序列模型常常会用到padding（nn.functional.pad()），而padding添加的就是0.0。例如：

    def train(self, input, real_val):
        self.model.train()
        self.optimizer.zero_grad()
        input = nn.functional.pad(input, (1,0,0,0))
        output = self.model(input)
        ......

而在计算loss的时候，并不需要计算padding部分的0，因此我们需要用到masked loss。例如：

def masked_mse(preds, labels, null_val=np.nan):
    if np.isnan(null_val):
        mask = ~torch.isnan(labels)
    else:
        mask = (labels != null_val)
    mask = mask.float()
    mask /= torch.mean((mask))
    mask = torch.where(torch.isnan(mask), torch.zeros_like(mask), mask)
    loss = (preds - labels) ** 2
    loss = loss * mask
    loss = torch.where(torch.isnan(loss), torch.zeros_like(loss), loss)
    return torch.mean(loss)