pmlpp/mlpp/gan/gan.h
2023-01-24 19:00:54 +01:00

57 lines
2.0 KiB
C++

#ifndef MLPP_GAN_hpp
#define MLPP_GAN_hpp
//
// GAN.hpp
//
// Created by Marc Melikyan on 11/4/20.
//
#include "../hidden_layer/hidden_layer.h"
#include "../output_layer/output_layer.h"
#include <string>
#include <tuple>
#include <vector>
namespace MLPP {
class GAN {
public:
GAN(double k, std::vector<std::vector<double>> outputSet);
~GAN();
std::vector<std::vector<double>> generateExample(int n);
void gradientDescent(double learning_rate, int max_epoch, bool UI = 1);
double score();
void save(std::string fileName);
void addLayer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
void addOutputLayer(std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
private:
std::vector<std::vector<double>> modelSetTestGenerator(std::vector<std::vector<double>> X); // Evaluator for the generator of the gan.
std::vector<double> modelSetTestDiscriminator(std::vector<std::vector<double>> X); // Evaluator for the discriminator of the gan.
double Cost(std::vector<double> y_hat, std::vector<double> y);
void forwardPass();
void updateDiscriminatorParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, std::vector<double> outputLayerUpdation, double learning_rate);
void updateGeneratorParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, double learning_rate);
std::tuple<std::vector<std::vector<std::vector<double>>>, std::vector<double>> computeDiscriminatorGradients(std::vector<double> y_hat, std::vector<double> outputSet);
std::vector<std::vector<std::vector<double>>> computeGeneratorGradients(std::vector<double> y_hat, std::vector<double> outputSet);
void UI(int epoch, double cost_prev, std::vector<double> y_hat, std::vector<double> outputSet);
std::vector<std::vector<double>> outputSet;
std::vector<double> y_hat;
std::vector<HiddenLayer> network;
OutputLayer *outputLayer;
int n;
int k;
};
} //namespace MLPP
#endif /* GAN_hpp */