PLearn 0.1
MatrixSoftmaxLossVariable.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: MatrixSoftmaxLossVariable.cc 3994 2005-08-25 13:35:03Z chapados $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "MatrixSoftmaxLossVariable.h"
00044 
00045 namespace PLearn {
00046 using namespace std;
00047 
00048 
00050 PLEARN_IMPLEMENT_OBJECT(MatrixSoftmaxLossVariable,
00051                         "ONE LINE DESCR",
00052                         "NO HELP");
00053 
00054 MatrixSoftmaxLossVariable::MatrixSoftmaxLossVariable(Variable* input1, Variable* input2) 
00055     : inherited(input1, input2, input2->length(), input2->width())
00056 {
00057     build_();
00058 }
00059 
00060 void
00061 MatrixSoftmaxLossVariable::build()
00062 {
00063     inherited::build();
00064     build_();
00065 }
00066 
00067 void
00068 MatrixSoftmaxLossVariable::build_()
00069 {
00070     if (input2 && !input2->isVec())
00071         PLERROR("In MatrixSoftmaxLossVariable: position must be a vector");
00072 }
00073 
00074 
00075 void MatrixSoftmaxLossVariable::recomputeSize(int& l, int& w) const
00076 {
00077     if (input2) {
00078         l = input2->length();
00079         w = input2->width();
00080     } else
00081         l = w = 0;
00082 }
00083 
00084 
00085 void MatrixSoftmaxLossVariable::fprop()
00086 {
00087     for (int i=0; i<input2->length(); i++)
00088     {
00089         int classnum = (int)input2->valuedata[i];
00090         real input_index = input1->matValue[classnum][i];
00091         real sum=0;
00092         for(int j=0; j<input1->length(); j++)
00093             sum += safeexp(input1->matValue[j][i]-input_index);
00094         valuedata[i] = 1.0/sum;
00095     }
00096 }
00097 
00098 
00099 void MatrixSoftmaxLossVariable::bprop()
00100 {  
00101     for (int i=0; i<input2->length(); i++)
00102     {
00103         int classnum = (int)input2->valuedata[i];
00104         real input_index = input1->matValue[classnum][i];
00105         real vali = valuedata[i];
00106         for(int j=0; j<input1->length(); j++)
00107         {
00108             if (j!=classnum){
00109                 input1->matGradient[j][i] = -gradientdata[i]*vali*vali*safeexp(input1->matValue[j][i]-input_index);}
00110             else
00111                 input1->matGradient[j][i] = gradientdata[i]*vali*(1.-vali);
00112         }
00113     }
00114 }
00115 
00116 
00117 void MatrixSoftmaxLossVariable::bbprop()
00118 {
00119     PLERROR("MatrixSoftmaxLossVariable::bbprop() not implemented");
00120 }
00121 
00122 
00123 void MatrixSoftmaxLossVariable::symbolicBprop()
00124 {
00125     PLERROR("MatrixSoftmaxLossVariable::symbolicBprop() not implemented");
00126 }
00127 
00128 
00129 void MatrixSoftmaxLossVariable::rfprop()
00130 {
00131     PLERROR("MatrixSoftmaxLossVariable::rfprop() not implemented");
00132 }
00133 
00134 
00135 
00136 } // end of namespace PLearn
00137 
00138 
00139 /*
00140   Local Variables:
00141   mode:c++
00142   c-basic-offset:4
00143   c-file-style:"stroustrup"
00144   c-file-offsets:((innamespace . 0)(inline-open . 0))
00145   indent-tabs-mode:nil
00146   fill-column:79
00147   End:
00148 */
00149 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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