PLearn 0.1
AdaptGradientOptimizer.h
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00001 // -*- C++ -*-
00002 
00003 // PLearn (A C++ Machine Learning Library)
00004 // Copyright (C) 1998 Pascal Vincent
00005 // Copyright (C) 1999-2003 Pascal Vincent, Yoshua Bengio,
00006 //                         Olivier Delalleau and University of Montreal
00007 //
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 
00040 /* *******************************************************      
00041  * $Id: AdaptGradientOptimizer.h 4774 2006-01-10 20:05:24Z tihocan $
00042  * This file is part of the PLearn library.
00043  ******************************************************* */
00044 
00045 
00048 #ifndef AdaptGradientOptimizer_INC
00049 #define AdaptGradientOptimizer_INC
00050 
00051 #include "Optimizer.h"
00052 
00053 namespace PLearn {
00054 using namespace std;
00055 
00056 
00064 class AdaptGradientOptimizer : public Optimizer
00065 {
00066     typedef Optimizer inherited;
00067       
00068 public:
00069 
00070 
00071     int adapt_every;    
00072 
00073     real adapt_coeff1;  
00074     real adapt_coeff2;  
00075 
00076     real decrease_constant;
00077 
00080     real learning_rate; // current learning rate
00081 
00087     int learning_rate_adaptation;
00088 
00089     real max_learning_rate;  
00090     real min_learning_rate;  
00091 
00092     int mini_batch;
00093 
00094     real start_learning_rate; 
00095 
00096 private:
00097 
00098     bool stochastic_hack; // true when we're computing a stochastic gradient
00099     Vec learning_rates;   // used to store the individual learning rates
00100     Vec gradient;         // used to store the gradient
00101     Vec tmp_storage;      // used to store various stuff
00102     // used to store the previous weights evolution, it can be used to
00103     // see how many times a weight has increased / decreased consecutively
00104     Vec old_evol;
00105     Array<Mat> oldgradientlocations; // used for the stochastic hack
00106     Vec store_var_grad;     // used to store the gradient variance
00107     Vec store_grad;         // used to store the gradient
00108     Vec store_quad_grad;    // used to store the gradient^2
00109     int count_updates;      // used to count how many examples we went through
00110 
00111 public: 
00112 
00113     AdaptGradientOptimizer();
00114       
00115     PLEARN_DECLARE_OBJECT(AdaptGradientOptimizer);
00116     virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies) { inherited::makeDeepCopyFromShallowCopy(copies); }
00117 
00118     virtual void build()
00119     {
00120         inherited::build();
00121         build_();
00122     }
00123 
00124 private:
00125 
00126     void build_();
00127     
00128 public:
00129 
00130     virtual real optimize();
00131     virtual bool optimizeN(VecStatsCollector& stats_coll);
00132 
00133 private:
00134 
00135     // Basic learning rate adaptation
00136     // If grad(i) > 0 : lr(i) = lr(i) + lr(i) * adapt_coeff1
00137     // else           : lr(i) = lr(i) - lr(i) * adapt_coeff2
00138     void adaptLearningRateBasic(
00139         Vec old_params,
00140         Vec new_evol);
00141 
00142     // ALAP1 formula learning rate adaptation
00143     // lr = lr + adapt_coeff1 * dot(grad(k-1), grad(k))
00144     // NB: has not been tested
00145     void adaptLearningRateALAP1(
00146         Vec old_gradient,
00147         Vec new_gradient);
00148 
00149     // Learning rate adaptation depending on the variance :
00150     // If var(i) is low, lr(i) = max_learning_rate
00151     // else              lr(i) = min_learning_rate
00152     void adaptLearningRateVariance();
00153 
00154 protected:
00155 
00156     static void declareOptions(OptionList& ol);
00157 
00158 };
00159 
00160 } // end of namespace PLearn
00161 
00162 #endif
00163 
00164 
00165 /*
00166   Local Variables:
00167   mode:c++
00168   c-basic-offset:4
00169   c-file-style:"stroustrup"
00170   c-file-offsets:((innamespace . 0)(inline-open . 0))
00171   indent-tabs-mode:nil
00172   fill-column:79
00173   End:
00174 */
00175 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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