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 MLPPWGAN {
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public:
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MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet);
~MLPPWGAN();
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std::vector<std::vector<real_t>> generateExample(int n);
void gradientDescent(real_t learning_rate, int max_epoch, bool UI = 1);
real_t score();
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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", real_t lambda = 0.5, real_t alpha = 0.5);
void addOutputLayer(std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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private:
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std::vector<std::vector<real_t>> modelSetTestGenerator(std::vector<std::vector<real_t>> X); // Evaluator for the generator of the WGAN.
std::vector<real_t> modelSetTestDiscriminator(std::vector<std::vector<real_t>> X); // Evaluator for the discriminator of the WGAN.
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real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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void forwardPass();
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void updateDiscriminatorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate);
void updateGeneratorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate);
std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> computeDiscriminatorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
std::vector<std::vector<std::vector<real_t>>> computeGeneratorGradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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void UI(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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std::vector<std::vector<real_t>> outputSet;
std::vector<real_t> y_hat;
std::vector<MLPPOldHiddenLayer> network;
MLPPOldOutputLayer *outputLayer;
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int n;
int k;
};
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#endif /* WGAN_hpp */