PLearn 0.1
MatrixSoftmaxVariable.cc
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00001 // -*- C++ -*-
00002 
00003 // PLearn (A C++ Machine Learning Library)
00004 // Copyright (C) 1998 Pascal Vincent
00005 // Copyright (C) 1999-2002 Pascal Vincent, Yoshua Bengio, Rejean Ducharme and University of Montreal
00006 // Copyright (C) 2001-2002 Nicolas Chapados, Ichiro Takeuchi, Jean-Sebastien Senecal
00007 // Copyright (C) 2002 Xiangdong Wang, Christian Dorion
00008 
00009 // Redistribution and use in source and binary forms, with or without
00010 // modification, are permitted provided that the following conditions are met:
00011 // 
00012 //  1. Redistributions of source code must retain the above copyright
00013 //     notice, this list of conditions and the following disclaimer.
00014 // 
00015 //  2. Redistributions in binary form must reproduce the above copyright
00016 //     notice, this list of conditions and the following disclaimer in the
00017 //     documentation and/or other materials provided with the distribution.
00018 // 
00019 //  3. The name of the authors may not be used to endorse or promote
00020 //     products derived from this software without specific prior written
00021 //     permission.
00022 // 
00023 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
00024 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
00025 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
00026 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
00027 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
00028 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
00029 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
00030 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
00031 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00032 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00033 // 
00034 // This file is part of the PLearn library. For more information on the PLearn
00035 // library, go to the PLearn Web site at www.plearn.org
00036 
00037 
00038 /* *******************************************************      
00039  * $Id: MatrixSoftmaxVariable.cc 4313 2005-10-25 13:44:40Z tihocan $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "MatrixSoftmaxVariable.h"
00044 
00045 namespace PLearn {
00046 using namespace std;
00047 
00048 
00051 PLEARN_IMPLEMENT_OBJECT(MatrixSoftmaxVariable,
00052                         "ONE LINE DESCR",
00053                         "NO HELP");
00054 
00055 MatrixSoftmaxVariable::MatrixSoftmaxVariable(Variable* input) 
00056     : inherited(input, input->length(), input->width())
00057 {}
00058 
00059 void MatrixSoftmaxVariable::recomputeSize(int& l, int& w) const
00060 {
00061     if (input) {
00062         l = input->length();
00063         w = input->width();
00064     } else
00065         l = w = 0;
00066 }
00067 
00068 
00069 void MatrixSoftmaxVariable::fprop()
00070 {
00071     Vec column_max(width());
00072     columnMax(input->matValue, column_max);
00073 
00074     for(int j=0; j<input->width(); j++)
00075     {
00076         real s = 0;
00077         real curmax = column_max[j];
00078         for(int i=0; i<input->length(); i++)      
00079             s += (matValue[i][j] = safeexp(input->matValue[i][j]-curmax));
00080         if (fast_exact_is_equal(s, 0)) PLERROR("trying to divide by 0 in softmax");
00081         s = 1.0 / s;
00082         for(int i=0; i<input->length(); i++)
00083             matValue[i][j] *= s;
00084     }
00085 }
00086 
00087 
00088 void MatrixSoftmaxVariable::bprop()
00089 {
00090     for(int i=0; i<input->width(); i++)
00091         for(int j=0; j<input->length(); j++)
00092         {
00093             real vali = matValue[j][i];
00094             for(int k=0; k<length(); k++)
00095             {
00096                 if(k!=j)
00097                     input->matGradient[j][i] -= matGradient[k][i]*vali*matValue[k][i];
00098                 else
00099                     input->matGradient[j][i] += matGradient[j][i]*vali*(1.-vali);
00100             }
00101         }
00102 }
00103 
00104 
00105 void MatrixSoftmaxVariable::bbprop()
00106 {
00107     PLERROR("MatrixSofmaxVariable::bbprop() not implemented");
00108 }
00109 
00110 
00111 void MatrixSoftmaxVariable::symbolicBprop()
00112 {
00113     PLERROR("MatrixSofmaxVariable::symbolicBprop() not implemented");
00114 }
00115 
00116 
00117 // 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)
00118 void MatrixSoftmaxVariable::rfprop()
00119 {
00120     PLERROR("SofmaxVariable::rfprop() not implemented");
00121 }
00122 
00123 
00124 
00125 } // end of namespace PLearn
00126 
00127 
00128 /*
00129   Local Variables:
00130   mode:c++
00131   c-basic-offset:4
00132   c-file-style:"stroustrup"
00133   c-file-offsets:((innamespace . 0)(inline-open . 0))
00134   indent-tabs-mode:nil
00135   fill-column:79
00136   End:
00137 */
00138 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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