VersatileClassificationTutorial-Network.cpp

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#include <shark/Data/Dataset.h>
#include <shark/Data/Csv.h>
#include <shark/ObjectiveFunctions/Loss/ZeroOneLoss.h>

#include <shark/Models/FFNet.h>
#include <shark/ObjectiveFunctions/Loss/CrossEntropy.h>
#include <shark/ObjectiveFunctions/ErrorFunction.h>
#include <shark/Algorithms/GradientDescent/Rprop.h>
#include <shark/Algorithms/StoppingCriteria/MaxIterations.h>
#include <shark/Algorithms/Trainers/OptimizationTrainer.h>


using namespace shark;

int main()
{
    // Load data, use 70% for training and 30% for testing.
    // The path is hard coded; make sure to invoke the executable
    // from a place where the data file can be found. It is located
    // under [shark]/examples/Supervised/data.
    ClassificationDataset traindata, testdata;
    importCSV(traindata, "data/quickstartData.csv", LAST_COLUMN, ' ');
    testdata = splitAtElement(traindata, 70 * traindata.numberOfElements() / 100);

    typedef FFNet<LogisticNeuron, LogisticNeuron> ModelType; // sigmoid transfer function for hidden and output neurons
    ModelType model;
    size_t N = inputDimension(traindata);
    size_t M = 10;
    model.setStructure(N, M, 2);         // N inputs (depends on the data),
                                         // M hidden neurons (depends on problem difficulty),
                                         // and two output neurons (two classes).
    initRandomUniform(model, -0.1, 0.1); // initialize with small random weights
    CrossEntropy trainloss;              // differentiable loss for neural network training
    IRpropPlus optimizer;                // gradient-based optimization algorithm
    MaxIterations<> stop(100);           // stop optimization after 100 Rprop steps
    OptimizationTrainer<ModelType, unsigned int> trainer(&trainloss, &optimizer, &stop);

    trainer.train(model, traindata);

    Data<RealVector> prediction = model(testdata.inputs());

    ZeroOneLoss<unsigned int, RealVector> loss;
    double error_rate = loss(testdata.labels(), prediction);

    std::cout << "model: " << model.name() << std::endl
        << "trainer: " << trainer.name() << std::endl
        << "test error rate: " << error_rate << std::endl;
}