torch.random.manual_seed(42);
acts = torch.randn((6,2))*2
acts
tensor([[ 0.6734, 0.2576],
[ 0.4689, 0.4607],
[-2.2457, -0.3727],
[ 4.4164, -1.2760],
[ 0.9233, 0.5347],
[ 1.0698, 1.6187]])
We can’t just take the sigmoid of this directly, since we don’t get rows that add to 1 (i.e., we want the probability of being a 3 plus the probability of being a 7 to add up to 1):
acts.sigmoid()
tensor([[0.6623, 0.5641],
[0.6151, 0.6132],
[0.0957, 0.4079],
[0.9881, 0.2182],
[0.7157, 0.6306],
[0.7446, 0.8346]])
We would expect that since this is just another way of representing the same problem, that we would be able to use sigmoid directly on the two-activation version of our neural net. And indeed we can! We can just take the difference between the neural net activations, because that reflects how much more sure we are of the input being a 3 than a 7, and then take the sigmoid of that:
(acts[:,0]-acts[:,1]).sigmoid()
tensor([0.6025, 0.5021, 0.1332, 0.9966, 0.5959, 0.3661])
q0)what does the “acts” tensor represent ?
q1.how does that reflects how much more sure we are of the input being a 3 than a 7?
q2. why is sigmoid function better for binary classification .
also I’m a beginner and hence these questions .