# training model = CNNmodel() print(model) criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) epochs = 50 model.train() start_time = time.time() for epoch in range(epochs): for seq, y_train in train_data: # The gradient is zeroed and initialized before each parameter update optimizer.zero_grad() # reshape # convert to conv1d input size（batch size, channel, series length） y_pred = model(seq.reshape(1,1,-1)) loss = criterion(y_pred, y_train) loss.backward() optimizer.step() print(f'Epoch: {epoch+1:2} Loss: {loss.item():10.8f}') print(f'\nDuration: {time.time() - start_time:.0f} seconds')报错name 'train_data' is not defined

这个错误说明变量train_data未定义，你需要确认是否已经定义了train_data变量并将其赋值为训练数据。如果没有定义，你需要定义train_data并将其赋值为你的训练数据。例如：

train_data = DataLoader(dataset, batch_size=1, shuffle=True)

请注意，这里的dataset应该是你的训练数据集，batch_size是你要使用的批量大小，shuffle是指是否对数据进行洗牌。你需要根据你的数据集和实验需求进行适当的设置。