void train(Configuration *cfg)
{
QString fileName(QDir::homePath() + "/" + QCoreApplication::applicationName() + ".ini");
qDebug() << "using config file:" << fileName;
QSettings settings(fileName, QSettings::IniFormat);
const float learningRate = settings.value("learningRate", 0.8).toFloat();
const unsigned int numLayers = settings.value("numLayers", 3).toInt();
const unsigned int numInput = settings.value("numInput", 1024).toInt();
const unsigned int numHidden = settings.value("numHidden", 32).toInt();
const unsigned int numOutput = settings.value("numOutput", 1).toInt();
const float desiredError = settings.value("desiredError", 0.0001f).toFloat();
const unsigned int maxIterations = settings.value("maxIterations", 3000).toInt();
const unsigned int iterationsBetweenReports = settings.value("iterationsBetweenReports", 100).toInt();
FANN::neural_net net;
net.create_standard(numLayers, numInput, numHidden, numOutput);
net.set_learning_rate(learningRate);
net.set_activation_steepness_hidden(0.5);
net.set_activation_steepness_output(0.5);
net.set_learning_momentum(0.6);
net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC);
net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC);
net.set_training_algorithm(FANN::TRAIN_RPROP);
net.print_parameters();
FANN::training_data data;
if (data.read_train_from_file(cfg->getDataSavePath().toStdString()))
{
qDebug() << "Wczytano dane";
//inicjalizacja wag
net.init_weights(data);
data.shuffle_train_data();
net.set_callback(printCallback, NULL);
net.train_on_data(data, maxIterations,
iterationsBetweenReports, desiredError);
net.save(cfg->getNetSavePath().toStdString());
qDebug() << "Nauczono i zapisano siec";
}
}
void neuralNetworkTraining(std::string training_data_file)
{
/*
* Parameters for create_standard method.
*
* num_layers : The total number of layers including the input and the output layer.
* num_input_neurons : The number of neurons in the input layer.
* num_hidden_one_neurons : The number of neurons in the first hidden layer.
* num_hidden_two_neurons : The number of neurons in the second hidden layer.
* num_output_neurons : The number of neurons in the output layer.
*/
const unsigned int num_layers = 3;
const unsigned int num_input_neurons = 8;
const unsigned int num_hidden_neurons = 5;
const unsigned int num_output_neurons = 1;
/*
* Parameters for train_on_data method.
*
* desired_errors : The desired get_MSE or get_bit_fail, depending on which stop function is chosen by set_train_stop_function.
* max_epochs : The maximum number of epochs the training should continue.
* epochs_between_reports : The number of epochs between printing a status report to stdout. A value of zero means no reports should be printed.
*/
const float desired_error = DESIRED_ERROR;
const unsigned int max_epochs = MAX_EPOCHS;
const unsigned int epochs_between_reports = EPOCHS_BETWEEN_REPORTS;
FANN::neural_net net;
// Create a standard fully connected backpropagation neural network.
net.create_standard(num_layers, num_input_neurons, num_hidden_neurons, num_output_neurons);
net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE); // Set the activation function for all of the hidden layers.
net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE); // Set the activation function for the output layer.
net.set_training_algorithm(FANN::TRAIN_RPROP); // Set the training algorithm.
net.randomize_weights(-INIT_EPSILON, INIT_EPSILON); // Give each connection a random weight between -INIT_EPSILON and INIT_EPSILON.
std::cout << std::endl << "Network Type : ";
switch (net.get_network_type())
{
case FANN::LAYER:
std::cout << "LAYER" << std::endl;
break;
case FANN::SHORTCUT:
std::cout << "SHORTCUT" << std::endl;
break;
default:
std::cout << "UNKNOWN" << std::endl;
break;
}
net.print_parameters();
std::cout << std::endl << "Training Network." << std::endl;
FANN::training_data data;
if (data.read_train_from_file(training_data_file))
{
std::cout << "Max Epochs: " << std::setw(8) << max_epochs << ". " << "Desired Error: " << std::left << desired_error << std::right << std::endl;
net.set_callback(printCallback, NULL); // Sets the callback function for use during training.
net.train_on_data(data, max_epochs, epochs_between_reports, desired_error); // Trains on an entire dataset, for a period of time.
std::cout << "Saving Network." << std::endl;
net.save("neural_network_controller_float.net");
unsigned int decimal_point = net.save_to_fixed("neural_network_controller_fixed.net");
data.save_train_to_fixed("neural_network_controller_fixed.data", decimal_point);
}
}
