pmlpp/mlpp/auto_encoder/auto_encoder.h

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#ifndef MLPP_AUTO_ENCODER_H
#define MLPP_AUTO_ENCODER_H
//
// AutoEncoder.hpp
//
// Created by Marc Melikyan on 11/4/20.
//
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include "../regularization/reg.h"
//REMOVE
#include <iostream>
#include <string>
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#include <vector>
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class MLPPAutoEncoder : public Reference {
GDCLASS(MLPPAutoEncoder, Reference);
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public:
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Ref<MLPPMatrix> get_input_set();
void set_input_set(const Ref<MLPPMatrix> &val);
int get_n_hidden();
void set_n_hidden(const int val);
std::vector<std::vector<real_t>> model_set_test(std::vector<std::vector<real_t>> X);
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);
void sgd(real_t learning_rate, int max_epoch, bool ui = false);
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();
~MLPPAutoEncoder();
protected:
real_t cost(std::vector<std::vector<real_t>> y_hat, std::vector<std::vector<real_t>> y);
std::vector<real_t> evaluatev(std::vector<real_t> x);
std::tuple<std::vector<real_t>, std::vector<real_t>> propagatev(std::vector<real_t> x);
std::vector<std::vector<real_t>> evaluatem(std::vector<std::vector<real_t>> X);
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::vector<real_t>>> propagatem(std::vector<std::vector<real_t>> X);
void forward_pass();
static void _bind_methods();
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std::vector<std::vector<real_t>> _input_set;
std::vector<std::vector<real_t>> _y_hat;
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std::vector<std::vector<real_t>> _weights1;
std::vector<std::vector<real_t>> _weights2;
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std::vector<real_t> _bias1;
std::vector<real_t> _bias2;
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std::vector<std::vector<real_t>> _z2;
std::vector<std::vector<real_t>> _a2;
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int _n;
int _k;
int _n_hidden;
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bool _initialized;
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};
#endif /* AutoEncoder_hpp */