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