epochs = 100    #训练的次数

loss_epoch = []  # 记录损失

for epoch in range(epochs):

    total_loss = 0.0   # 损失初始化为0

    train_samples = 0  # 训练样本数初始化为0

    for train_x, train_y in dataloader:

        # 将1个batch的数据送给模型

        y_pred = mode1(train_x.type(torch.float32))

        # 计算损失

        loss = loss_fn(y_pred, train_y.type(torch.float32))

        # 梯度清零

        optimizer.zero_grad()

        # 自动微分

        loss.backward()

        # 优化更新参数

        optimizer.step()

        # 累计损失和样本数

        total_loss += loss.item()

        train_samples += len(train_y)

    # 计算平均损失

    epoch_loss = total_loss / train_samples

    # 记录平均损失

    loss_epoch.append(epoch_loss)

报错：

d:\miniconda3\envs\DeepLearn\Lib\site-packages\torch\nn\modules\loss.py:626: UserWarning: Using a target size (torch.Size([32])) that is different to the input size (torch.Size([32, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.

  return F.mse_loss(input, target, reduction=self.reduction)

d:\miniconda3\envs\DeepLearn\Lib\site-packages\torch\nn\modules\loss.py:626: UserWarning: Using a target size (torch.Size([4])) that is different to the input size (torch.Size([4, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.

  return F.mse_loss(input, target, reduction=self.reduction)

这也太不巧了，我的是intel的显卡

请问老师，我这个怎么恢复：

sudo docker run hello-world

报错信息如下：

Unable to find image 'hello-world:latest' locally

docker: Error response from daemon: Get "https://registry-1.docker.io/v2/": net/http: request canceled while waiting for connection (Client.Timeout exceeded while awaiting headers).

See 'docker run --help'.

[root@localhost yum.repos.d]#