Thank you in the time since ive been working with BCEWithLogitsLoss and also have discovered the applying sigmoid to the output idea. my only concern is the raw numbers I’m outputting are as follow:
guessright = net(torch.tensor([6, 0, 1, 0, 0.3971354166666663]))
guessleft = net(torch.tensor([6, 0, 1, 0, -0.3971354166666663]))

print("right", (guessright > 0.5).float())
print("left", (guessleft > 0.5).float())
print("right Sigmoid",  torch.round(torch.sigmoid(guessright)))
print("left Sigmoid", torch.round(torch.sigmoid(guessright)))
print("right", (guessright))
print("left", (guessleft))

right tensor([0., 1., 0., 0.])
left tensor([0., 1., 0., 0.])
right Sigmoid tensor([0., 1., 0., 0.], grad_fn=<RoundBackward>)
left Sigmoid tensor([0., 1., 0., 0.], grad_fn=<RoundBackward>)
right tensor([-4.9131,  1.6094, -4.4618, -1.3654], grad_fn=<AddBackward0>)
left tensor([-4.4941,  1.5184, -5.1282, -1.4909], grad_fn=<AddBackward0>)

The idea is i have 2 examples to test but the raw data coming out i was in my head expecting to be between 0-1 but the raw numbers are any number is this normal behavior?
I’m just trying to understand if the out put that is given is actually what its meant to be or if my conversions are flawed