example_fcnn_training_load.cpp

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

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

Para executar o programa:

./example_fcnn_training_load

Retornando por consola:

 2000/2000 [================================] -- ERT  0:00 -- acc: 96.850000

Datos de treinamento.

Datos despues del treinamento.

Código example_fcnn_training_load.cpp:

 
#include <Pds/Ra>
#include <Pds/Nn>
    
int main(void)
{
    unsigned int M=2000;
    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());
    
    Pds::Nn::DefaultActFunc=Pds::Sigmoid;
    Pds::Nn::DefaultDActFunc=Pds::DSigmoid;
    Pds::FCNn NN0("../test/NeuralNetwork0.txt");
    
    Y=2*Y-1.0;
    
    for(unsigned int k=0;k<M;k++)
    {
        NN0.Training(X,Y,0.1,0.01);
        
        NN0.Predict(X,Ys);
        
        str="acc: "+std::to_string(100.0*Pds::Accuracy(Ys,Y,0.1));
        Pds::Ra::ProgressBarWithTime(32,k,M,false,str);
    }  
    std::cout<<std::endl;
    
    Pds::Octave::XLabel="x_1";
    Pds::Octave::YLabel="x_2";
    Pds::Octave::Plot::PointsX2DY(X,Y ,"testando.m","dataset_data_Y.png");
    Pds::Octave::Plot::PointsX2DY(X,Ys,"testando.m","dataset_data_Ys.png");
        
    NN0.Save("../test/NeuralNetwork0.txt"); 
    return 0;
}