LogAddVariable.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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// 
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/* *******************************************************      
   * $Id: LogAddVariable.cc,v 1.7 2004/04/27 15:58:16 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "ExpVariable.h"
#include "LogAddVariable.h"
#include "Var_operators.h"
//#include "Var_utils.h"

namespace PLearn {
using namespace std;



PLEARN_IMPLEMENT_OBJECT(LogAddVariable,
                        "output = log(exp(input1)+exp(input2)) but it is "
                        "computed in such a way as to preserve precision",
                        "NO HELP");

LogAddVariable::LogAddVariable(Variable* input1, Variable* input2)
  : inherited(input1, input2, input1->length(), input1->width())
{
    build_();
}

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

void
LogAddVariable::build_()
{
    if (input1 && input2) {
        if (input1->length() != input2->length()  ||  input1->width() != input2->width())
            PLERROR("PLogPVariable LogAddVariable input1 and input2 must have the same size");
    }
}

void LogAddVariable::recomputeSize(int& l, int& w) const
{
    if (input1) {
        l = input1->length();
        w = input1->width();
    } else
        l = w = 0;
}

void LogAddVariable::fprop()
{
  apply(input1->value,input2->value,value,logadd);
}


void LogAddVariable::bprop()
{
  Vec grad1(nelems());
  grad1 = input1->value - value;
  apply(grad1, grad1, safeexp);
  input1->gradient += grad1%gradient;

  Vec grad2(nelems());
  grad2 = input2->value - value;
  apply(grad2, grad2, safeexp);
  input2->gradient += grad2%gradient;
}

void LogAddVariable::symbolicBprop()
{
  input1->accg(g * (exp(input1)/(exp(input1)+exp(input2))));
  input2->accg(g * (exp(input2)/(exp(input1)+exp(input2))));
}



} // end of namespace PLearn

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