OneHotSquaredLoss.cc

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// -*- C++ -*-

// PLearn (A C++ Machine Learning Library)
// Copyright (C) 1998 Pascal Vincent
// Copyright (C) 1999-2002 Pascal Vincent, Yoshua Bengio, Rejean Ducharme and University of Montreal
// Copyright (C) 2001-2002 Nicolas Chapados, Ichiro Takeuchi, Jean-Sebastien Senecal
// Copyright (C) 2002 Xiangdong Wang, Christian Dorion

// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
// 
//  1. Redistributions of source code must retain the above copyright
//     notice, this list of conditions and the following disclaimer.
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//     products derived from this software without specific prior written
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/* *******************************************************      
   * $Id: OneHotSquaredLoss.cc,v 1.6 2004/04/27 16:03:35 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "OneHotSquaredLoss.h"
#include "RowAtPositionVariable.h"
#include "Var_operators.h"
//#include "Var_utils.h"

namespace PLearn {
using namespace std;


PLEARN_IMPLEMENT_OBJECT(OneHotSquaredLoss,
                        "Computes sum(square_i(netout[i]-(i==classnum ?hotval :coldval))",
                        "NO HELP");

OneHotSquaredLoss::OneHotSquaredLoss(Variable* netout, Variable* classnum, real coldval, real hotval)
  : inherited(netout,classnum,1,1), coldval_(coldval), hotval_(hotval)
{
    build_();
}

void
OneHotSquaredLoss::build()
{
    inherited::build();
    build_();
}

void
OneHotSquaredLoss::build_()
{
    // input2 is classnum from constructor
    if(input2 && !input2->isScalar())
        PLERROR("In OneHotSquaredLoss: classnum must be a scalar variable representing an index of netout (typically a classnum)");
}

void
OneHotSquaredLoss::declareOptions(OptionList &ol)
{
    declareOption(ol, "coldval_", &OneHotSquaredLoss::coldval_, OptionBase::buildoption, "");
    declareOption(ol, "hotval_", &OneHotSquaredLoss::hotval_, OptionBase::buildoption, "");
    inherited::declareOptions(ol);
}

void OneHotSquaredLoss::recomputeSize(int& l, int& w) const
{ l=1, w=1; }
  
void OneHotSquaredLoss::fprop()
{
  real* netout = input1->valuedata;
  int n = input1->value.size();
  int classnum = (int) input2->valuedata[0];
  real res = 0.;
  for(int i=0; i<n; i++)
    res += square(*netout++ - (i==classnum ? hotval_ : coldval_));
  *valuedata = res;
}


void OneHotSquaredLoss::bprop()
{
  real gr = *gradientdata;
  real* netout = input1->valuedata;
  int n = input1->value.size();
  int classnum = (int) input2->valuedata[0];
  real* input1grptr = input1->gradientdata;
  if(gr!=1.)
  {
    gr = gr+gr;
    for(int i=0; i<n; i++)
      *input1grptr++ += gr*(*netout++ - (i==classnum ? hotval_ : coldval_));
  }
  else // specialised version for gr==1
  {
    for(int i=0; i<n; i++)
      input1->gradientdata[i] += two(netout[i] - (i==classnum ? hotval_ : coldval_));        
  }
}


void OneHotSquaredLoss::symbolicBprop()
{
  Var gi =  g*(input1 - coldval_);
  Var gindex = new RowAtPositionVariable(g*(coldval_-hotval_), input2, input1->length());
  Var ginput = gi + gindex;
  input1->accg(ginput+ginput); //2*gi
}


void OneHotSquaredLoss::rfprop()
{
  int n = input1->value.size();
  int classnum = (int) input2->valuedata[0];
  real sum = 0;
  for (int i=0; i<n; i++)
       sum += 2 * input1->rvaluedata[i] * (input1->valuedata[i] - (i==classnum ? hotval_ : coldval_));
  rvaluedata[0] = sum;
}



} // end of namespace PLearn

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