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

Figure B.3 - Annotated model diagram
Decision Boundary, Activation Style!
And then I have a question for you:
"What does the decision boundary drawn by a logistic regression look
like?"
It is a straight line! We’ve seen this in Chapter 3 already. Before proceeding, let’s
make a quick summary to organize our findings so far:
• In the original feature space (x 0 and x 1 ), depicted in Figure B.1, it is impossible to
draw a straight line that separates the red and blue parabolas.
• In the transformed feature space (z 0 and z 1 ), it is still impossible to draw a
straight line that separates both parabolas, since the affine transformation
preserves parallel lines.
• In the activated feature space, (f(z 0 ) and f(z 1 )), where f is an activation function of
our choice, it becomes possible to draw a straight line that separates the red
and blue parabolas.
Even better, let’s look at the result of a trained model (using a sigmoid as activation
function f).
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