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));

    DataSet ds(&data_vec);

    /* NETWORK DEFINITION */
    NeuralNetwork net;

    /* Input neurons */
    NeuronLinear *i1 = new NeuronLinear( );  //f(x) = x
    NeuronLinear *i2 = new NeuronLinear( );  //f(x) = x

    /* Output neuron */
    NeuronLinear *o1 = new NeuronLinear( );  //f(x) = x



    /* Adding neurons to the net */
    size_t idx1 = net.add_neuron(i1, BIAS_TYPE::NO_BIAS);
    size_t idx2 = net.add_neuron(i2, BIAS_TYPE::NO_BIAS);
    size_t idx3 = net.add_neuron(o1, 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, SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT);
    net.add_connection_simple(idx2, idx3, 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 */
    MSE mse(&net, &ds);

    /* TRAINING METHOD SETUP */
    std::vector<double> domain_bounds = {-10.0, 10.0, -10.0, 10.0, -10.0, 10.0};
    ParticleSwarm swarm_01(&mse, &domain_bounds);
    /* 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 = 0.9;
    swarm_01.optimize(gamma, epsilon, delta);

    std::vector<double> *parameters = swarm_01.get_solution();
    net.copy_parameter_space(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.print_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;
    NeuralNetwork net2("saved_network.4nt");
    net2.print_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;
}