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); } }