MatrixSoftmaxVariable.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

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// modification, are permitted provided that the following conditions are met:
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//  1. Redistributions of source code must retain the above copyright
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/* *******************************************************      
   * $Id: MatrixSoftmaxVariable.cc,v 1.5 2004/04/27 15:58:16 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "MatrixSoftmaxVariable.h"

namespace PLearn {
using namespace std;


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

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

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


void MatrixSoftmaxVariable::fprop()
{
  Vec column_max(width());
  columnMax(input->matValue, column_max);

  for(int j=0; j<input->width(); j++)
    {
    real s = 0;
    real curmax = column_max[j];
    for(int i=0; i<input->length(); i++)      
      s += (matValue[i][j] = safeexp(input->matValue[i][j]-curmax));
    if (s == 0) PLERROR("trying to divide by 0 in softmax");
    s = 1.0 / s;
    for(int i=0; i<input->length(); i++)
      matValue[i][j] *= s;
    }
}


void MatrixSoftmaxVariable::bprop()
{
  for(int i=0; i<input->width(); i++)
    for(int j=0; j<input->length(); j++)
      {
       real vali = matValue[j][i];
       for(int k=0; k<length(); k++)
         {
          if(k!=j)
            input->matGradient[j][i] -= matGradient[k][i]*vali*matValue[k][i];
          else
            input->matGradient[j][i] += matGradient[j][i]*vali*(1.-vali);
         }
       }
}


void MatrixSoftmaxVariable::bbprop()
{
  PLERROR("MatrixSofmaxVariable::bbprop() not implemented");
}


void MatrixSoftmaxVariable::symbolicBprop()
{
  PLERROR("MatrixSofmaxVariable::symbolicBprop() not implemented");
}


// R{ s_i = exp(x_i) / sum_j exp(x_j) }   = (s_i(1-s_i) - sum_{k!=i} s_i s_k) R(s_i) = s_i ((1-s_i) - sum_{k!=i} s_k) R(s_i)
void MatrixSoftmaxVariable::rfprop()
{
  PLERROR("SofmaxVariable::rfprop() not implemented");
}



} // end of namespace PLearn

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