PLearn 0.1
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00001 // -*- C++ -*- 00002 00003 // RBMBinomialLayer.cc 00004 // 00005 // Copyright (C) 2006 Pascal Lamblin & Dan Popovici 00006 // 00007 // Redistribution and use in source and binary forms, with or without 00008 // modification, are permitted provided that the following conditions are met: 00009 // 00010 // 1. Redistributions of source code must retain the above copyright 00011 // notice, this list of conditions and the following disclaimer. 00012 // 00013 // 2. Redistributions in binary form must reproduce the above copyright 00014 // notice, this list of conditions and the following disclaimer in the 00015 // documentation and/or other materials provided with the distribution. 00016 // 00017 // 3. The name of the authors may not be used to endorse or promote 00018 // products derived from this software without specific prior written 00019 // permission. 00020 // 00021 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR 00022 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 00023 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN 00024 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 00025 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED 00026 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 00027 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 00028 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 00029 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 00030 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 00031 // 00032 // This file is part of the PLearn library. For more information on the PLearn 00033 // library, go to the PLearn Web site at www.plearn.org 00034 00035 // Authors: Pascal Lamblin & Dan Popovici 00036 00039 #include "RBMBinomialLayer.h" 00040 #include <plearn/math/TMat_maths.h> 00041 #include "RBMParameters.h" 00042 00043 namespace PLearn { 00044 using namespace std; 00045 00046 PLEARN_IMPLEMENT_OBJECT( 00047 RBMBinomialLayer, 00048 "Layer in an RBM formed with binomial units", 00049 ""); 00050 00051 RBMBinomialLayer::RBMBinomialLayer() 00052 { 00053 } 00054 00055 RBMBinomialLayer::RBMBinomialLayer( int the_size ) 00056 { 00057 size = the_size; 00058 units_types = string( the_size, 'l' ); 00059 activations.resize( the_size ); 00060 sample.resize( the_size ); 00061 expectation.resize( the_size ); 00062 expectation_is_up_to_date = false; 00063 } 00064 00067 void RBMBinomialLayer::getUnitActivations( int i, PP<RBMParameters> rbmp, 00068 int offset ) 00069 { 00070 Vec activation = activations.subVec( i, 1 ); 00071 rbmp->computeUnitActivations( i+offset, 1, activation ); 00072 expectation_is_up_to_date = false; 00073 } 00074 00077 void RBMBinomialLayer::getAllActivations( PP<RBMParameters> rbmp, int offset ) 00078 { 00079 rbmp->computeUnitActivations( offset, size, activations ); 00080 expectation_is_up_to_date = false; 00081 } 00082 00083 void RBMBinomialLayer::generateSample() 00084 { 00085 computeExpectation(); 00086 00087 for( int i=0 ; i<size ; i++ ) 00088 sample[i] = random_gen->binomial_sample( expectation[i] ); 00089 } 00090 00091 void RBMBinomialLayer::computeExpectation() 00092 { 00093 if( expectation_is_up_to_date ) 00094 return; 00095 00096 for( int i=0 ; i<size ; i++ ) 00097 expectation[i] = sigmoid( -activations[i] ); 00098 00099 expectation_is_up_to_date = true; 00100 } 00101 00102 void RBMBinomialLayer::bpropUpdate(const Vec& input, const Vec& output, 00103 Vec& input_gradient, 00104 const Vec& output_gradient) 00105 { 00106 PLASSERT( input.size() == size ); 00107 PLASSERT( output.size() == size ); 00108 PLASSERT( output_gradient.size() == size ); 00109 input_gradient.resize( size ); 00110 00111 for( int i=0 ; i<size ; i++ ) 00112 { 00113 real output_i = output[i]; 00114 input_gradient[i] = - output_i * (1-output_i) * output_gradient[i]; 00115 } 00116 } 00117 00118 00119 00120 void RBMBinomialLayer::declareOptions(OptionList& ol) 00121 { 00122 /* 00123 declareOption(ol, "size", &RBMBinomialLayer::size, 00124 OptionBase::buildoption, 00125 "Number of units."); 00126 */ 00127 // Now call the parent class' declareOptions 00128 inherited::declareOptions(ol); 00129 } 00130 00131 void RBMBinomialLayer::build_() 00132 { 00133 if( size < 0 ) 00134 size = int(units_types.size()); 00135 if( size != (int) units_types.size() ) 00136 units_types = string( size, 'l' ); 00137 00138 activations.resize( size ); 00139 sample.resize( size ); 00140 expectation.resize( size ); 00141 expectation_is_up_to_date = false; 00142 } 00143 00144 void RBMBinomialLayer::build() 00145 { 00146 inherited::build(); 00147 build_(); 00148 } 00149 00150 00151 void RBMBinomialLayer::makeDeepCopyFromShallowCopy(CopiesMap& copies) 00152 { 00153 inherited::makeDeepCopyFromShallowCopy(copies); 00154 } 00155 00156 00157 } // end of namespace PLearn 00158 00159 00160 /* 00161 Local Variables: 00162 mode:c++ 00163 c-basic-offset:4 00164 c-file-style:"stroustrup" 00165 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00166 indent-tabs-mode:nil 00167 fill-column:79 00168 End: 00169 */ 00170 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :