roc_auc = auc(fpr, tpr) wandb.log({'accuracy': accuracy_score(test_y, preds), "roc_curve": wandb.Image(plt)}) 通过使用wandb.log(),我们可以将某些输出(如图表)添加为图像,我们可以在 wandb 仪表板上看到这些图像: WandB 仪表板的指标 如我们所见,我们的仪表板将提供对模型进行完整评估所需的所有...
wandb.log()和wandb.Image() wandb.log()会将数据记录到当前的历史记录,换句话说就是每次运行到这里,系统就会将log内的参数值自动上传更新,一般数据会直接绘制成表格。wandb.Image()用于图像的显示,numpy格式的数组或者PIL实例转化为PNG,从而在网页上直接显示出来。 wandb.log({'loss': loss, 'epoch': epoch, ...
wandb.log({"examples": [wandb.Image(image, caption=caption) for image, caption in zip(images, captions)]}) 在W&B仪表板中可以查看实验的所有图表和媒体,方便用户对比不同实验结果的差异并进一步分析模型性能。 五、代码和模型版本控制 为了更好地管理实验代码和模型,W&B提供了版本控制功能。可以在每次实...
log_image('image', [image]) I saw the following output for the print statements and was successfully able to log an image into W&B: <pytorch_lightning.loggers.wandb.WandbLogger object at 0x7fa8b2d8edf0> <bound method WandbLogger.log_image of <pytorch_lightning.loggers.wandb.WandbLogger ...
()# Log one batch of images to the dashboard, always same batch_idx.if i==batch_idx and log_images:log_image_table(images, predicted, labels, outputs.softmax(dim=1))return val_loss / len(valid_dl.dataset), correct / len(valid_dl.dataset)def log_image_table(images, predicted, ...
1,实验跟踪:experiment tracking (wandb.log) 2,版本管理:version management (wandb.log_artifact, wandb.save) 3,case分析:case visualization (wandb.Table, wandb.Image) 4,超参调优:model optimization (wandb.sweep) 本文我们主要介绍 前3个能力,超参调优的介绍在下一篇文章。
(device)# Make predictions: Pass image data from test dataset,# make predictions about class image belongs to(0-9 in this case)output = model(data)# Compute the loss sum up batch loss test_loss += F.nll_loss(output, target, reduction='sum').item()# Get the index of the max log-...
()forstepinrange(4):frames=[]# 每个step输出一个由4张图片组成的列表foriinrange(4):obs,r,done,_=env.step(env.action_space.sample())# wandb.Image将numpy arrays或PILImage的实例转化为PNG以供展示frames.append(wandb.Image(obs,caption="Pong"))wandb.log({"frames":frames},step=step)ifdone:...
Image( data[0], caption="Pred: {} Truth: {}".format(pred[0].item(), target[0]))) #通过wandb来记录模型在测试集上的Accuracy和Loss wandb.log({ "Examples": example_images, "Test Accuracy": 100. * correct / len(test_loader.dataset), "Test Loss": test_loss}) # 定义项目在wandb上...
Description & Motivation Weights & Biases supports rich media logging with multiple media types. However, currently the WandbLogger only has log_image method. This request is to extend the logger to include other media type such as audio...