PLearn 0.1
|
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: ProductVariable.cc 8957 2008-05-08 19:19:31Z tihocan $ 00040 * This file is part of the PLearn library. 00041 ******************************************************* */ 00042 00043 #include "ProductTransposeVariable.h" 00044 #include "ProductVariable.h" 00045 #include "TransposeProductVariable.h" 00046 //#include "Var_utils.h" 00047 00048 namespace PLearn { 00049 using namespace std; 00050 00051 00054 // Matrix product 00055 00056 PLEARN_IMPLEMENT_OBJECT( 00057 ProductVariable, 00058 "Matrix product.", 00059 ""); 00060 00061 ProductVariable::ProductVariable(Variable* m1, Variable* m2) 00062 : inherited(m1, m2, m1->length(), m2->width()) 00063 { 00064 build_(); 00065 } 00066 00067 void 00068 ProductVariable::build() 00069 { 00070 inherited::build(); 00071 build_(); 00072 } 00073 00074 void 00075 ProductVariable::build_() 00076 { 00077 if (input1 && input2) { 00078 // input1 and input2 are (respectively) m1 and m2 from constructor 00079 if (input1->width() != input2->length()) 00080 PLERROR("In ProductVariable: the size of m1 and m2 are not compatible for a matrix product"); 00081 } 00082 } 00083 00084 00085 void ProductVariable::recomputeSize(int& l, int& w) const 00086 { 00087 if (input1 && input2) { 00088 l = input1->length(); 00089 w = input2->width(); 00090 } else 00091 l = w = 0; 00092 } 00093 00094 void ProductVariable::fprop() 00095 { 00096 // m[i,j] = sum_k input1[i,k] * input2[k,j] 00097 product(matValue, input1->matValue, input2->matValue); 00098 } 00099 00100 00101 void ProductVariable::bprop() 00102 { 00103 // dC/dinput1[i,k] = sum_j dC/dm[i,j] input2[k,j] 00104 productTransposeAcc(input1->matGradient, matGradient, input2->matValue); 00105 // dC/dinput2[k,j] += sum_i dC/dm[i,j] input1[i,k] 00106 transposeProductAcc(input2->matGradient, input1->matValue, matGradient); 00107 } 00108 00109 00110 void ProductVariable::bbprop() 00111 { 00112 if (input1->diaghessian.length()==0) 00113 input1->resizeDiagHessian(); 00114 if (input2->diaghessian.length()==0) 00115 input2->resizeDiagHessian(); 00116 // d^2C/dinput1[i,k]^2 = sum_j d^2C/dm[i,j]^2 input2[k,j]*input2[k,j] 00117 product2TransposeAcc(input1->matGradient, matGradient, input2->matValue); 00118 // dC/dinput2[k,j] += sum_i d^2C/dm[i,j]^2 input1[i,k]*input1[i,k] 00119 transposeProduct2Acc(input2->matGradient, input1->matValue, matGradient); 00120 } 00121 00122 00123 void ProductVariable::symbolicBprop() 00124 { 00125 // dC/dinput1[i,k] = sum_j dC/dm[i,j] input2[k,j] 00126 input1->accg(productTranspose(g, input2)); 00127 // dC/dinput2[k,j] += sum_i dC/dm[i,j] input1[i,k] 00128 input2->accg(transposeProduct(input1, g)); 00129 } 00130 00131 00132 //R(x1x2)=R(x1)x2+x1R(x2) 00133 void ProductVariable::rfprop() 00134 { 00135 if (rValue.length()==0) 00136 resizeRValue(); 00137 product(matRValue, input1->matValue, input2->matRValue); 00138 productAcc(matRValue,input1->matRValue, input2->matValue); 00139 } 00140 00141 00142 00143 } // end of namespace PLearn 00144 00145 00146 /* 00147 Local Variables: 00148 mode:c++ 00149 c-basic-offset:4 00150 c-file-style:"stroustrup" 00151 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00152 indent-tabs-mode:nil 00153 fill-column:79 00154 End: 00155 */ 00156 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :