void Player_neural::train(FANN::training_data &data) { net.set_learning_rate(LEARNING_RATE); for (unsigned int i = 0; i < NB_EPOCHS; ++i) { data.shuffle_train_data(); net.train_epoch(data); net.set_learning_rate(net.get_learning_rate() * LEARNING_RATE_DECAY); } net.print_connections(); }
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"; } }