DeepNetworkTrainingRBM.cpp

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//noisy AutoencoderModel model and deep network
#include <shark/Models/LinearModel.h>//single dense layer
#include <shark/Models/ConcatenatedModel.h>//for stacking layers and concatenating noise model
#include <shark/Unsupervised/RBM/BinaryRBM.h> // model for unsupervised pre-training
 
//training the  model
#include <shark/ObjectiveFunctions/ErrorFunction.h>//the error function performing the regularisation of the hidden neurons
#include <shark/ObjectiveFunctions/Loss/SquaredLoss.h> // squared loss used for unsupervised pre-training
#include <shark/ObjectiveFunctions/Loss/CrossEntropy.h> // loss used for supervised training
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h> // loss used for evaluation of performance
#include <shark/ObjectiveFunctions/Regularizer.h> //L1 and L2 regularisation
#include <shark/Algorithms/GradientDescent/SteepestDescent.h> //optimizer: simple gradient descent.
#include <shark/Algorithms/GradientDescent/Rprop.h> //optimizer for autoencoders
 
using namespace std;
using namespace shark;
 
//our artificial problem
LabeledData<RealVector,unsigned int> createProblem(){
    std::vector<RealVector> data(320,RealVector(16));
    std::vector<unsigned int> label(320);
    RealVector line(4);
    for(std::size_t k = 0; k != 10; ++k){
        for(size_t x=0; x != 16; x++) {
            for(size_t j=0; j != 4; j++) {
                bool val = (x & (1<<j)) > 0;
                line(j) = val;
                if(random::coinToss(random::globalRng, 0.3))
                    line(j) = !val;
            }
 
            for(int i=0; i != 4; i++) {
                subrange(data[x+k*16],i*4 ,i*4 + 4) = line;
            }
            for(int i=0; i != 4; i++) {
                for(int l=0; l<4; l++) {
                    data[x+k*16+160](l*4 + i) = line(l);
                }
            }
            label[x+k*16] = 1; 
            label[x+k*16+160] = 0; 
        }
    }
    return createLabeledDataFromRange(data,label);
}
 
//training of an RBM
BinaryRBM trainRBM(
    UnlabeledData<RealVector> const& data,//the data to train with
    std::size_t numHidden,//number of features in the AutoencoderModel
    std::size_t iterations, //number of iterations to optimize
    double regularisation,//strength of the regularisation
    double learningRate // learning rate of steepest descent
){
    //create rbm with simple binary units using the global random number generator
    std::size_t inputs = dataDimension(data);
    BinaryRBM rbm(random::globalRng);
    rbm.setStructure(inputs,numHidden);
    initRandomUniform(rbm,-0.1*std::sqrt(1.0/inputs),0.1*std::sqrt(1.0/inputs));//initialize weights uniformly
    
    //create derivative to optimize the rbm
    //we want a simple vanilla CD-1.
    BinaryCD estimator(&rbm);
    TwoNormRegularizer<> regularizer;
    //0.0 is the regularization strength. 0.0 means no regularization. choose as >= 0.0
    estimator.setRegularizer(regularisation,&regularizer);
    estimator.setK(1);//number of sampling steps
    estimator.setData(data);//the data used for optimization
 
    //create and configure optimizer
    SteepestDescent<> optimizer;
    optimizer.setLearningRate(learningRate);//learning rate of the algorithm
    
    //now we train the rbm and evaluate the mean negative log-likelihood at the end
    unsigned int numIterations = iterations;//iterations for training
    estimator.init();
    optimizer.init(estimator);
    for(unsigned int iteration = 0; iteration != numIterations; ++iteration) {
        optimizer.step(estimator);
    }
    rbm.setParameterVector(optimizer.solution().point);
    return rbm;
}
 
 
int main()
{
    //model parameters
    std::size_t numHidden1 = 8;
    std::size_t numHidden2 = 8;
    //unsupervised hyper parameters
    double unsupRegularisation = 0.001;
    double unsupLearningRate = 0.1;
    std::size_t unsupIterations = 10000;
    //supervised hyper parameters
    double regularisation = 0.0001;
    std::size_t iterations = 200;
    
    //load data and split into training and test
    LabeledData<RealVector,unsigned int> data = createProblem();
    data.shuffle();
    LabeledData<RealVector,unsigned int> test = splitAtElement(data,static_cast<std::size_t>(0.5*data.numberOfElements()));
    
    //train the first hidden layer
    std::cout<<"pre-training first layer"<<std::endl;
    BinaryRBM rbm1 =  trainRBM(
        data.inputs(),numHidden1,
        unsupRegularisation,unsupIterations, unsupLearningRate
    );
    
    //compute the mapping onto features of the first hidden layer
    rbm1.evaluationType(true,true);//we compute the direction visible->hidden and want the features and no samples
    UnlabeledData<RealVector> intermediateData=rbm1(data.inputs());
    
    //train the next layer
    std::cout<<"pre-training second layer"<<std::endl;
    BinaryRBM rbm2 =  trainRBM(
        intermediateData,numHidden2,
        unsupRegularisation,unsupIterations, unsupLearningRate
    );
    
 
    //build three layer neural network from the re-trained RBMs
    LinearModel<RealVector, LogisticNeuron> layer1(rbm1.weightMatrix(),rbm1.hiddenNeurons().bias());
    LinearModel<RealVector, LogisticNeuron> layer2(rbm2.weightMatrix(),rbm2.hiddenNeurons().bias());
    LinearModel<RealVector> output(layer2.outputShape(),numberOfClasses(data));
    initRandomNormal(output,0.01);
    auto network = layer1 >> layer2 >> output;
    
    //create the supervised problem. Cross Entropy loss with two norm regularisation
    CrossEntropy<unsigned int, RealVector> loss;
    ErrorFunction<> error(data, &network, &loss);
    TwoNormRegularizer<> regularizer(error.numberOfVariables());
    error.setRegularizer(regularisation,&regularizer);
    
    //optimize the model
    std::cout<<"training supervised model"<<std::endl;
    Rprop<> optimizer;
    error.init();
    optimizer.init(error);
    for(std::size_t i = 0; i != iterations; ++i){
        optimizer.step(error);
        std::cout<<i<<" "<<optimizer.solution().value<<std::endl;
    }
    network.setParameterVector(optimizer.solution().point);
    
    //evaluation
    ZeroOneLoss<unsigned int,RealVector> loss01;
    Data<RealVector> predictionTrain = network(data.inputs());
    cout << "classification error,train: " << loss01.eval(data.labels(), predictionTrain) << endl;
    
    Data<RealVector> prediction = network(test.inputs());
    cout << "classification error,test: " << loss01.eval(test.labels(), prediction) << endl;
    
}