BinaryRBM.cpp

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//used for training the RBM
#include <shark/Unsupervised/RBM/BinaryRBM.h>
#include <shark/Algorithms/GradientDescent/SteepestDescent.h>

//the problem
#include <shark/Unsupervised/RBM/Problems/BarsAndStripes.h>

//for evaluation
#include <shark/Unsupervised/RBM/analytics.h>
#include <iostream>

using namespace shark;
using namespace std;

int main(){

    //we first create the problem. in this tutorial, we use BarsAndStripes
    BarsAndStripes problem;
    UnlabeledData<RealVector> data = problem.data();
    
    //some constants needed for training
    size_t numberOfHidden = 32;//hidden units of the rbm
    size_t numberOfVisible = problem.inputDimension();//visible units of the inputs

    //create rbm with simple binary units
    BinaryRBM rbm(random::globalRng);
    rbm.setStructure(numberOfVisible,numberOfHidden);
    
    //create derivative to optimize the rbm
    //we want a simple vanilla CD-1
    BinaryCD cd(&rbm);
    cd.setK(1);
    cd.setData(data);

    //generate optimizer
    SteepestDescent optimizer;
    optimizer.setMomentum(0);
    optimizer.setLearningRate(0.1);
    
    //now we train the rbm and evaluate the mean negative log-likelihood at the end
    unsigned int numIterations = 1000;//iterations for training
    unsigned int numTrials = 10;//number of trials for training
    double meanResult = 0;
    for(unsigned int trial = 0; trial != numTrials; ++trial) {
        initRandomUniform(rbm, -0.1,0.1);
        cd.init();
        optimizer.init(cd);

        for(unsigned int iteration = 0; iteration != numIterations; ++iteration) {
            optimizer.step(cd);
        }
        //evaluate exact likelihood after training. this is only possible for small problems!
        double likelihood = negativeLogLikelihood(rbm,data);
        std::cout<<trial<<" "<<likelihood<<std::endl;
        meanResult +=likelihood;
    }
    meanResult /= numTrials;

    //print the mean performance
    cout << "RESULTS: " << std::endl;
    cout << "======== " << std::endl;
    cout << "mean negative log likelihood: " << meanResult << std::endl;

}