Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Output
array([[0.5504593 ],
[0.94999564],
[0.9757515 ],
[0.22519748]], dtype=float32)
Now we’re talking! These are the probabilities, given our model, of those four
points being positive examples.
Lastly, we need to go from probabilities to classes. If the probability is greater than
or equal to a threshold, it is a positive example. If it is less than the threshold, it is a
negative example. Simple enough. The trivial choice of a threshold is 0.5:
Equation 3.19 - From probabilities to classes
But the probability itself is just the sigmoid function applied to the logit (z):
Equation 3.20 - From logits to classes, via sigmoid function
But the sigmoid function has a value of 0.5 only when the logit (z) has a value of
zero:
Equation 3.21 - From logits to classes, directly
Thus, if we don’t care about the probabilities, we could use the predictions (logits)
directly to get the predicted classes for the data points:
Model Training | 235