mirror of https://github.com/Relintai/pmlpp.git
84 lines
3.2 KiB
C++
84 lines
3.2 KiB
C++
#ifndef MLPP_ANN_H
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#define MLPP_ANN_H
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//
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// ANN.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 "../hidden_layer/hidden_layer.h"
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#include "../output_layer/output_layer.h"
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#include "../hidden_layer/hidden_layer_old.h"
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#include "../output_layer/output_layer_old.h"
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#include <string>
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#include <tuple>
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#include <vector>
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class MLPPANN : public Reference {
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GDCLASS(MLPPANN, Reference);
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public:
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std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
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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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void momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool nag, bool ui = false);
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void adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool ui = false);
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void adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool ui = false);
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void adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
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void adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
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void nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
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void amsgrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
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real_t score();
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void save(std::string file_name);
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void set_learning_rate_scheduler(std::string type, real_t decay_constant);
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void set_learning_rate_scheduler_drop(std::string type, real_t decay_constant, real_t drop_rate);
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void add_layer(int n_hidden, std::string activation, std::string weight_init = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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void add_output_layer(std::string activation, std::string loss, std::string weight_init = "Default", std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
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MLPPANN(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set);
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MLPPANN();
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~MLPPANN();
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protected:
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real_t apply_learning_rate_scheduler(real_t learning_rate, real_t decay_constant, real_t epoch, real_t drop_rate);
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real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
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void forward_pass();
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void update_parameters(std::vector<std::vector<std::vector<real_t>>> hidden_layer_updations, std::vector<real_t> output_layer_updation, real_t learning_rate);
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std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> compute_gradients(std::vector<real_t> y_hat, std::vector<real_t> _output_set);
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void print_ui(int epoch, real_t cost_prev, std::vector<real_t> y_hat, std::vector<real_t> p_output_set);
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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<real_t> _output_set;
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std::vector<real_t> _y_hat;
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std::vector<MLPPOldHiddenLayer> _network;
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MLPPOldOutputLayer *_output_layer;
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int _n;
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int _k;
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std::string _lr_scheduler;
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real_t _decay_constant;
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real_t _drop_rate;
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};
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#endif /* ANN_hpp */ |