Deep-Learning-with-PyTorch

Predicting malignancy
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fine-tuning. It probably would be good to pull up a few images of that type to see
what’s happening.
ROC AND OTHER CURVES IN TENSORBOARD
As mentioned earlier, TensorBoard does not natively support drawing ROC curves. We
can, however, use the ability to export any graph from Matplotlib. The data preparation
looks just like in section 14.5.2: we use the data that we also plotted in the histogram
to compute the TPR and FPR—tpr and fpr, respectively. We again plot our
data, but this time we keep track of pyplot.figure and pass it to the SummaryWriter
method add_figure.
Listing 14.12
training.py:482, .logMetrics
Sets up a new Matplotlib figure. We usually don’t need it Uses arbitrary pyplot functions
because it is implicitly done in Matplotlib, but here we do.
fig = pyplot.figure()
Adds our figure to TensorBoard
pyplot.plot(fpr, tpr)
writer.add_figure('roc', fig, self.totalTrainingSamples_count)
Because this is given to TensorBoard as an image, it appears under that heading. We
didn’t draw the comparison curve or anything else, so as not to distract you from the
actual function call, but we could use any Matplotlib facilities here. In figure 14.15, we
see again that the depth-2 fine-tuning (left) overfits, while the head-only fine-tuning
(right) does not.
Figure 14.15
Training ROC curves in TensorBoard. A slider lets us go through the iterations.