test_fcnn_training.cpp

#include <Pds/Ra>
#include <Pds/Nn>
 
int main(void)
{
    unsigned int M=2000;
    unsigned int k;
    Pds::Vector Acc(M);
    std::string str;
    
    Pds::Matrix X(Pds::Ra::TextFormat,"../test/data_x_yinyang.txt");
    Pds::Matrix Y(Pds::Ra::TextFormat,"../test/data_y_yinyang.txt");
    
    Pds::Matrix Xt(Pds::Ra::TextFormat,"../test/data_x_yinyang_test.txt");
    Pds::Matrix Yt(Pds::Ra::TextFormat,"../test/data_y_yinyang_test.txt");
    
    Pds::Matrix Ys(Y.Size());
    
    Pds::Ra::Randomize();
    
    std::vector<unsigned int> N={2,512,64,8,1};
    Pds::FCNn NN0=Pds::FCNn(N,Pds::Sigmoid,Pds::DSigmoid);
    
    Pds::Ra::Tic();
    for(k=0;k<M;k++)
    {
        if(k%2==0)  NN0.Training(X,Y,0.1,0.01);
        else        NN0.Training(Xt,Yt,0.1,0.01);
        
        NN0.Predict(Xt,Ys);
        
        Acc.Set(k,100.0*Pds::Accuracy(Ys.Geq(0.5),Yt));
        
        str="acc: "+std::to_string(Acc.At(k));
        Pds::Ra::ProgressBarWithTime(32,k,M,false,str);
 
    }
    std::cout<<std::endl;
    Pds::Ra::Toc();
    
    Pds::Octave::XLabel="Iter";
    Pds::Octave::YLabel="Accuracy";
    Pds::Octave::Plot::CurveXY(Pds::LinSpace(0,M-1,M),Acc,"testando.m","test_fcnn_training_Acc.png");
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DY(X ,Y ,"testando.m","test_fcnn_training_Y.png");
    Pds::Octave::Plot::PointsX2DY(Xt,Ys,"testando.m","test_fcnn_training_Ys.png");
 
    
    NN0.Save("../test/NeuralNetwork1.txt"); 
    return 0;
}