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

Figure 5.22 - Feature maps (classifier)
The hidden layer performed an affine transformation (remember those?), reducing
the dimensionality from sixteen to ten dimensions. Next, the activation function, a
ReLU, eliminated negative values, resulting in the "activated" feature space in the
middle row.
Finally, the output layer used these ten values to compute three logits, one for
each class. Even without transforming them into probabilities, we know that the
largest logit wins. The largest logit is shown as the brightest pixel, so we can tell
which class was predicted by looking at the three shades of gray and picking the
index of the brightest one.
The classifier got eight out of ten right. It made wrong predictions for images #6
and #8. Unsurprisingly, these are the two images that got their vertical lines
suppressed. The filter doesn’t seem to work so well whenever the vertical line is
too close to the left edge of the image.
"How good is the model actually?"
Good question! Let’s check it out.
Accuracy
In Chapter 3, we made predictions using our own predict() method and used
Scikit-Learn’s metrics module to evaluate them. Now, let’s build a method that also
takes features (x) and labels (y), as returned by a data loader, and that takes all
necessary steps to produce two values for each class: the number of correct
predictions and the number of data points in that class.
Visualizing Filters and More! | 407