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...
deftrain_epoch(model,dl_train,optimizer):model.train()forstep,batchinenumerate(dl_train):features,labels=batch features,labels=features.to(device),labels.to(device)preds=model(features)loss=nn.CrossEntropyLoss()(preds,labels)loss.backward()optimizer.step()optimizer.zero_grad()returnmodel defeval_e...
"train/epoch": (step + 1 + (n_steps_per_epoch * epoch)) / n_steps_per_epoch,"train/example_ct": example_ct}if step + 1 < n_steps_per_epoch:# 🐝 Log train metrics to wandbwandb.log(metrics)step_ct += 1val_loss, accuracy = validate_model(model, valid_dl, loss_func, log...
点击wandb-mnist项目,可以看到3次训练过程中的loss和epoch的变化对比以及关于系统负载的变化对比。 选择点击其中任意一个训练记录,首先查看汇总信息,这里可以看到训练环境信息(操作系统,python版本,训练命令,甚至包括git的信息),wandb.init记录的超参数信息和wandb.log记录训练结束后的epoch和loss值。 然后查看图表信息,这...
wandb.define_metric("train/step") wandb.define_metric("train/*", step_metric="train/step") for i in range(10): wandb.log( {"train/step": i , 'train/acc1': (i*0.1)**2, 'train/epoch1': i, 'train/batch1': i+5})
test.log({'images': wandb.Image(images.float()), 'masks': { 'true': wandb.Image(targets.float()), 'pred': wandb.Image(pred.float()), }} ) self.optimizer.zero_grad() loss.backward() self.optimizer.step() cur_iters += 1
save_hyperparameters() def training_step(self, batch, batch_idx): x, y = batch x = x.view(x.size(0), -1) z = self.encoder(x) x_hat = self.decoder(z) loss = nn.functional.mse_loss(x_hat, x) # log metrics to wandb self.log("train_loss", loss) return loss def configure...
(100,10)y=torch.randn(100,1)# 模型、损失函数和优化器model=SimpleModel()criterion=nn.MSELoss()optimizer=optim.SGD(model.parameters(),lr=0.01)# 训练过程forepochinrange(100):optimizer.zero_grad()output=model(x)loss=criterion(output,y)loss.backward()optimizer.step()# 记录losswandb.log({"...
metrics = model.training_step() # log metrics inside your training loop to visualize model performance wandb.log(metrics) # optional: save model at the end model.to_onnx() wandb.save("model.onnx") 1. 2. 3. 4. 5. 6. 7.
output = model(data)# Define our loss function, and compute the loss loss = F.nll_loss(output, target)# Backward pass:compute the gradients of loss,the model's parameters loss.backward()# update the neural network weights optimizer.step() 定义测试函数 # wandb.log用来记录一些日志(accuracy,...