So I have a task where the net outputs different variables. Some outputs are bounded like [0,1], [0, +inf] and [-inf, +inf]. I want the network to handle these restrictions. My thought was to do

```
def forward(self, x):
x = self.net(x)
#print(x.shape), (bs, 5)
# say idx 0 is the variable with [0,1] bound so
x[:, 0] = nn.Sigmoid(x[:, 0])
# idx 1-3 is [0, +inf] bound so
x[:, 1:4] = nn.ReLU(x[:, 1:4])
# idx 5 is [-inf, +inf] so just left as is.
return x
```

Is this a valid approach? The model will be saved with jit.trace and served using the c++ api.

Offtopic. What you guys think of training with only MSE loss? Sure you can combine cross entrpoy loss for the 0-1 vars and MSE for the others but I wonder if it will have a major effect?