Deep-Learning-with-PyTorch

320 CHAPTER 12 Improving training with metrics and augmentation
1. Guard dogs
2. Birds and
burglars
5. Balancing
POS
NEG
6.
Augmentation
3. Ratios recaLl
and precision
4. new metric:
f1 score
7. Workin’ great!
Figure 12.2
magnificent
The metaphors we’ll use to modify the metrics measuring our model to make it
at the how the resulting values change epoch by epoch during training. Finally, we’ll
make some much-needed changes to our LunaDataset implementation with an aim at
improving our training results: (5) Balancing and (6) Augmentation. Then we will see
if those experimental changes have the expected impact on our performance metrics.
By the time we’re through with this chapter, our trained model will be performing
much better: (7) Workin’ Great! While it won’t be ready to drop into clinical use just
yet, it will be capable of producing results that are clearly better than random. This
will mean we have a workable implementation of step 4, nodule candidate classification;
and once we’re finished, we can begin to think about how to incorporate steps 2
(segmentation) and 3 (grouping) into the project.
12.2 Good dogs vs. bad guys: False positives and false negatives
Instead of models and tumors, we’re going to consider the two guard dogs in figure
12.3, both fresh out of obedience school. They both want to alert us to burglars—a
rare but serious situation that requires prompt attention.
Unfortunately, while both dogs are good dogs, neither is a good guard dog. Our
terrier (Roxie) barks at just about everything, while our old hound dog (Preston)
barks almost exclusively at burglars—but only if he happens to be awake when they
arrive.