test_fcnn_training2.cpp

#include <Pds/Ra>
#include <Pds/Nn>
 
int main(void)
{
    unsigned int M=2000;
    Pds::Vector Acc(M);
    
    Pds::Matrix X(Pds::Ra::TextFormat,"../test/data_x_yinyang.txt");
    Pds::Matrix Y(Pds::Ra::TextFormat,"../test/data_y_yinyang.txt");
    Pds::Matrix Ys(Y.Size());
    
    std::vector<unsigned int> N={2,512,64,8,1};
    Pds::FCNn NN0=Pds::FCNn(N,Pds::Sigmoid,Pds::DSigmoid);
    
    Pds::Matrix Xtr=X;
    Pds::Matrix Ytr=Y;
    Pds::Vector T;
    
    Pds::Ra::Tic();
    for(unsigned int k=0;k<M;k++)
    {
        NN0.Training(Xtr,Ytr,0.1,0.01);
        
        NN0.Predict(X,Ys);
        
        Acc.Set(k,100.0*Pds::Accuracy(Ys.Geq(0.5),Y));
        std::cout<<"Test["<<k<<"]\t";
        std::cout<<"Accuracy %: "<<Acc.At(k)<<std::endl;
        
        T=Ys.Geq(0.5).NotEqualTo(Y);
        Xtr=X.FindRows(T);
        Ytr=Y.FindRows(T);
    }
    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_training2_Acc.png");
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DY(X,Y ,"testando.m","test_fcnn_training2_Y.png");
    Pds::Octave::Plot::PointsX2DY(X,Ys,"testando.m","test_fcnn_training2_Ys.png");
    
    NN0.Save("../test/NeuralNetwork1.txt"); 
    return 0;
}