我被分配编写一个使用 nn.ModuleDict 的简单网络。以下是我的实现：
third_model = torch.nn.ModuleDict({'flatten': torch.nn.Flatten(),'fc1': torch.nn.Linear(32 * 32 * 3, 1024),'relu': torch.nn.ReLU(),'fc2': torch.nn.Linear(1024, 240),'relu': torch.nn.ReLU(),'fc3': torch.nn.Linear(240, 10)})然后我尝试使用 cuda 训练它：
third_model.to(device)criterion = nn.CrossEntropyLoss()optimizer = optim.SGD(third_model.parameters(), lr=0.001, momentum=0.9)train(third_model, criterion, optimizer, train_dataloader, test_dataloader)函数 “train(model, criterion, optimizer, train_dataloader, test_dataloader)” 用于训练模型并可视化模型的损失和准确率。它的工作正常。
训练函数如下：
def train(model, criterion, optimizer, train_dataloader, test_dataloader): train_loss_log = [] train_acc_log = [] val_loss_log = [] val_acc_log = [] for epoch in range(NUM_EPOCH): model.train() train_loss = 0. train_size = 0 train_acc = 0. for imgs, labels in train_dataloader: imgs, labels = imgs.to(device), labels.to(device) optimizer.zero_grad() y_pred = model(imgs) loss = criterion(y_pred, labels) loss.backward() optimizer.step() train_loss += loss.item() train_size += y_pred.size(0) train_loss_log.append(loss.data / y_pred.size(0)) _, pred_classes = torch.max(y_pred, 1) train_acc += (pred_classes == labels).sum().item() train_acc_log.append(np.mean((pred_classes == labels).cpu().numpy())) val_loss = 0. val_size = 0 val_acc = 0. model.eval() with torch.no_grad(): for imgs, labels in test_dataloader: imgs, labels = imgs.to(device), labels.to(device) pred = model(imgs) loss = criterion(pred, labels) val_loss += loss.item() val_size += pred.size(0) _, pred_classes = torch.max(pred, 1) val_acc += (pred_classes == labels).sum().item() val_loss_log.append(val_loss / val_size) val_acc_log.append(val_acc / val_size) clear_output() plot_history(train_loss_log, val_loss_log, 'loss') plot_history(train_acc_log, val_acc_log, 'accuracy') print('训练损失:', train_loss / train_size) print('训练准确率:', train_acc / train_size) print('验证损失:', val_loss / val_size) print('验证准确率:', val_acc / val_size)我已经训练了使用 nn.Sequential 编码的模型，一切正常。然而，使用 nn.ModuleDict 时，我遇到了一个错误：
TypeError Traceback (most recent call last)<ipython-input-144-8b33ad3aad2c> in <module>() 2 optimizer = optim.SGD(third_model.parameters(), lr=0.001, momentum=0.9) 3 ----> 4 train(third_model, criterion, optimizer, train_dataloader, test_dataloader)1 frames/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs) 720 result = self._slow_forward(*input, **kwargs) 721 else:--> 722 result = self.forward(*input, **kwargs) 723 for hook in itertools.chain( 724 _global_forward_hooks.values(),TypeError: forward() takes 1 positional argument but 2 were given我尝试查找有关 nn.ModuleDict 的任何文档，但似乎没有使用它编码网络的例子。
问题似乎可能出在线性层上，尽管我不知道为什么。
所以，我希望有人能解释一下错误出在哪里。将非常感谢任何可能的建议。
回答：
nn.moduleDict 是一个容器，它的 forward 函数没有定义。它应该用于存储子模块/网络。
你应该使用以有序字典 OrderedDict 初始化的 nn.Sequential：
third_model = torch.nn.Sequential( OrderedDict([ ('flatten', torch.nn.Flatten()), ('fc1', torch.nn.Linear(32 * 32 * 3, 1024)), ('relu', torch.nn.ReLU()), ('fc2', torch.nn.Linear(1024, 240)), ('relu', torch.nn.ReLU()), ('fc3', torch.nn.Linear(240, 10))]))