Deep-Learning-with-PyTorch

Exercises
355
12.8 Exercises
1 The F1 score can be generalized to support values other than 1.
a Read https://en.wikipedia.org/wiki/F1_score, and implement F2 and F0.5
scores.
b Determine which of F1, F2, and F0.5 makes the most sense for this project.
Track that value, and compare and contrast it with the F1 score. 6
2 Implement a WeightedRandomSampler approach to balancing the positive and
negative training samples for LunaDataset with ratio_int set to 0.
a How did you get the required information about the class of each sample?
b Which approach was easier? Which resulted in more readable code?
3 Experiment with different class-balancing schemes.
a What ratio results in the best score after two epochs? After 20?
b What if the ratio is a function of epoch_ndx?
4 Experiment with different data augmentation approaches.
a Can any of the existing approaches be made more aggressive (noise, offset,
and so on)?
b Does the inclusion of noise augmentation help or hinder your training
results?
– Are there other values that change this result?
c Research data augmentation that other projects have used. Are any applicable
here?
– Implement “mixup” augmentation for positive nodule candidates. Does it
help?
5 Change the initial normalization from nn.BatchNorm to something custom, and
retrain the model.
a Can you get better results using fixed normalization?
b What normalization offset and scale make sense?
c Do nonlinear normalizations like square roots help?
6 What other kinds of data can TensorBoard display besides those we’ve covered
here?
a Can you have it display information about the weights of your network?
b What about intermediate results from running your model on a particular
sample?
– Does having the backbone of the model wrapped in an instance of
nn.Sequential help or hinder this effort?
6
Yep, that’s a hint it’s not the F1 score!