net_test_1.cpp

/**
 * Basic example using particle swarm method to train the network
 */

//
// Created by martin on 7/16/18.
//

#include <vector>
#include <iostream>

#include "4neuro.h"

void optimize_via_particle_swarm( l4n::NeuralNetwork &net, l4n::ErrorFunction &ef ){

    /* 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 = 50;
    size_t iter_max = 1000;

    /* 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( ef );

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

    /* ERROR CALCULATION */
    std::cout << "Run finished! Error of the network[Particle swarm]: " << ef.eval( nullptr ) << std::endl;
    std::cout << "***********************************************************************************************************************" <<std::endl;
}

void optimize_via_gradient_descent( l4n::NeuralNetwork &net, l4n::ErrorFunction &ef ){

    std::cout << "***********************************************************************************************************************" <<std::endl;
    l4n::GradientDescent gd( 1e-6, 1000 );

    gd.optimize( ef );

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

    /* ERROR CALCULATION */
    std::cout << "Run finished! Error of the network[Gradient descent]: " << ef.eval( nullptr ) << std::endl;
}

int main() {

    std::cout << "Running lib4neuro example   1: Basic use of the particle swarm or gradient method to train a simple network with few linear neurons" << std::endl;
    std::cout << "***********************************************************************************************************************" <<std::endl;
    std::cout << "The code attempts to find an approximate solution to the system of equations below:" << std::endl;
    std::cout << "0 * w1 + 1 * w2 = 0.50" << std::endl;
    std::cout << "1 * w1 + 0.5*w2 = 0.75" << 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 */
    l4n::NeuronLinear *i1 = new l4n::NeuronLinear( );  //f(x) = x
    l4n::NeuronLinear *i2 = new l4n::NeuronLinear( );  //f(x) = x

    /* Output neuron */
    l4n::NeuronLinear *o1 = new l4n::NeuronLinear( );  //f(x) = x



    /* 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::NO_BIAS);
//

    /* 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);



    /* 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);

    /* PARTICLE SWARM LEARNING */
    net.randomize_parameters();
    optimize_via_particle_swarm( net, mse );


    /* GRADIENT DESCENT LEARNING */
    net.randomize_parameters();
    optimize_via_gradient_descent( net, mse );

    /* Normalize data to prevent 'nan' results */
    ds.normalize();
    net.randomize_parameters();
    optimize_via_gradient_descent(net, mse);

    return 0;
}