// Train the net with all the data void train_net(FANN::neural_net &net,std::string oppName, std::string day, std::string type, const int num_output) { const float desired_error = 0.001f; const unsigned int max_iterations = 1000; const unsigned int iterations_between_reports = 1000; std::string trainFileName="input/"+type+"_Casino_Day-"+day+"_"+oppName+"_vs_mybotisamazing.txt"; std::string valFileName="input/"+type+"_Casino_Day-"+day+"_mybotisamazing_vs_"+oppName+".txt"; cout << endl << "Training network." << endl; FANN::training_data data; FANN::training_data vData; data.read_train_from_file(trainFileName); vData.read_train_from_file(valFileName); // Initialize and train the network with the data net.init_weights(data); cout << "Max Epochs " << setw(8) << max_iterations << ". " << "Desired Error: " << left << desired_error << right << endl; net.set_callback(print_callback, NULL); clock_t start=clock(); net.train_on_data(data, max_iterations, iterations_between_reports, desired_error); clock_t end=clock(); cout<<"Runtime:"<< end-start<<endl; cout << endl << "Testing network." << endl; std::string oFileName="output/"+type+"_"+oppName+"_day_"+day+".txt"; std::string actionFile="input/action_Casino_Day-"+day+"_mybotisamazing_vs_"+oppName+".txt"; validate_net(net, oFileName,actionFile, &vData, num_output); ///////////////////////////////////////////////////// net.train_on_data(vData,max_iterations,iterations_between_reports,desired_error); std::string oFileName2="output/"+type+"2_"+oppName+"_day_"+day+".txt"; std::string actionFile2="input/action_Casino_Day-"+day+"_mybotisamazing_vs_"+oppName+".txt"; validate_net(net,oFileName2,actionFile,&vData,num_output); // Save the network in floating point and fixed point std::string netFile="output/"+type+"_"+oppName+"_day_"+day+".net"; net.save(netFile); //unsigned int decimal_point = net.save_to_fixed("training.net"); //data.save_train_to_fixed("training_fixed.data", decimal_point); }
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 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); } }