Dear all,

Currently I am building a neural net to estimate the uncertainty in a regression, which is performed by the neural net. I will use a custom loss to update the weights of the neurons. The custom loss consits of two values, which are the outputs of the neural net. For now I am using nn.MSEloss() the perform the regression. I’m quite unsure how exactly i can implement two outputs. I would be thankful for any help regrading this issue. My Code is displayed below

```
class NN(nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(NN, self).__init__()
self.hidden = nn.Linear(n_feature, n_hidden)
self.predict = nn.Linear(n_hidden, n_output)
#probably here i can define the outputs
def forward(self, x):
x = torch.sigmoid(self.hidden(x))
x = self.predict(x)
#define the activation func for the outputs
return x
net = NN(n_feature=1, n_hidden=10, n_output=1)
#create the optimizer
optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
def MyLoss():
#takes outputs of the neural net to compute
return None
n_iter = 200
#training loop
for i in range(n_iter):
prediction = net(x) # input x and predict based on x
loss = My_Loss()
optimizer.zero_grad() # zero the gradient buffers
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
.
.
.
```