Hello,
Can the above program achieve the goal?
If the value of regression is (104, 180, 60) and the value of classification is laber(1, 2, 3, 4, …, 10). Need to process with loss? or not? (e.g., normalization)
What to do if needed?
    loss = loss_func(pre_class, y)     # must be (1. nn output, 2. target)
    loss2 = loss_func2(pre_regression, y)     # must be (1. nn output, 2. target)
    loss_total = loss +loss2

And,
does pytorch automatically update all parameters of the network?
def __init__(self, n_feature, n_hidden): 
    super(Net, self).__init__() 
    self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer 
    self.out = torch.nn.Linear(n_hidden, 4) # output layer 
    self.out2 = torch.nn.Linear(n_hidden, 3) # output layer

Thanks  ~ ￣U￣