writer.add_scalar('Loss/train', np.random.random(), n_iter) writer.add_scalar('Loss/test', np.random.random(), n_iter) writer.add_scalar('Accuracy/train', np.random.random(), n_iter) writer.add_scalar('Accuracy/test', np.random.random(), n_iter) 通过斜杠/来反映等级。结果: 2....
writer.add_scalar（）学习1.写到⼀个类别下 from torch.utils.tensorboard import SummaryWriter import numpy as np np.random.seed(20200910)writer = SummaryWriter()for n_iter in range(100):writer.add_scalar('Loss/train', np.random.random(), n_iter)writer.add_scalar('Loss/test', np.random....
summary_writer.add_scalar('val_recon_err', val_recon_err, n_epoch) print('Epoch[%d] train acc: %.4f loss: %.6f recon_err: %.6f'% (n_epoch, train_acc, train_loss, train_recon_err)) print('Epoch[%d] val acc: %.4f loss: %.6f recon_err: %.6f'% (n_epoch, val_acc, va...
forepochinrange(10):train_loss=0.1*(10-epoch)# 模拟的训练损失val_loss=0.05*(10-epoch)# 模拟的验证损失writer.add_scalar('Loss/train',train_loss,epoch)writer.add_scalar('Loss/validation',val_loss,epoch) 1. 2. 3. 4. 5. 2.3 记录图像 ...
from torch.utils.tensorboard import SummaryWriter # 创建TensorBoard写入器 writer = SummaryWriter() # 训练模型并记录损失值 for epoch in range(1000): optimizer.zero_grad() output = model(X) loss = criterion(output, y) loss.backward() optimizer.step() writer.add_scalar('Loss/train', loss....
writer.add_scalar('loss/step_loss', loss, steps)# Add the epoch loss to tensorboard and update the progressbarwriter.add_scalar('loss/epoch_loss', epoch_loss, steps) pbar.set_postfix(epoch_loss=epoch_loss) model.eval()# Setting the model in evaluation mode for testingif(epoch %5==0...
然后,可以使用writer的add_scalar方法来记录标量数据,如损失函数值、准确率等: loss= accuracy= epoch=1 _scalar("Loss/train", loss, epoch) _scalar("Accuracy/train", accuracy, epoch) 其中,“Loss/train”和”Accuracy/train”是记录的标签名称,可以根据具体需求自定义。loss和accuracy是要记录的数据值,epoch...
值forepochinrange(num_epochs):fori,(inputs,labels)inenumerate(train_loader):# Forward passoutputs=model(inputs)loss=criterion(outputs,labels)# Backward and optimizeoptimizer.zero_grad()loss.backward()optimizer.step()# 记录损失值writer.add_scalar('Loss/train',loss.item(),epoch)# 关闭Summary...
train_n_label,train_l_dist,train_ctc_loss=loss_metric.get_name_value()summary_writer.add_scalar('loss epoch',train_ctc_loss,n_epoch)summary_writer.add_scalar('CER
指定日志保存路径 writer = SummaryWriter('runs/exp-1') # 模拟训练过程,假设我们有100个epoch for epoch in range(100): # 假设这是每个epoch的损失值 loss = torch.rand(1).item() # 生成一个随机数作为损失值 # 使用add_scalar方法记录损失值 writer.add_scalar('Loss/train', loss, epoch) # ...