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105 lines
3.0 KiB
C++
105 lines
3.0 KiB
C++
#ifndef MLPP_SOFTMAX_NET_H
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#define MLPP_SOFTMAX_NET_H
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//
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// SoftmaxNet.hpp
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//
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// Created by Marc Melikyan on 10/2/20.
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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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#include <string>
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#include <vector>
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class MLPPSoftmaxNet : public Reference {
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GDCLASS(MLPPSoftmaxNet, Reference);
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public:
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/*
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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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Ref<MLPPMatrix> get_output_set();
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void set_output_set(const Ref<MLPPMatrix> &val);
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MLPPReg::RegularizationType get_reg();
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void set_reg(const MLPPReg::RegularizationType val);
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real_t get_lambda();
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void set_lambda(const real_t val);
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real_t get_alpha();
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void set_alpha(const real_t val);
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*/
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std::vector<real_t> model_test(std::vector<real_t> x);
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std::vector<std::vector<real_t>> model_set_test(std::vector<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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std::vector<std::vector<real_t>> get_embeddings(); // This class is used (mostly) for word2Vec. This function returns our embeddings.
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bool is_initialized();
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void initialize();
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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);
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//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);
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MLPPSoftmaxNet();
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~MLPPSoftmaxNet();
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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>> _output_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_class;
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int _n_hidden;
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// Regularization Params
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MLPPReg::RegularizationType _reg;
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real_t _lambda;
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real_t _alpha; /* This is the controlling param for Elastic Net*/
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bool _initialized;
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};
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#endif /* SoftmaxNet_hpp */
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