neural_network.hpp

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/*
 * 
 * Copyright 2025 Nathanne Isip
 * 
 * Redistribution and use in source and binary forms,
 * with or without modification, are permitted provided
 * that the following conditions are met:
 * 
 * 1. Redistributions of source code must retain the
 *    above copyright notice, this list of conditions
 *    and the following disclaimer.
 * 
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 *    and the following disclaimer in the documentation
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#ifndef CHISEI_NEURAL_NETWORK_HPP
#define CHISEI_NEURAL_NETWORK_HPP
 
#include <exception>
#include <fstream>
#include <functional>
#include <random>
#include <vector>
 
#include <chisei/activation_functions.hpp>
#include <chisei/cpu_feature_optimizer.hpp>
 
namespace chisei {
 
    class NeuralNetwork {
    private:
 
        std::vector<size_t> layer_sizes;
 
        std::vector<std::vector<std::vector<double>>> weights;
 
        std::vector<std::vector<double>> biases;
 
        std::function<double(double)> activation;
 
        std::function<double(double)> activation_derivative;
 
        std::random_device rd;
 
        std::mt19937 gen{rd()};
 
        std::normal_distribution<> weight_dist{0, 0.1};
 
    public:
        NeuralNetwork(
            const std::vector<size_t>& _layers,
            std::function<double(double)> _activation,
            std::function<double(double)> _activation_derivative
        );
 
        NeuralNetwork(const NeuralNetwork& other);
 
        ~NeuralNetwork();
 
        NeuralNetwork& operator=(NeuralNetwork&& other) noexcept;
 
        std::vector<double> predict(const std::vector<double>& input);
 
        void train(
            const std::vector<std::vector<double>>& inputs, 
            const std::vector<std::vector<double>>& targets, 
            double learning_rate = 0.1, 
            int epochs = 10000
        );
 
        double compute_mse_loss(
            const std::vector<double>& prediction, 
            const std::vector<double>& target
        );
 
        std::vector<double> compute_output_gradient(
            const std::vector<double>& prediction, 
            const std::vector<double>& target
        );
 
        double compute_accuracy(
            const std::vector<std::vector<double>>& inputs, 
            const std::vector<std::vector<double>>& targets
        );
 
        bool is_correct_prediction(
            const std::vector<double>& prediction, 
            const std::vector<double>& target
        );
 
        void save_model(const std::string& filename);
 
        static NeuralNetwork loadFromModel(const std::string& filename);
    };
}
 
#endif