Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

classification: The predicted class is the one with the largest logit.
If there is a single column of labels, that would be a binary classification: The
predicted class will be the positive class if the predicted probability is above a
given threshold (usually 0.5). But there’s a catch here: If the last layer of the model
is not a sigmoid, we need to apply a sigmoid to the logits first to get the
probabilities, and only then compare them with the threshold.
Then, for each possible class, the method figures out how many predictions match
the labels, and appends the result to a tensor. The shape of the resulting tensor will
be (number of classes, 2), the first column representing correct predictions, the
second, the number of data points.
Let’s try applying this new method to the first mini-batch of our data loader:
sbs_cnn1.correct(images_batch, labels_batch)
Output
tensor([[5, 7],
[3, 3],
[6, 6]])
So, there are only two wrong predictions, both for class #0 (parallel lines),
corresponding to images #6 and #8, as we’ve already seen in the previous section.
"What if I want to compute it for all mini-batches in a data loader?"
Loader Apply
On it! That’s the role of the static method loader_apply(): It applies a function to
each mini-batch, and stacks the results before applying a reducing function such as
sum or mean.
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