example_fcnn_training_actfunc.cpp

Programa para el testeo de las funciones Fully Connected Layer.Para compilar o código example_fcnn_training_actfunc.cpp:

g++ -static -o example_fcnn_training_actfunc example_fcnn_training_actfunc.cpp -lpdsramm -lpdsnnmm 

Para executar o programa:

./example_fcnn_training_actfunc

Retornando por consola:

 8000/8000 [================================] -- ERT  0:00 -- acc0: 81.000000   acc1: 89.300000 acc2: 88.900000

Datos de treinamento.

Datos despues del treinamento NN0.

Datos despues del treinamento NN1.

Datos despues del treinamento NN2.

Datos de Accuracy.

Datos de Accuracy.

Código example_fcnn_training_actfunc.cpp:

 
#include <Pds/Ra>
#include <Pds/Nn>
    
int main(void)
{
    unsigned int M=8000;
    Pds::Vector Acc0(M);
    Pds::Vector Acc1(M);
    Pds::Vector Acc2(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 Ys(Y.Size());
    
    std::vector<unsigned int> N={2,5,11,23,11,5,1};
    Pds::FCNn NN0=Pds::FCNn(N,Pds::Sigmoid,Pds::DSigmoid);
    
    Pds::FCNn NN1=Pds::FCNn(NN0);
    NN1.SetActFunc(0,Pds::ELU,Pds::DELU);
    NN1.SetActFunc(1,Pds::Tanh,Pds::DTanh);
    NN1.SetActFunc(2,Pds::ELU,Pds::DELU);
    NN1.SetActFunc(3,Pds::Tanh,Pds::DTanh);
    NN1.SetActFunc(4,Pds::ELU,Pds::DELU);
    
    Pds::FCNn NN2=Pds::FCNn(NN0);
    NN2.SetActFunc(Pds::Tanh,Pds::DTanh);
 
    for(unsigned int k=0;k<M;k++)
    {
        NN0.Training(X,Y,0.1,0.01);
        NN1.Training(X,Y,0.1,0.01);
        NN2.Training(X,Y,0.1,0.01);
        
        NN0.Predict(X,Ys);
        Acc0.Set(k,100.0*Pds::Accuracy(Ys.Geq(0.5),Y));
        
        NN1.Predict(X,Ys);
        Acc1.Set(k,100.0*Pds::Accuracy(Ys.Geq(0.5),Y));
        
        NN2.Predict(X,Ys);
        Acc2.Set(k,100.0*Pds::Accuracy(Ys.Geq(0.5),Y));
        
        // Progress bar
        str="acc0: "+std::to_string(Acc0.At(k));
        str=str+"\tacc1: "+std::to_string(Acc1.At(k));
        str=str+"\tacc2: "+std::to_string(Acc2.At(k));
        Pds::Ra::ProgressBarWithTime(32,k,M,false,str);
    }
    std::cout<<std::endl;
    
    // Ploting
    Pds::Octave::XLabel="Iter";
    Pds::Octave::YLabel="Accuracy";
    Pds::Octave::Legend={"Pds::Sigmoid","Compound"};
    Pds::Octave::Plot::CurveXYXY(   Pds::LinSpace(0,M-1,M),Acc0,
                                    Pds::LinSpace(0,M-1,M),Acc1,
                                    "testandoAcc01.m",
                                    "example_fcnn_training_actfunc_Acc01.png");
    Pds::Octave::Legend={"Pds::Sigmoid","Pds::Tanh"};
    Pds::Octave::Plot::CurveXYXY(   Pds::LinSpace(0,M-1,M),Acc0,
                                    Pds::LinSpace(0,M-1,M),Acc2,
                                    "testandoAcc02.m",
                                    "example_fcnn_training_actfunc_Acc02.png");
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DY(X,Y ,"testando.m","example_fcnn_training_actfunc_Y.png");
    
    NN0.Predict(X,Ys);
    Pds::Octave::Plot::PointsX2DY(X,Ys,"testando.m","example_fcnn_training_actfunc_Ys0.png");
    
    NN1.Predict(X,Ys);
    Pds::Octave::Plot::PointsX2DY(X,Ys,"testando.m","example_fcnn_training_actfunc_Ys1.png");
    
    NN2.Predict(X,Ys);
    Pds::Octave::Plot::PointsX2DY(X,Ys,"testando.m","example_fcnn_training_actfunc_Ys2.png");
    return 0;
}