Deep-Learning-with-PyTorch

Predicting malignancy
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metrics_t[METRICS_PRED_P_NDX, posLabel_mask],
self.totalTrainingSamples_count,
bins=bins
Now we can take a look at our prediction distribution for benign samples and how it
evolves over each epoch. We want to examine two main features of the histograms in
figure 14.13. As we would expect if our network is learning anything, in the top row of
benign samples and non-nodules, there is a mountain on the left where the network is
very confident that what it sees is not malignant. Similarly, there is a mountain on the
right for the malignant samples.
But looking closer, we see the capacity problem of fine-tuning only one layer.
Focusing on the top-left series of histograms, we see the mass to the left is somewhat
spread out and does not seem to reduce much. There is even a small peak round 1.0,
and quite a bit of probability mass is spread out across the entire range. This reflects
the loss that didn’t want to decrease below 0.3.
Figure 14.13
TensorBoard histogram display for fine-tuning the head only