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106 lines
4.1 KiB
C++
106 lines
4.1 KiB
C++
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#ifndef MLPP_WGAN_H
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#define MLPP_WGAN_H
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//
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// WGAN.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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#include "core/containers/vector.h"
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#include "core/math/math_defs.h"
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#include "core/string/ustring.h"
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#include "core/object/reference.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include "../hidden_layer/hidden_layer.h"
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#include "../output_layer/output_layer.h"
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#include <string>
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#include <tuple>
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#include <vector>
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class MLPPWGAN : public Reference {
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GDCLASS(MLPPWGAN, Reference);
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public:
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std::vector<std::vector<real_t>> generate_example(int n);
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void gradient_descent(real_t learning_rate, int max_epoch, bool UI = false);
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real_t score();
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void save(std::string fileName);
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void add_layer(int n_hidden, std::string activation, std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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void add_output_layer(std::string weightInit = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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MLPPWGAN(real_t k, std::vector<std::vector<real_t>> outputSet);
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MLPPWGAN();
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~MLPPWGAN();
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protected:
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std::vector<std::vector<real_t>> model_set_test_generator(std::vector<std::vector<real_t>> X); // Evaluator for the generator of the WGAN.
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std::vector<real_t> model_set_test_discriminator(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 forward_pass();
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void update_discriminator_parameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, std::vector<real_t> outputLayerUpdation, real_t learning_rate);
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void update_generator_parameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate);
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std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> compute_discriminator_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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std::vector<std::vector<std::vector<real_t>>> compute_generator_gradients(std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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void handle_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> outputSet);
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static void _bind_methods();
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std::vector<std::vector<real_t>> outputSet;
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std::vector<real_t> y_hat;
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std::vector<MLPPOldHiddenLayer> network;
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MLPPOldOutputLayer *outputLayer;
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int n;
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int k;
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};
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class MLPPWGANOld {
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public:
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MLPPWGANOld(real_t k, std::vector<std::vector<real_t>> outputSet);
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~MLPPWGANOld();
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std::vector<std::vector<real_t>> generateExample(int n);
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void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
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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);
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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.
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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);
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void updateGeneratorParameters(std::vector<std::vector<std::vector<real_t>>> hiddenLayerUpdations, real_t learning_rate);
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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);
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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;
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std::vector<real_t> y_hat;
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std::vector<MLPPOldHiddenLayer> network;
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MLPPOldOutputLayer *outputLayer;
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int n;
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int k;
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};
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#endif /* WGAN_hpp */ |