2023-01-24 18:57:18 +01:00
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#ifndef MLPP_AUTO_ENCODER_H
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#define MLPP_AUTO_ENCODER_H
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2023-01-23 21:13:26 +01:00
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//
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// AutoEncoder.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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2023-01-27 13:01:16 +01:00
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#include "core/math/math_defs.h"
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#include <string>
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#include <tuple>
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#include <vector>
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2023-01-24 19:29:29 +01:00
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class MLPPAutoEncoder {
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public:
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std::vector<std::vector<real_t>> modelSetTest(std::vector<std::vector<real_t>> X);
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std::vector<real_t> modelTest(std::vector<real_t> x);
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2023-02-10 20:05:47 +01:00
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2023-02-04 13:59:26 +01:00
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void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
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void SGD(real_t learning_rate, int max_epoch, bool UI = false);
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void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
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real_t score();
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void save(std::string fileName);
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MLPPAutoEncoder(std::vector<std::vector<real_t>> inputSet, int n_hidden);
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private:
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real_t Cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
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std::vector<std::vector<real_t>> Evaluate(std::vector<std::vector<real_t>> X);
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std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> propagate(std::vector<std::vector<real_t>> X);
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std::vector<real_t> Evaluate(std::vector<real_t> x);
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std::tuple<std::vector<real_t>, std::vector<real_t>> propagate(std::vector<real_t> x);
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void forwardPass();
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std::vector<std::vector<real_t>> inputSet;
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std::vector<std::vector<real_t>> y_hat;
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std::vector<std::vector<real_t>> weights1;
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std::vector<std::vector<real_t>> weights2;
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std::vector<real_t> bias1;
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std::vector<real_t> bias2;
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std::vector<std::vector<real_t>> z2;
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std::vector<std::vector<real_t>> a2;
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int n;
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int k;
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int n_hidden;
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};
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#endif /* AutoEncoder_hpp */
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