PLearn 0.1
StackedFocusedAutoassociatorsNet.h
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00001 // -*- C++ -*-
00002 
00003 // StackedFocusedAutoassociatorsNet.h
00004 //
00005 // Copyright (C) 2007 Hugo Larochelle
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
00036 
00040 #ifndef StackedFocusedAutoassociatorsNet_INC
00041 #define StackedFocusedAutoassociatorsNet_INC
00042 
00043 #include <plearn/vmat/ClassSubsetVMatrix.h>
00044 #include <plearn_learners/generic/PLearner.h>
00045 #include <plearn_learners/online/GradNNetLayerModule.h>
00046 #include <plearn_learners/online/OnlineLearningModule.h>
00047 #include <plearn_learners/online/CostModule.h>
00048 #include <plearn_learners/online/ModuleStackModule.h>
00049 #include <plearn_learners/online/NLLCostModule.h>
00050 #include <plearn_learners/online/ClassErrorCostModule.h>
00051 #include <plearn_learners/online/CombiningCostsModule.h>
00052 #include <plearn_learners/online/RBMClassificationModule.h>
00053 #include <plearn_learners/online/RBMLayer.h>
00054 #include <plearn_learners/online/RBMMixedLayer.h>
00055 #include <plearn_learners/online/RBMConnection.h>
00056 #include <plearn_learners/online/SoftmaxModule.h>
00057 #include <plearn/misc/PTimer.h>
00058 
00059 namespace PLearn {
00060 
00067 class StackedFocusedAutoassociatorsNet : public PLearner
00068 {
00069     typedef PLearner inherited;
00070 
00071 public:
00072     //#####  Public Build Options  ############################################
00073 
00075     real cd_learning_rate;
00076     
00078     real cd_decrease_ct;
00079 
00081     real greedy_learning_rate;
00082 
00086     real greedy_decrease_ct;
00087 
00089     real supervised_greedy_learning_rate;
00090 
00092     real supervised_greedy_decrease_ct;
00093 
00095     real fine_tuning_learning_rate;
00096 
00099     real fine_tuning_decrease_ct;
00100 
00103     TVec<int> training_schedule;
00104 
00106     TVec< PP<RBMLayer> > layers;
00107 
00109     TVec< PP<RBMConnection> > connections;
00110 
00112     TVec< PP<RBMConnection> > reconstruction_connections;
00113 
00115     TVec< PP<RBMLayer> > unsupervised_layers;
00116 
00118     TVec< PP<RBMConnection> > unsupervised_connections;
00119 
00122     int k_neighbors;
00123 
00125     int n_classes;
00126 
00128     real dissimilar_example_cost_precision;
00129 
00132     bool do_not_use_knn_classifier;
00133 
00135     real output_weights_l1_penalty_factor;
00136 
00138     real output_weights_l2_penalty_factor;
00139 
00140     //#####  Public Learnt Options  ###########################################
00141 
00143     int n_layers;
00144 
00145 public:
00146     //#####  Public Member Functions  #########################################
00147 
00149     StackedFocusedAutoassociatorsNet();
00150 
00151     //#####  PLearner Member Functions  #######################################
00152 
00155     virtual int outputsize() const;
00156 
00160     virtual void forget();
00161 
00165     virtual void train();
00166 
00168     virtual void computeOutput(const Vec& input, Vec& output) const;
00169 
00171     virtual void computeCostsFromOutputs(const Vec& input, const Vec& output,
00172                                          const Vec& target, Vec& costs) const;
00173 
00178     virtual void updateTrainSetRepresentations() const;
00179 
00182     virtual TVec<std::string> getTestCostNames() const;
00183 
00186     virtual TVec<std::string> getTrainCostNames() const;
00187 
00195     virtual void setTrainingSet(VMat training_set, bool call_forget=true);
00196 
00197     void greedyStep( const Vec& input, const Vec& target, int index, 
00198                      Vec train_costs, int stage, Vec similar_example,
00199                      Vec dissimilar_example);
00200 
00201     void fineTuningStep( const Vec& input, const Vec& target,
00202                          Vec& train_costs, Vec similar_example, 
00203                          Vec dissimilar_example );
00204 
00205     void computeRepresentation( const Vec& input, 
00206                                 Vec& representation, int layer) const;
00207 
00208     //#####  PLearn::Object Protocol  #########################################
00209 
00210     // Declares other standard object methods.
00211     // ### If your class is not instantiatable (it has pure virtual methods)
00212     // ### you should replace this by PLEARN_DECLARE_ABSTRACT_OBJECT_METHODS
00213     PLEARN_DECLARE_OBJECT(StackedFocusedAutoassociatorsNet);
00214 
00215     // Simply calls inherited::build() then build_()
00216     virtual void build();
00217 
00219     // (PLEASE IMPLEMENT IN .cc)
00220     virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies);
00221 
00222 protected:
00223     //#####  Not Options  #####################################################
00224 
00227     mutable TVec<Vec> activations;
00228 
00231     mutable TVec<Vec> expectations;
00232 
00236     mutable TVec<Vec> activation_gradients;
00237 
00241     mutable TVec<Vec> expectation_gradients;
00242 
00244     mutable Vec greedy_activation;
00245 
00247     mutable Vec greedy_expectation;
00248 
00251     mutable Vec greedy_activation_gradient;
00252 
00255     mutable Vec greedy_expectation_gradient;
00256 
00258     mutable Vec reconstruction_activations;
00259     
00261     mutable Vec reconstruction_activation_gradients;
00262 
00264     mutable Vec reconstruction_expectation_gradients;
00265 
00267     TVec< PP<RBMLayer> > greedy_layers;
00268 
00270     TVec< PP<RBMConnection> > greedy_connections;
00271 
00273     Vec similar_example_representation;
00274 
00276     Vec dissimilar_example_representation;
00277 
00279     mutable Vec input_representation;
00280 
00282     Vec previous_input_representation;
00283 
00285     Vec dissimilar_gradient_contribution;
00286 
00288     Vec pos_down_val;
00290     Vec pos_up_val;
00292     Vec neg_down_val;
00294     Vec neg_up_val;
00295 
00297     mutable Vec final_cost_input;
00299     mutable Vec final_cost_value;
00301     mutable Vec final_cost_gradient;
00302 
00304     TVec< PP<ClassSubsetVMatrix> > class_datasets;
00305 
00308     Mat other_classes_proportions;
00309 
00311     TMat<int> nearest_neighbors_indices;
00312 
00314     mutable TVec<int> test_nearest_neighbors_indices;
00315 
00317     TVec<int> test_votes;
00318 
00320     mutable Mat train_set_representations;
00321     mutable VMat train_set_representations_vmat;
00322     mutable TVec<int> train_set_targets;
00323 
00325     mutable bool train_set_representations_up_to_date;
00326 
00328     TVec<int> greedy_stages;
00329 
00332     int currently_trained_layer;
00333 
00335     PP<OnlineLearningModule> final_module;
00336 
00338     PP<CostModule> final_cost;
00339 
00340 protected:
00341     //#####  Protected Member Functions  ######################################
00342 
00344     static void declareOptions(OptionList& ol);
00345 
00346 private:
00347     //#####  Private Member Functions  ########################################
00348 
00350     void build_();
00351 
00352     void build_layers_and_connections();
00353 
00354     void build_output_layer_and_cost();
00355 
00356     void setLearningRate( real the_learning_rate );
00357 
00358 private:
00359     //#####  Private Data Members  ############################################
00360 
00361     // The rest of the private stuff goes here    
00362 };
00363 
00364 // Declares a few other classes and functions related to this class
00365 DECLARE_OBJECT_PTR(StackedFocusedAutoassociatorsNet);
00366 
00367 } // end of namespace PLearn
00368 
00369 #endif
00370 
00371 
00372 /*
00373   Local Variables:
00374   mode:c++
00375   c-basic-offset:4
00376   c-file-style:"stroustrup"
00377   c-file-offsets:((innamespace . 0)(inline-open . 0))
00378   indent-tabs-mode:nil
00379   fill-column:79
00380   End:
00381 */
00382 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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