void NextEpoch(vector<dna>& dnas) { size_t cnt = dnas.size(); #pragma omp parallel #pragma omp for for (short it = 0; it < cnt; ++it) { FANN::neural_net net; net.create_standard(3, data.num_input_train_data(), dnas[it].numberOfHidden, data.num_output_train_data()); net.set_learning_rate(dnas[it].learningRate); net.set_learning_momentum(dnas[it].momentum); net.set_activation_steepness_hidden(1.0); net.set_activation_steepness_output(1.0); net.set_activation_function_hidden(FANN::LINEAR); net.set_activation_function_output(FANN::LINEAR); net.set_callback(print_fake, NULL); net.set_error_log(NULL); net.train_on_data(data, dnas[it].maxIteration, -1, dnas[it].desiredError); dnas[it].fitness = (1. - net.test_data(testdata)) ; } }
void PrintBest(vector<dna>& dnas, int gen) { cout << "Best gen : "<< gen << "----------" << endl; for (int i = 0; i < gen; ++i) { cout << endl; cout << "-********************-" << endl; cout << "fitness" << dnas[i].fitness << endl; cout << "learningRate" << dnas[i].learningRate << endl; cout << "momentum" << dnas[i].momentum << endl; cout << "numberOfHidden" << dnas[i].numberOfHidden << endl; cout << "maxIteration" << dnas[i].maxIteration << endl; cout << "desiredError" << dnas[i].desiredError<< endl; cout << "-********************-" << endl; cout << endl; } FANN::neural_net net; net.create_standard(3, data.num_input_train_data(), dnas[0].numberOfHidden, data.num_output_train_data()); net.set_learning_rate(dnas[0].learningRate); net.set_learning_momentum(dnas[0].momentum); net.set_activation_steepness_hidden(1.0); net.set_activation_steepness_output(1.0); net.set_activation_function_hidden(FANN::SIGMOID); net.set_activation_function_output(FANN::SIGMOID); ofstream file("ErrorHistory/" + file + ".csv"); net.set_callback(print_callback, &file); net.set_error_log(NULL); net.train_on_data(data, dnas[0].maxIteration, 1, dnas[0].desiredError); cout << "----------------------" << endl; }
//Creates a neural net with 1 hidden layer FANN::neural_net create_net(float learning_rate, unsigned int num_layers,unsigned int num_input, unsigned int num_hidden, unsigned int num_output){ FANN::neural_net net; unsigned int layers[3]={num_input,num_hidden,num_output}; net.create_standard_array(num_layers,layers); net.set_learning_rate(learning_rate); net.set_activation_steepness_hidden(.1); net.set_activation_steepness_output(.1); net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE); net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE); //net.set_training_algorithm(FANN::TRAIN_INCREMENTAL); // Set additional properties such as the training algorithm //net.set_training_algorithm(FANN::TRAIN_QUICKPROP); return net; }
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); } }
void trainingThread::train() { std::stringstream log; log << std::endl << " test started." << std::endl; const float learning_rate = netConfigPTR->learning_rate ; const unsigned int num_layers = netConfigPTR->num_layers; const unsigned int num_input = netConfigPTR->num_input; const unsigned int num_hidden = netConfigPTR->num_hidden; const unsigned int num_output = netConfigPTR->num_output; const float desired_error = netConfigPTR->desired_error; const unsigned int max_iterations = netConfigPTR->max_iterations; const unsigned int iterations_between_reports = netConfigPTR->iterations_between_reports; log << std::endl << "Creating network." << std::endl; FANN::neural_net net; if (netConfigPTR->leyersVector.size() > 1) { unsigned int vectorSize = netConfigPTR->leyersVector.size(); unsigned int* leyers = new unsigned int[vectorSize+2]; leyers[0] = num_input; for (unsigned int i = 0; i < vectorSize; ++i) { leyers[i+1] = netConfigPTR->leyersVector.at(i); } leyers[num_layers-1] = num_output; for ( unsigned int i = 0 ; i< vectorSize+2 ; ++i) { qDebug() << "vector size: "<< vectorSize+2<<" i:"<<i<< " leyers "<< leyers[i]; } net.create_standard_array(vectorSize+2, leyers); //net.create_standard(vectorSize+2, leyers[0], leyers[2],leyers[3], leyers[1]); delete[] leyers; } else { net.create_standard(num_layers, num_input, num_hidden, num_output); } net.set_learning_rate(learning_rate); net.set_activation_steepness_hidden(1.0); net.set_activation_steepness_output(1.0); net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE); net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE); // Set additional properties such as the training algorithm net.set_training_algorithm(netConfigPTR->trainingAlgo); // Output network type and parameters log << std::endl << "Network Type : "; switch (net.get_network_type()) { case FANN::LAYER: log << "LAYER" << std::endl; break; case FANN::SHORTCUT: log << "SHORTCUT" << std::endl; break; default: log << "UNKNOWN" << std::endl; break; } //net.print_parameters(); log << std::endl << "Training network." << std::endl; FANN::training_data data; if (data.read_train_from_file(netConfigPTR->trainingDataPatch)) { // Initialize and train the network with the data net.init_weights(data); log << "Max Epochs " << std::setw(8) << max_iterations << ". " << "Desired Error: " << std::left << desired_error << std::right << std::endl; emit updateLog(QString::fromStdString(log.str())); log << "dupa"; log.str(""); log.clear(); net.set_callback(print_callback, nullptr); net.train_on_data(data, max_iterations, iterations_between_reports, desired_error); log << std::endl << "Testing network." << std::endl; for (unsigned int i = 0; i < data.length_train_data(); ++i) { // Run the network on the test data fann_type *calc_out = net.run(data.get_input()[i]); log << "test ("; for (unsigned int j = 0; j < num_input; ++j) { log << std::showpos << data.get_input()[i][j] << ", "; //qDebug()<< "jestem w log<<"; } log << ") -> " ; for(unsigned int k = 0 ; k < num_output ; ++k) { log << calc_out[k] <<", "; } log << ",\t should be "; for(unsigned int k = 0 ; k < num_output ; ++k) { log << data.get_output()[i][k] <<", "; } log << std::endl ; } log << std::endl << "Saving network." << std::endl; // Save the network in floating point and fixed point net.save(netConfigPTR->netFloat); unsigned int decimal_point = net.save_to_fixed(netConfigPTR->netFixed); std::string path = netConfigPTR->trainingDataPatch.substr(0,netConfigPTR->trainingDataPatch.size()-5); data.save_train_to_fixed(path +"_fixed.data", decimal_point); log << std::endl << "test completed." << std::endl; emit updateLog(QString::fromStdString(log.str())); emit updateProgressBar(100); } }