SigmoidVariable.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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/* *******************************************************      
   * $Id: SigmoidVariable.cc,v 1.6 2004/04/27 16:02:26 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "SigmoidVariable.h"
#include "Var_operators.h"

namespace PLearn {
using namespace std;


PLEARN_IMPLEMENT_OBJECT(SigmoidVariable,
                        "ONE LINE DESCR",
                        "NO HELP");

SigmoidVariable::SigmoidVariable(Variable* input) 
  : inherited(input, input->length(), input->width())
{}

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

void SigmoidVariable::fprop()
{
  int l = nelems();
  real* valueptr = valuedata;
  real* inputvalueptr = input->valuedata;
  for(int i=0; i<l; i++)
    *valueptr++ = sigmoid(*inputvalueptr++);
}


void SigmoidVariable::bprop()
{
  int l = nelems();
  real* inputgradientptr = input->gradientdata;
  real* gradientptr = gradientdata;
  real* valueptr = valuedata;
  for(int i=0; i<l; i++)
    {
      real val = *valueptr++;
      *inputgradientptr++ += *gradientptr++ * val*(1.0-val);
    }
}


void SigmoidVariable::bbprop()
{
  if (input->diaghessian.length()==0)
    input->resizeDiagHessian();
  for(int i=0; i<nelems(); i++)
  {
    real yi = valuedata[i];
    real fprime = yi*(1-yi);
    input->gradientdata[i] += gradientdata[i] * fprime * fprime;
  }
}


void SigmoidVariable::symbolicBprop()
{
  Var v(this);
  input->accg(g*v*(1. - v));
}


// R{sigmoid(x)} = f(x)(1-f(x))R(x)
void SigmoidVariable::rfprop()
{
  if (rValue.length()==0) resizeRValue();
  int l = nelems();
  real* inputptr = input->rvaluedata;
  real* inputvalueptr = valuedata;
  real* ptr = rvaluedata;
  for(int i=0; i<l; i++)
  {
    real val = *inputvalueptr++;
    *ptr++ = *inputptr++ * val * (1.0 - val);
  }
}

Var softmax(Var x1, Var x2, Var hardness)
{
  Var w=sigmoid(hardness*(x1-x2));
  return x1*w + x2*(1-w);
}


} // end of namespace PLearn

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