Loss Function & Its Inputs For Binary Classification PyTorch

For binary outputs you can use 1 output unit, so then:

self.outputs = nn.Linear(NETWORK_WIDTH, 1)

Then you use sigmoid activation to map the values of your output unit to a range between 0 and 1 (of course you need to arrange your training data this way too):

def forward(self, x):
    # other layers omitted
    x = self.outputs(x)           
    return torch.sigmoid(x)  

Finally you can use the torch.nn.BCELoss:

criterion = nn.BCELoss()

net_out = net(data)
loss = criterion(net_out, target)

This should work fine for you.

You can also use torch.nn.BCEWithLogitsLoss, this loss function already includes the sigmoid function so you could leave it out in your forward.

If you, want to use 2 output units, this is also possible. But then you need to use torch.nn.CrossEntropyLoss instead of BCELoss. The Softmax activation is already included in this loss function.

Edit: I just want to emphasize that there is a real difference in doing so. Using 2 output units gives you twice as many weights compared to using 1 output unit.. So these two alternatives are not equivalent.