Deep-Learning-with-PyTorch

428 CHAPTER 14 End-to-end nodule analysis, and where to go next
14.5.4 More output in TensorBoard
While we are retraining the model, it might be worth looking at a few more outputs
we could add to TensorBoard to see how we are doing. For histograms, TensorBoard
has a premade recording function. For ROC curves, it does not, so we have an opportunity
to meet the Matplotlib interface.
HISTOGRAMS
We can take the predicted probabilities for malignancy and make a histogram of
them. Actually, we make two: one for (according to the ground truth) benign and one
for malignant nodules. These histograms give us a fine-grained view into the outputs
of the model and let us see if there are large clusters of output probabilities that are
completely wrong.
NOTE In general, shaping the data you display is an important part of getting
quality information from the data. If you have many extremely confident correct
classifications, you might want to exclude the leftmost bin. Getting the
right things onscreen will typically require some iteration of careful thought
and experimentation. Don’t hesitate to tweak what you’re showing, but also
take care to remember if you change the definition of a particular metric without
changing the name. It can be easy to compare apples to oranges unless
you’re disciplined about naming schemes or removing now-invalid runs of data.
We first create some space in the tensor metrics_t holding our data. Recall that we
defined the indices somewhere near the top.
Listing 14.10
training.py:31
METRICS_LABEL_NDX=0
METRICS_PRED_NDX=1
METRICS_PRED_P_NDX=2
METRICS_LOSS_NDX=3
METRICS_SIZE = 4
Our new index, carrying the prediction probabilities
(rather than prethresholded predictions)
Once that’s done, we can call writer.add_histogram with a label, the data, and the
global_step counter set to our number of training samples presented; this is similar
to the scalar call earlier. We also pass in bins set to a fixed scale.
Listing 14.11
training.py:496, .logMetrics
bins = np.linspace(0, 1)
writer.add_histogram(
'label_neg',
metrics_t[METRICS_PRED_P_NDX, negLabel_mask],
self.totalTrainingSamples_count,
bins=bins
)
writer.add_histogram(
'label_pos',