/**
* 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;
}