test_fcnn_training.cpp

#include <Pds/Ra>
#include <Pds/Nn>
#include "extras.h"
 
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::Ra::Tic();
    for(unsigned int k=0;k<M;k++)
    {
        NN0.Training(X,Y,0.1,0.01);
        
        NN0.Evaluate(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;
    }
    Pds::Ra::Toc();
    
    pds_octave_plot_points(Pds::LinSpace(0,M-1,M),Acc,"Iter","Accuracy","testando.m","test_fcnn_training_Acc.png");
    pds_octave_plot_vector(X,Y ,"x_1","x_2","testando.m","test_fcnn_training_Y.png");
    pds_octave_plot_vector(X,Ys,"x_1","x_2","testando.m","test_fcnn_training_Ys.png");
    
    NN0.Save("../test/NeuralNettwork1.txt"); 
    return 0;
}