term 'Decay*||Weights||^2' is added to error
function.
If you don't know what Decay to choose, use 0.001.
Restarts - number of restarts from random position, >0.
If you don't know what Restarts to choose, use 2.
WStep - stopping criterion. Algorithm stops if step size is
less than WStep. Recommended value - 0.01. Zero step
size means stopping after MaxIts iterations.
MaxIts - stopping criterion. Algorithm stops after MaxIts
iterations (NOT gradient calculations). Zero MaxIts
means stopping when step is sufficiently small.
OUTPUT PARAMETERS:
Network - trained neural network.
Info - return code:
* -8, if both WStep=0 and MaxIts=0
* -2, if there is a point with class number
outside of [0..NOut-1].
* -1, if wrong parameters specified
(NPoints<0, Restarts<1).
* 2, if task has been solved.
Rep - training report
-- ALGLIB --
Copyright 09.12.2007 by Bochkanov Sergey
*************************************************************************/
void mlptrainlbfgs(multilayerperceptron& network,
const ap::real_2d_array& xy,
int npoints,
double decay,
int restarts,
double wstep,
int maxits,
int& info,
mlpreport& rep);
/*************************************************************************
Neural network training using early stopping (base algorithm - L-BFGS with
regularization).
INPUT PARAMETERS:
Network - neural network with initialized geometry
TrnXY - training set
TrnSize - training set size
ValXY - validation set
ValSize - validation set size
Decay - weight decay constant, >=0.001
Decay term 'Decay*||Weights||^2' is added to error
function.
If you don't know what Decay to choose, use 0.001.
Restarts - number of restarts from random position, >0.
If you don't know what Restarts to choose, use 2.
OUTPUT PARAMETERS:
Network - trained neural network.
Info - return code:
* -2, if there is a point with class number
outside of [0..NOut-1].
* -1, if wrong parameters specified
(NPoints<0, Restarts<1, ...).
* 2, task has been solved, stopping criterion met -
sufficiently small step size. Not expected (we
use EARLY stopping) but possible and not an
error.
* 6, task has been solved, stopping criterion met -
increasing of validation set error.
Rep - training report
NOTE:
Algorithm stops if validation set error increases for a long enough or
step size is small enought (there are task where validation set may
decrease for eternity). In any case solution returned corresponds to the
minimum of validation set error.
-- ALGLIB --
Copyright 10.03.2009 by Bochkanov Sergey
*************************************************************************/
void mlptraines(multilayerperceptron& network,
const ap::real_2d_array& trnxy,
int trnsize,
const ap::real_2d_array& valxy,
int valsize,
double decay,
int restarts,
int& info,
mlpreport& rep);
/*************************************************************************
Cross-validation estimate of generalization error.
Base algorithm - L-BFGS.
INPUT PARAMETERS:
Network - neural network with initialized geometry. Network is
not changed during cross-validation - it is used only
as a representative of its architecture.
XY - training set.
SSize - training set size
Decay - weight decay, same as in MLPTrainLBFGS
Restarts - number of restarts, >0.
restarts are counted for each partition separately, so
total number of restarts will be Restarts*FoldsCount.
WStep - stopping criterion, same as in MLPTrainLBFGS
MaxIts - stopping criterion, same as in MLPTrainLBFGS
FoldsCount - number of folds in k-fold cross-validation,
2<=FoldsCount<=SSize.
recommended value: 10.
OUTPUT PARAMETERS:
Info - return code, same as in MLPTrainLBFGS
Rep - report, same as in MLPTrainLM/MLPTrainLBFGS
CVRep - generalization error estimates
-- ALGLIB --
Copyright 09.12.2007 by Bochkanov Sergey
*************************************************************************/
void mlpkfoldcvlbfgs(const multilayerperceptron& network,
const ap::real_2d_array& xy,
int npoints,
double decay,
int restarts,
double wstep,
int maxits,
int foldscount,
int& info,
mlpreport& rep,
mlpcvreport& cvrep);
/*************************************************************************
Cross-validation estimate of generalization error.
Base algorithm - Levenberg-Marquardt.
INPUT PARAMETERS:
Network - neural network with initialized geometry. Network is
not changed during cross-validation - it is used only
as a representative of its architecture.
XY - training set.
SSize - training set size
Decay - weight decay, same as in MLPTrainLBFGS
Restarts - number of restarts, >0.
restarts are counted for each partition separately, so
total number of restarts will be Restarts*FoldsCount.
FoldsCount - number of folds in k-fold cross-validation,
2<=FoldsCount<=SSize.
recommended value: 10.
OUTPUT PARAMETERS:
Info - return code, same as in MLPTrainLBFGS
Rep - report, same as in MLPTrainLM/MLPTrainLBFGS
CVRep - generalization error estimates
-- ALGLIB --
Copyright 09.12.2007 by Bochkanov Sergey
*************************************************************************/
void mlpkfoldcvlm(const multilayerperceptron& network,
const ap::real_2d_array& xy,
int npoints,
double decay,
int restarts,
int foldscount,
int& info,
mlpreport& rep,
mlpcvreport& cvrep);
#endif
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