php网站开发需求文档,企业所得税交多少,广州网站建设c2c,需要服务器的网站关键词#xff1a;python 、epoch、loss、log #x1f916;: 记录模型的训练过程的步骤如下#xff1a; 导入logging模块。配置日志记录器#xff0c;设置日志文件名、日志级别、日志格式等。在每个epoch结束时#xff0c;使用logging模块记录性能指标、损失值、准确率等信…关键词python 、epoch、loss、log : 记录模型的训练过程的步骤如下 导入logging模块。配置日志记录器设置日志文件名、日志级别、日志格式等。在每个epoch结束时使用logging模块记录性能指标、损失值、准确率等信息。在训练过程中记录其他重要信息比如学习率的变化、每个batch的损失值等。日志记录的信息可以帮助你更好地理解模型的训练过程以及在后续分析和调试中提供有用的信息。 一、定义logger类
1. util里定义Logger
class Logger(object):Write console output to external text file.Code imported from https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py.def __init__(self, fpathNone):self.console sys.stdoutself.file Noneif fpath is not None:self.file open(fpath, a)def __del__(self):self.close()def __enter__(self):passdef __exit__(self, *args):self.close()def write(self, msg):self.console.write(msg\n)if self.file is not None:self.file.write(msg\n)def flush(self):self.console.flush()if self.file is not None:self.file.flush()os.fsync(self.file.fileno())def close(self):self.console.close()if self.file is not None:self.file.close()2. train里调用
log_path pjoin(./result, train, args.city, f{args.tinterval})
logger util.Logger(pjoin(log_path, test.log))
logger.write(f\nTesting configs: {args})
# use tensorboard to draw the curves.
train_writer SummaryWriter(pjoin(./result, train, args.city, f{args.tinterval}))
val_writer SummaryWriter(pjoin(./result, val, args.city, f{args.tinterval}))logger.write(“文本提示”) logger.write(start training...)best idloss{变量: 格式d/f}占位符03d是一个格式化字符串其中的0表示用0来填充空位3表示总共占据3位d表示这是一个十进制整数。因此当i的值小于100时会用0来填充确保输出的字符串总共占据3位。 if i%args.print_every 0:logger.write(fEpoch: {i:03d}, MAE: {mtrain_mae:.2f}, RMSE: {mtrain_rmse:.2f}, MAPE: {mtrain_mape:.2f}, Valid MAE: {mvalid_mae:.2f}, RMSE: {mvalid_rmse:.2f}, MAPE: {mvalid_mape:.2f})torch.save(engine.model.state_dict(), save_path_epoch_str(i)_str(round(mvalid_mae,2)).pth)logger.write(Average Training Time: {:.4f} secs/epoch.format(np.mean(train_time)))bestid np.argmin(his_loss)engine.model.load_state_dict(torch.load(save_path_epoch_str(bestid1)_str(round(his_loss[bestid],2)).pth))logger.write(Training finished)logger.write(fThe valid loss on best model is {str(round(his_loss[bestid],4))})二、自定义print log
def print_log(*values, logNone, end\n):print(*values, endend)if log:if isinstance(log, str):log open(log, a)print(*values, filelog, endend)log.flush()1. 初始化日志文件
记录时间保存路径文件名称 # ------------------------------- make log file ------------------------------ #now datetime.datetime.now().strftime(%Y-%m-%d-%H-%M-%S)log_path f../logs/if not os.path.exists(log_path):os.makedirs(log_path)log os.path.join(log_path, f{model_name}-{dataset}-{now}.log)log open(log, a)log.seek(0)log.truncate()2. 模型记录epoch # --------------------------- train and test model --------------------------- #print_log(fLoss: {criterion._get_name()}, loglog)print_log(loglog)model train(model,trainset_loader,valset_loader,optimizer,scheduler,criterion,clip_gradcfg.get(clip_grad),max_epochscfg.get(max_epochs, 200),early_stopcfg.get(early_stop, 10),verbose1,loglog,savesave,)print_log(fSaved Model: {save}, loglog)test_model(model, testset_loader, loglog)log.close()3. 在每次调用的模型函数train\test)里面保存需要的内容
train(arg, log log) 将定义的log传入模型训练函数
out_str fEarly stopping at epoch: {epoch1}\nout_str fBest at epoch {best_epoch1}:\nout_str Train Loss %.5f\n % train_loss_list[best_epoch]out_str Train RMSE %.5f, MAE %.5f, MAPE %.5f\n % (train_rmse,train_mae,train_mape,)out_str Val Loss %.5f\n % val_loss_list[best_epoch]out_str Val RMSE %.5f, MAE %.5f, MAPE %.5f % (val_rmse,val_mae,val_mape,)print_log(out_str, loglog)print_log(需要保存的值, log 定义的log)
4. log内容编辑
f-strings 是指以f或F 开头的字符串其中以 {}包含的表达式会进行值替换在字符串前加r可防止字符串转义“文本提示字符串 { 属性值} 换行\n”%d 、%f for i in range(out_steps):rmse, mae, mape RMSE_MAE_MAPE(y_true[:, i, :], y_pred[:, i, :])out_str Step %d RMSE %.5f, MAE %.5f, MAPE %.5f\n % (i 1,rmse,mae,mape,)5. 效果展示 三、 自定义logger
1. 函数定义
def get_logger(config, nameNone):log_dir ./libcity/logif not os.path.exists(log_dir):os.makedirs(log_dir)log_filename {}-{}-{}-{}.log.format(config[exp_id],config[model], config[dataset], get_local_time())logfilepath os.path.join(log_dir, log_filename)logger logging.getLogger(name)log_level config.get(log_level, INFO)if log_level.lower() info:level logging.INFOelif log_level.lower() debug:level logging.DEBUGelif log_level.lower() error:level logging.ERRORelif log_level.lower() warning:level logging.WARNINGelif log_level.lower() critical:level logging.CRITICALelse:level logging.INFOlogger.setLevel(level)formatter logging.Formatter(%(asctime)s - %(levelname)s - %(message)s)file_handler logging.FileHandler(logfilepath)file_handler.setFormatter(formatter)console_formatter logging.Formatter(%(asctime)s - %(levelname)s - %(message)s)console_handler logging.StreamHandler(sys.stdout)console_handler.setFormatter(console_formatter)logger.addHandler(file_handler)logger.addHandler(console_handler)logger.info(Log directory: %s, log_dir)return logger2. 函数调用 logger get_logger(config)logger.info(Begin pipeline, task{}, model_name{}, dataset_name{}, exp_id{}.format(str(task), str(model_name), str(dataset_name), str(exp_id)))logger.info(config.config)best_trial result.get_best_trial(loss, min, last)logger.info(Best trial config: {}.format(best_trial.config))logger.info(Best trial final validation loss: {}.format(best_trial.last_result[loss]))四、自定义log_string()
1. utils.py
# log string
def log_string(log, string):log.write(string \n)log.flush()print(string)2. main.py
import上面的utils
定义路径打开log写入log
parser.add_argument(--log_file, default./data/log,helplog file)
args parser.parse_args()
log open(log_file, w)
# load data
log_string(log, loading data...)记入时间%.1fmin一位小数字符串min分钟单位调用变量值%或者字符串里{:}.format(变量)
if __name__ __main__:start time.time()loss_train, loss_val train(model, args, log, loss_criterion, optimizer, scheduler)plot_train_val_loss(loss_train, loss_val, figure/train_val_loss.png)trainPred, valPred, testPred test(args, log)end time.time()log_string(log, total time: %.1fmin % ((end - start) / 60))
