PLearn 0.1
TorchLearner.cc
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00001 // -*- C++ -*-
00002 
00003 // TorchLearner.cc
00004 //
00005 // Copyright (C) 2005 Olivier Delalleau 
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 /* *******************************************************      
00036  * $Id: TorchLearner.cc 6351 2006-10-25 19:05:45Z chapados $ 
00037  ******************************************************* */
00038 
00039 // Authors: Olivier Delalleau
00040 
00044 #include "TorchLearner.h"
00045 #include <plearn_torch/TMachine.h>
00046 #include <plearn_torch/TTorchDataSetFromVMat.h>
00047 #include <plearn_torch/TTrainer.h>
00048 
00049 namespace PLearn {
00050 using namespace std;
00051 
00053 // TorchLearner //
00055 TorchLearner::TorchLearner() 
00056     : outputsize_(-1)
00057 {
00058     allocator = new Torch::Allocator;
00059     inputs = 0;
00060 }
00061 
00062 PLEARN_IMPLEMENT_OBJECT(TorchLearner,
00063                         "A generic learner that can use Torch learning algorithms.",
00064                         ""
00065     );
00066 
00068 // declareOptions //
00070 void TorchLearner::declareOptions(OptionList& ol)
00071 {
00072     // ### For the "flags" of each option, you should typically specify  
00073     // ### one of OptionBase::buildoption, OptionBase::learntoption or 
00074     // ### OptionBase::tuningoption. Another possible flag to be combined with
00075     // ### is OptionBase::nosave
00076 
00077     // Build options.
00078 
00079     declareOption(ol, "machine", &TorchLearner::machine, OptionBase::buildoption,
00080                   "The Torch learning machine.");
00081 
00082     declareOption(ol, "trainer", &TorchLearner::trainer, OptionBase::buildoption,
00083                   "The Torch trainer, responsible for training the machine.");
00084 
00085     // Learnt options.
00086 
00087     declareOption(ol, "outputsize", &TorchLearner::outputsize_, OptionBase::learntoption,
00088                   "Saves the output size of this learner for faster access.");
00089 
00090     // Now call the parent class' declareOptions.
00091     inherited::declareOptions(ol);
00092 
00093     // Hide unused parent's options.
00094 
00095     redeclareOption(ol, "seed", &TorchLearner::seed_, OptionBase::nosave,
00096                     "Torch learners in general will not use the PLearn seed.");
00097 
00098     redeclareOption(ol, "nstages", &TorchLearner::nstages, OptionBase::nosave,
00099                     "A Torch learner is usually only trained on one stage.");
00100 
00101 }
00102 
00104 // build //
00106 void TorchLearner::build()
00107 {
00108     inherited::build();
00109     build_();
00110 }
00111 
00113 // build_ //
00115 void TorchLearner::build_()
00116 {
00117     // ### This method should do the real building of the object,
00118     // ### according to set 'options', in *any* situation. 
00119     // ### Typical situations include:
00120     // ###  - Initial building of an object from a few user-specified options
00121     // ###  - Building of a "reloaded" object: i.e. from the complete set of all serialised options.
00122     // ###  - Updating or "re-building" of an object after a few "tuning" options have been modified.
00123     // ### You should assume that the parent class' build_() has already been called.
00124     if (machine && machine->machine->outputs)
00125         outputsize_ = machine->machine->outputs->frame_size;
00126     // Initialize the inputs sequence.
00127     if (inputsize_ >= 0 && (!inputs || inputs->frame_size != inputsize_)) {
00128         allocator->free(inputs); // Free old input sequence.
00129         inputs = new(allocator) Torch::Sequence(1, inputsize_);
00130     }
00131 }
00132 
00134 // computeCostsFromOutputs //
00136 void TorchLearner::computeCostsFromOutputs(const Vec& input, const Vec& output, 
00137                                            const Vec& target, Vec& costs) const
00138 {
00139     // No cost computed for now.
00140 }                                
00141 
00143 // computeOutput //
00145 void TorchLearner::computeOutput(const Vec& input, Vec& output) const
00146 {
00147     PLASSERT( outputsize_ >= 0);
00148     output.resize(outputsize_);
00149     inputs->copyFrom(input.data());
00150     machine->forward(inputs);
00151     machine->machine->outputs->copyTo(output.data());
00152 }    
00153 
00155 // forget //
00157 void TorchLearner::forget()
00158 {
00159     stage = 0;
00160     outputsize_ = -1;
00161     if (machine)
00162         machine->reset();
00163 }
00164     
00166 // getTestCostNames //
00168 TVec<string> TorchLearner::getTestCostNames() const
00169 {
00170     // No cost computed for now.
00171     return TVec<string>();
00172 }
00173 
00175 // getTrainCostNames //
00177 TVec<string> TorchLearner::getTrainCostNames() const
00178 {
00179     // No cost computed for now.
00180     return TVec<string>();
00181 }
00182 
00184 // makeDeepCopyFromShallowCopy //
00186 void TorchLearner::makeDeepCopyFromShallowCopy(CopiesMap& copies)
00187 {
00188     inherited::makeDeepCopyFromShallowCopy(copies);
00189 
00190     // ### ex:
00191     // deepCopyField(trainvec, copies);
00192 
00193     // ### Remove this line when you have fully implemented this method.
00194     PLERROR("TorchLearner::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!");
00195 }
00196 
00198 // outputsize //
00200 int TorchLearner::outputsize() const
00201 {
00202     // Compute and return the size of this learner's output, (which typically
00203     // may depend on its inputsize(), targetsize() and set options).
00204     PLASSERT( machine );
00205     PLASSERT( outputsize_ >= 0 || machine->machine->outputs );
00206     if (outputsize_ >=0)
00207         return outputsize_;
00208     return machine->machine->outputs->frame_size;
00209 }
00210 
00212 // setTrainingSet //
00214 void TorchLearner::setTrainingSet(VMat training_set, bool call_forget) {
00215     inherited::setTrainingSet(training_set, call_forget);
00216     torch_train_set = new TTorchDataSetFromVMat(training_set);
00217     allocator->free(inputs); // Free old input sequence.
00218     inputs = new(allocator) Torch::Sequence(1, training_set->inputsize());
00219 }
00220 
00222 // train //
00224 void TorchLearner::train()
00225 {
00226     if (stage >= nstages) {
00227         PLWARNING("In TorchLearner::train - Learner has already been trained, skipping training");
00228         return;
00229     }
00230     if (!trainer || !machine)
00231         PLERROR("In TorchLearner::train - You must set both the 'trainer' and 'machine' options "
00232                 "before calling train()");
00233     trainer->train((TTorchDataSetFromVMat*) torch_train_set);
00234     if (machine->machine->outputs)
00235         // Update outputsize_
00236         outputsize_ = machine->machine->outputs->frame_size;
00237     stage = 1;
00238 }
00239 
00241 // ~TorchLearner //
00243 TorchLearner::~TorchLearner() {
00244     delete allocator;
00245 }
00246 
00247 } // end of namespace PLearn
00248 
00249 
00250 /*
00251   Local Variables:
00252   mode:c++
00253   c-basic-offset:4
00254   c-file-style:"stroustrup"
00255   c-file-offsets:((innamespace . 0)(inline-open . 0))
00256   indent-tabs-mode:nil
00257   fill-column:79
00258   End:
00259 */
00260 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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