pmlpp/mlpp/wgan/wgan.h

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#ifndef MLPP_WGAN_H
#define MLPP_WGAN_H
//
// WGAN.hpp
//
// Created by Marc Melikyan on 11/4/20.
//
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#include "../hidden_layer/hidden_layer.h"
#include "../output_layer/output_layer.h"
#include <string>
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#include <tuple>
#include <vector>
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class WGAN {
public:
WGAN(double k, std::vector<std::vector<double>> outputSet);
~WGAN();
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);
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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);
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private:
std::vector<std::vector<double>> modelSetTestGenerator(std::vector<std::vector<double>> X); // Evaluator for the generator of the WGAN.
std::vector<double> modelSetTestDiscriminator(std::vector<std::vector<double>> X); // Evaluator for the discriminator of the WGAN.
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double Cost(std::vector<double> y_hat, std::vector<double> y);
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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);
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void UI(int epoch, double cost_prev, std::vector<double> y_hat, std::vector<double> outputSet);
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std::vector<std::vector<double>> outputSet;
std::vector<double> y_hat;
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std::vector<HiddenLayer> network;
OutputLayer *outputLayer;
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int n;
int k;
};
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#endif /* WGAN_hpp */