mirror of https://github.com/Relintai/pmlpp.git
56 lines
2.2 KiB
C++
56 lines
2.2 KiB
C++
//
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// GAN.hpp
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//
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// Created by Marc Melikyan on 11/4/20.
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//
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#ifndef GAN_hpp
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#define GAN_hpp
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#include "HiddenLayer/HiddenLayer.hpp"
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#include "OutputLayer/OutputLayer.hpp"
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#include <vector>
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#include <tuple>
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#include <string>
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namespace MLPP{
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class GAN{
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public:
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GAN(double k, std::vector<std::vector<double>> outputSet);
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~GAN();
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std::vector<std::vector<double>> generateExample(int n);
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void gradientDescent(double learning_rate, int max_epoch, bool UI = 1);
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double 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", double lambda = 0.5, double alpha = 0.5);
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void addOutputLayer(std::string weightInit = "Default", std::string reg = "None", double lambda = 0.5, double alpha = 0.5);
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private:
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std::vector<std::vector<double>> modelSetTestGenerator(std::vector<std::vector<double>> X); // Evaluator for the generator of the gan.
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std::vector<double> modelSetTestDiscriminator(std::vector<std::vector<double>> X); // Evaluator for the discriminator of the gan.
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double Cost(std::vector<double> y_hat, std::vector<double> y);
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void forwardPass();
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void updateDiscriminatorParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, std::vector<double> outputLayerUpdation, double learning_rate);
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void updateGeneratorParameters(std::vector<std::vector<std::vector<double>>> hiddenLayerUpdations, double learning_rate);
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std::tuple<std::vector<std::vector<std::vector<double>>>, std::vector<double>> computeDiscriminatorGradients(std::vector<double> y_hat, std::vector<double> outputSet);
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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;
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std::vector<double> y_hat;
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std::vector<HiddenLayer> network;
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OutputLayer *outputLayer;
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int n;
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int k;
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};
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}
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#endif /* GAN_hpp */ |