# wandb.log用来记录一些日志(accuracy,loss and epoch), 便于随时查看网路的性能def test(args, model, device, test_loader, classes): model.eval()# switch model to evaluation mode.# This is necessary for layers like dropout, batchNorm etc. which behave differently in training and evaluation mode ...
总体来说,wandb目前的核心功能有以下4个: 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...
对于Any framework,使用wandb的代码如下: # Flexible integration for any Python scriptimportwandb# 1. Start a W&B runwandb.init(project='gpt3')# 2. Save model inputs and hyperparametersconfig = wandb.config config.learning_rate =0.01# Model training here# 3. Log metrics over time to vis...
AI检测代码解析 importwandbimporttorchimporttorch.nnasnnimporttorch.optimasoptim# 初始化wandbwandb.init(project="my-project",name="experiment-1")# 模型定义classSimpleModel(nn.Module):def__init__(self):super(SimpleModel,self).__init__()self.fc=nn.Linear(10,1)defforward(self,x):returnself.fc...
(x)output = F.log_softmax(x, dim=1)returnoutputdeftrain(args, model, device, train_loader, optimizer, epoch, wandb):model.train()forbatch_idx, (data, target)inenumerate(train_loader):data, target = data.to(device), target.to(device)optimizer.zero_grad()output = model(data)loss = ...
) * don't log base model architecture in wandb is log model is false * Update src/transformers/integrations/integration_utils.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * convert log model setting into an enum * fix formatting --- Co-authored-by: amyero...
在你想要记录的位置,使用wandb库中的log函数,记录你感兴趣的指标或变量。 运行代码,并观察wandb的仪表盘,即可实时看到实验数据的记录情况。 如何在wandb上与团队成员协作? 邀请团队成员加入你的项目。在wandb的项目详情页面,点击“邀请成员”按钮,输入要邀请的成员的邮箱,发送邀请链接。
(28*28, 256),nn.BatchNorm1d(256),nn.ReLU(),nn.Dropout(dropout),nn.Linear(256,10)).to(device)return modeldef validate_model(model, valid_dl, loss_func, log_images=False, batch_idx=0):"Compute performance of the model on the validation dataset and log a wandb.Table"model.eval()val...
WandbCallback()方法将为我们处理大部分指标捕获。但是,如果我们想添加一些其他信息,例如混淆矩阵或准确度分数,我们可以使用wandb.log()来实现相同的目的。 例如,我们可以将以下代码片段添加到上面的代码中: preds = model.predict(test_x) preds = np.round(preds).astype(int).reshape(1, -1)[0] ...
wandb.log(metrics) # optional: save model at the end model.to_onnx() wandb.save("model.onnx") 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 这里的关键代码只有 5 条,对模型代码入侵很小,下面依次介绍 ...