network_serialization.cpp

/**
 * Example of saving neural network to a file and loading it.
 * Network creation and training is copied from net_test_1.
 *
 * @author Martin Beseda
 * @date 9.8.18
 */

#include <vector>
#include <4neuro.h>

int main() {
    std::cout << "Running lib4neuro Serialization example   1" << std::endl;
    std::cout
        << "********************************************************************************************************************************************"
        << std::endl;
    std::cout << "First, it finds an approximate solution to the system of equations below:" << std::endl;
    std::cout << "0 * w1 + 1 * w2 = 0.50 + b" << std::endl;
    std::cout << "1 * w1 + 0.5*w2 = 0.75 + b" << std::endl;
    std::cout
        << "********************************************************************************************************************************************"
        << std::endl;
    std::cout << "Then it stores the network with its weights into a file via serialization" << std::endl;
    std::cout << "Then it loads the network from a file via serialization" << std::endl;
    std::cout << "Finally it tests the loaded network parameters by evaluating the error function" << std::endl;
    std::cout
        << "********************************************************************************************************************************************"
        << std::endl;

    /* TRAIN DATA DEFINITION */
    std::vector<std::pair<std::vector<double>, std::vector<double>>> data_vec;
    std::vector<double>                                              inp, out;

    inp = {0, 1};
    out = {0.5};
    data_vec.emplace_back(std::make_pair(inp,
                                         out));

    inp = {1, 0.5};
    out = {0.75};
    data_vec.emplace_back(std::make_pair(inp,
                                         out));

    l4n::DataSet ds(&data_vec);

    /* NETWORK DEFINITION */
    l4n::NeuralNetwork net;

    /* Input neurons */

    std::shared_ptr<l4n::NeuronLinear> i1 = std::make_shared<l4n::NeuronLinear>();
    std::shared_ptr<l4n::NeuronLinear> i2 = std::make_shared<l4n::NeuronLinear>();

    /* Output neuron */
    std::shared_ptr<l4n::NeuronLinear> o1 = std::make_shared<l4n::NeuronLinear>();


    /* Adding neurons to the net */
    size_t idx1 = net.add_neuron(i1,
                                 l4n::BIAS_TYPE::NO_BIAS);
    size_t idx2 = net.add_neuron(i2,
                                 l4n::BIAS_TYPE::NO_BIAS);
    size_t idx3 = net.add_neuron(o1,
                                 l4n::BIAS_TYPE::NEXT_BIAS);

    std::vector<double>* bv = net.get_parameter_ptr_biases();
    for (size_t i = 0; i < 1; ++i) {
        bv->at(i) = 1.0;
    }

    /* Adding connections */
    net.add_connection_simple(idx1,
                              idx3,
                              l4n::SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT);
    net.add_connection_simple(idx2,
                              idx3,
                              l4n::SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT);

    //net.randomize_weights();

    /* specification of the input/output neurons */
    std::vector<size_t> net_input_neurons_indices(2);
    std::vector<size_t> net_output_neurons_indices(1);
    net_input_neurons_indices[0] = idx1;
    net_input_neurons_indices[1] = idx2;

    net_output_neurons_indices[0] = idx3;

    net.specify_input_neurons(net_input_neurons_indices);
    net.specify_output_neurons(net_output_neurons_indices);

    /* ERROR FUNCTION SPECIFICATION */
    l4n::MSE mse(&net,
                 &ds);

    /* TRAINING METHOD SETUP */
    std::vector<double> domain_bounds(2 * (net.get_n_weights() + net.get_n_biases()));

    for (size_t i = 0; i < domain_bounds.size() / 2; ++i) {
        domain_bounds[2 * i]     = -10;
        domain_bounds[2 * i + 1] = 10;
    }

    double c1          = 1.7;
    double c2          = 1.7;
    double w           = 0.7;
    size_t n_particles = 5;
    size_t iter_max    = 10;

    /* if the maximal velocity from the previous step is less than 'gamma' times the current maximal velocity, then one
     * terminating criterion is met */
    double gamma = 0.5;

    /* if 'delta' times 'n' particles are in the centroid neighborhood given by the radius 'epsilon', then the second
     * terminating criterion is met ('n' is the total number of particles) */
    double epsilon = 0.02;
    double delta   = 0.7;

    l4n::ParticleSwarm swarm_01(
        &domain_bounds,
        c1,
        c2,
        w,
        gamma,
        epsilon,
        delta,
        n_particles,
        iter_max
    );
    swarm_01.optimize(mse);

    std::vector<double>* parameters = swarm_01.get_parameters();
    net.copy_parameter_space(swarm_01.get_parameters());

    printf("w1 = %10.7f\n",
           parameters->at(0));
    printf("w2 = %10.7f\n",
           parameters->at(1));
    printf(" b = %10.7f\n",
           parameters->at(2));


    /* SAVE NETWORK TO THE FILE */
    std::cout
        << "********************************************************************************************************************************************"
        << std::endl;
    std::cout << "Network generated by the example" << std::endl;
    net.write_stats();
    net.save_text("saved_network.4nt");
    std::cout
        << "--------------------------------------------------------------------------------------------------------------------------------------------"
        << std::endl;
    double error = 0.0;
    inp = {0, 1};
    net.eval_single(inp,
                    out);
    error += (0.5 - out[0]) * (0.5 - out[0]);
    std::cout << "x = (0,   1), expected output: 0.50, real output: " << out[0] << std::endl;

    inp = {1, 0.5};
    net.eval_single(inp,
                    out);
    error += (0.75 - out[0]) * (0.75 - out[0]);
    std::cout << "x = (1, 0.5), expected output: 0.75, real output: " << out[0] << std::endl;
    std::cout << "Error of the network: " << 0.5 * error << std::endl;
    std::cout
        << "********************************************************************************************************************************************"
        << std::endl;

    std::cout << "Network loaded from a file" << std::endl;
    l4n::NeuralNetwork net2("saved_network.4nt");
    net2.write_stats();
    std::cout
        << "--------------------------------------------------------------------------------------------------------------------------------------------"
        << std::endl;
    error = 0.0;
    inp   = {0, 1};
    net2.eval_single(inp,
                     out);
    error += (0.5 - out[0]) * (0.5 - out[0]);
    std::cout << "x = (0,   1), expected output: 0.50, real output: " << out[0] << std::endl;

    inp = {1, 0.5};
    net2.eval_single(inp,
                     out);
    error += (0.75 - out[0]) * (0.75 - out[0]);
    std::cout << "x = (1, 0.5), expected output: 0.75, real output: " << out[0] << std::endl;
    std::cout << "Error of the network: " << 0.5 * error << std::endl;
    std::cout
        << "********************************************************************************************************************************************"
        << std::endl;
    return 0;
}