pmlpp/mlpp/softmax_net/softmax_net.h

105 lines
3.0 KiB
C++

#ifndef MLPP_SOFTMAX_NET_H
#define MLPP_SOFTMAX_NET_H
//
// SoftmaxNet.hpp
//
// Created by Marc Melikyan on 10/2/20.
//
#include "core/math/math_defs.h"
#include "core/object/reference.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include "../regularization/reg.h"
#include <string>
#include <vector>
class MLPPSoftmaxNet : public Reference {
GDCLASS(MLPPSoftmaxNet, Reference);
public:
/*
Ref<MLPPMatrix> get_input_set();
void set_input_set(const Ref<MLPPMatrix> &val);
Ref<MLPPMatrix> get_output_set();
void set_output_set(const Ref<MLPPMatrix> &val);
MLPPReg::RegularizationType get_reg();
void set_reg(const MLPPReg::RegularizationType val);
real_t get_lambda();
void set_lambda(const real_t val);
real_t get_alpha();
void set_alpha(const real_t val);
*/
std::vector<real_t> model_test(std::vector<real_t> x);
std::vector<std::vector<real_t>> model_set_test(std::vector<std::vector<real_t>> X);
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);
real_t score();
void save(std::string fileName);
std::vector<std::vector<real_t>> get_embeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings.
bool is_initialized();
void initialize();
MLPPSoftmaxNet(std::vector<std::vector<real_t>> p_input_set, std::vector<std::vector<real_t>> p_output_set, int p_n_hidden, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
//MLPPSoftmaxNet(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
MLPPSoftmaxNet();
~MLPPSoftmaxNet();
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();
std::vector<std::vector<real_t>> _input_set;
std::vector<std::vector<real_t>> _output_set;
std::vector<std::vector<real_t>> _y_hat;
std::vector<std::vector<real_t>> _weights1;
std::vector<std::vector<real_t>> _weights2;
std::vector<real_t> _bias1;
std::vector<real_t> _bias2;
std::vector<std::vector<real_t>> _z2;
std::vector<std::vector<real_t>> _a2;
int _n;
int _k;
int _n_class;
int _n_hidden;
// Regularization Params
MLPPReg::RegularizationType _reg;
real_t _lambda;
real_t _alpha; /* This is the controlling param for Elastic Net*/
bool _initialized;
};
#endif /* SoftmaxNet_hpp */