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84 lines
2.0 KiB
C++
84 lines
2.0 KiB
C++
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#ifndef MLPP_AUTO_ENCODER_H
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#define MLPP_AUTO_ENCODER_H
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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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#include "core/math/math_defs.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 "../regularization/reg.h"
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//REMOVE
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#include <iostream>
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#include <string>
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#include <vector>
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class MLPPAutoEncoder : public Reference {
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GDCLASS(MLPPAutoEncoder, Reference);
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public:
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Ref<MLPPMatrix> get_input_set();
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void set_input_set(const Ref<MLPPMatrix> &val);
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int get_n_hidden();
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void set_n_hidden(const int val);
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std::vector<std::vector<real_t>> model_set_test(std::vector<std::vector<real_t>> X);
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std::vector<real_t> model_test(std::vector<real_t> x);
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void gradient_descent(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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MLPPAutoEncoder();
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~MLPPAutoEncoder();
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protected:
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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<real_t> evaluatev(std::vector<real_t> x);
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std::tuple<std::vector<real_t>, std::vector<real_t>> propagatev(std::vector<real_t> x);
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std::vector<std::vector<real_t>> evaluatem(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>>> propagatem(std::vector<std::vector<real_t>> X);
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void forward_pass();
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static void _bind_methods();
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std::vector<std::vector<real_t>> _input_set;
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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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bool _initialized;
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};
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#endif /* AutoEncoder_hpp */
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