EigenLibraryNeuralNetwork/main.cpp at main · SedatDurgun/EigenLibraryNeuralNetwork · GitHub

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#include "NeuralNetwork.h"
#include <iostream>

int main()
{


    // {2, 4, 1} → 2 input, 4 hidden neuron, 1 output

    EigenLibrary::NeuralNetwork nn({ 2, 4, 1 }, 0.1);


    // 2. XOR Data

    std::vector<Eigen::VectorXd> inputs = {
        (Eigen::VectorXd(2) << 0, 0).finished(),
        (Eigen::VectorXd(2) << 0, 1).finished(),
        (Eigen::VectorXd(2) << 1, 0).finished(),
        (Eigen::VectorXd(2) << 1, 1).finished()
    };

    std::vector<Eigen::VectorXd> targets = {
        (Eigen::VectorXd(1) << 0).finished(),
        (Eigen::VectorXd(1) << 1).finished(),
        (Eigen::VectorXd(1) << 1).finished(),
        (Eigen::VectorXd(1) << 0).finished()
    };

    // 3. Train

    std::cout << "=== Training Begins  ===" << std::endl;
    nn.Train(inputs, targets, 5000);
    std::cout << "=== Training is Over ===" << std::endl;


    // 4. Predict

    std::cout << "\n=== Predicts ===" << std::endl;
    for (size_t i = 0; i < inputs.size(); i++)
    {
        Eigen::VectorXd output = nn.Predict(inputs[i]);
        std::cout << "Input: [" << inputs[i].transpose() << "]"
            << " Predict: " << output[0]
            << " Expected: " << targets[i][0]
            << std::endl;
    }


    // 5. Evaluate

    std::cout << "\n=== Evaluate ===" << std::endl;
    nn.Evaluate(inputs, targets);


    // 6. Save/load

    std::cout << "\n=== Model SAVING ===" << std::endl;
    nn.SaveModel("\nmodel.bin");

    std::cout << "\n=== Model LOADING ===" << std::endl;
    EigenLibrary::NeuralNetwork nn2({ 2, 4, 1 }, 0.1);
    nn2.LoadModel("\nmodel.bin");

    std::cout << "\n=== Uploaded Model Predictions  ===" << std::endl;
    for (size_t i = 0; i < inputs.size(); i++)
    {
        Eigen::VectorXd output = nn2.Predict(inputs[i]);
        std::cout << "Input: [" << inputs[i].transpose() << "]"
            << "  Predict: " << output[0]
            << std::endl;
    }

    return 0;
}