text: Title: why use x_max while computing softmax in function log_sum_exp()?
  Body: def log_sum_exp(x)    
      x_max = x.data.max()
      return torch.log(torch.sum(torch.exp(x-x_max), 1, keepdim=True)) + x_max
  In this function ,if we remove x_max,the output of this function is just the same,so why should we use the x_max ?