我正在使用household_power_consumption.txt数据集解决一个多变量时间序列预测问题，并实现了一个1D CNN模型。数据加载的批量大小为64，序列长度为50，有7个特征。预处理完成。下面提供了模型代码。请帮助我解决模型维度不匹配的问题。

class CNN_ForecastNet(nn.Module):
    def __init__(self):
        super(CNN_ForecastNet,self).__init__()
        self.conv1d = nn.Conv1d(50,200,kernel_size=1)
        self.relu = nn.ReLU(inplace=True)
        self.drop_out = nn.Dropout(0.5)
        self.max_pooling = nn.MaxPool1d(1)
        self.fc1 = nn.Linear(200,100)
        self.fc2 = nn.Linear(100,1)
        
    def forward(self,x):
        x = self.conv1d(x)
        x = self.relu(x)
        x = self.drop_out(x)
        x = self.max_pooling(x)
        x = x.view(x.size(0),-1)
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        
        return x
    
    
model = CNN_ForecastNet()
train_losses = []
valid_losses = []
def Train():
    
    running_loss = .0
    
    model.train()
    
    for idx, (inputs,labels) in enumerate(train_loader):
        optimizer.zero_grad()
        preds = model(inputs.float())
        loss = criterion(preds,labels)
        loss.backward()
        optimizer.step()
        running_loss += loss
        
    train_loss = running_loss/len(train_loader)
    train_losses.append(train_loss.detach().numpy())
    
    print(f'train_loss {train_loss}')
    
def Valid():
    running_loss = .0
    
    model.eval()
    
    with torch.no_grad():
        for idx, (inputs, labels) in enumerate(test_loader):
            optimizer.zero_grad()
            preds = model(inputs.float())
            loss = criterion(preds,labels)
            running_loss += loss
            
        valid_loss = running_loss/len(test_loader)
        valid_losses.append(valid_loss.detach().numpy())
        print(f'valid_loss {valid_loss}')

epochs = 10
for epoch in range(epochs):
  if epoch % 2==0:

    print('epochs {}/{}'.format(epoch+1,epochs))
    Train()
    Valid()
    gc.collect()

字符串
错误如下所示

Traceback (most recent call last):

  Cell In[15], line 73
    Train()

  Cell In[15], line 41 in Train
    preds = model(inputs.float())

  File ~\anaconda3\Lib\site-packages\torch\nn\modules\module.py:1501 in _call_impl
    return forward_call(*args, **kwargs)

  Cell In[15], line 17 in forward
    x = self.fc1(x)

  File ~\anaconda3\Lib\site-packages\torch\nn\modules\module.py:1501 in _call_impl
    return forward_call(*args, **kwargs)

  File ~\anaconda3\Lib\site-packages\torch\nn\modules\linear.py:114 in forward
    return F.linear(input, self.weight, self.bias)

RuntimeError: mat1 and mat2 shapes cannot be multiplied (64x1400 and 200x100)

型