Deep-Learning-with-PyTorch

Preventing overfitting with data augmentation
353
--balanced --augment-noise
sanity-bal-noise
$ .venv/bin/python -m p2ch12.training --epochs 20 \
--balanced --augmented sanity-bal-aug
While that’s running, we can start TensorBoard. Let’s direct it to only show these runs
by changing the logdir parameter like so: ../path/to/tensorboard --logdir
runs/p2ch12.
Depending on the hardware you have at your disposal, the training might take a
long time. Feel free to skip the flip, shift, and scale training jobs and reduce the
first and last runs to 11 epochs if you need to move things along more quickly. We
chose 20 runs because that helps them stand out from the other runs, but 11 should
work as well.
If you let everything run to completion, your TensorBoard should have data like
that shown in figure 12.22. We’re going to deselect everything except the validation
data, to reduce clutter. When you’re looking at your data live, you can also change the
smoothing value, which can help clarify the trend lines. Take a quick look at the figure,
and then we’ll go over it in some detail.
Unaugmented
Noise is
worse than
unaugmented
tag: correct/all tag: correct/neg tag: correct/pos
Unaugmented
Individual
Augments
FuLly
Augmented
tag: loss/all tag: loss/neg tag:
loss/pos
FuLly
Augmented
tag: pr/f1_score tag: pr/precision
p
tag:
pr/recall
FuLly Augmented
is Worse than unaugmented...
...Except for how unaugmented
is overfiTting on positive samples
Figure 12.22 Percent correctly classified, loss, F1 score, precision, and recall for the validation set from
networks trained with a variety of augmentation schemes