def train(net, train_iter, val_iter, num_epochs, lr, wd, devices, lr_period, lr_decay): global val_acc, metric trainer = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9, weight_decay=wd) scheduler = torch.optim.lr_scheduler.StepLR(trainer, lr_period, lr_decay) num_batches, timer = len(train_iter), d2l.Timer() train_loss, train_accs, val_accs = [], [], [] for epoch in range(num_epochs): net.train() metric = d2l.Accumulator(3) for i, (features, labels) in enumerate(train_iter): trainer.zero_grad() features, labels = features.to(devices[0]), labels.to(devices[0]) l, acc = d2l.train_batch_ch13(net, features, labels, loss, trainer, devices) metric.add(l, acc, labels.shape[0]) train_loss.append(metric[0] / metric[2]) train_accs.append(metric[1] / metric[2]) if val_iter is not None: val_acc = d2l.evaluate_accuracy_gpu(net, val_iter) val_accs.append(val_acc) d2l.plot(range(1, epoch + 2), [train_loss, train_accs, val_accs], xlabel='epoch', legend=['train loss', 'train acc', 'val acc'], figsize=(8, 6)) scheduler.step() TypeError: 'torch.device' object is not subscriptable

这个错误通常是由于在 `for` 循环中使用了一个不是列表或元组类型的对象进行索引操作，而这个对象又是一个 `torch.device` 类型的对象。具体来说，在下面这行代码中，`devices` 参数传递进来的可能不是一个列表或元组，而是一个单独的 `torch.device` 类型的对象：

features, labels = features.to(devices[0]), labels.to(devices[0])

要解决这个问题，需要确保 `devices` 参数传递进来的是一个列表或元组类型的对象，而不是一个单独的 `torch.device` 类型的对象。可以在调用 `train` 函数时，将 `devices` 参数改为一个列表或元组类型的对象，如下所示：

devices = [torch.device('cuda:0'), torch.device('cuda:1')]  # 例子，具体的设备数量和编号根据实际情况而定
train(net, train_iter, val_iter, num_epochs, lr, wd, devices, lr_period, lr_decay)

如果你只使用了一个 GPU 设备，那么可以将 `devices` 参数改为如下形式：

devices = [torch.device('cuda:0')]
train(net, train_iter, val_iter, num_epochs, lr, wd, devices, lr_period, lr_decay)

这样就可以避免这个错误了。