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

The value z above is the output of our model. It is a "glorified linear regression!"
And this is a classification problem! How come?! Hold that thought; it will become
more clear in the next section, "Decision Boundary".
But, before going down that road, I would like to use our model (and the
StepByStep class) to make predictions for, say, the first four data points in our
training set:
Making Predictions (Logits)
predictions = sbs.predict(x_train_tensor[:4])
predictions
Output
array([[ 0.20252657],
[ 2.944347 ],
[ 3.6948545 ],
[-1.2356305 ]], dtype=float32)
Clearly, these are not probabilities, right? These are logits, as expected.
We can still get the corresponding probabilities, though.
"How do we go from logits to probabilities," you ask, just to make
sure you got it right.
That’s what the sigmoid function is good for.
Making Predictions (Probabilities)
probabilities = sigmoid(predictions)
probabilities
234 | Chapter 3: A Simple Classification Problem