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

First, let’s compute the logits and corresponding probabilities:
Evaluation
logits_val = sbs.predict(X_val)
probabilities_val = sigmoid(logits_val).squeeze()
Then, let’s visualize the probabilities on a line. It means we’re going from the fancy
contour plot to a simpler plot:
Figure 3.9 - Probabilities on a line
The left plot comes from Figure 3.8. It shows the contour plot of the probabilities
and the decision boundary as a straight gray line. We place the data points on a
line, according to their predicted probabilities. That’s the plot on the right.
The decision boundary is shown as a vertical dashed line placed at the chosen
threshold (0.5). Points to the left of the dashed line are classified as red, and
therefore have red edges around them, while those to the right are classified as
blue, and have blue edges around them.
The points are filled with their actual color, meaning that those with distinct
colors for edge and filling are misclassified. In the figure above, we have one blue
point classified as red (left) and two red points classified as blue (right).
Now, let’s make a tiny change to our plot to make it more visually interesting: We’ll
plot blue (positive) points below the probability line and red (negative) points
above the probability line.
242 | Chapter 3: A Simple Classification Problem