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

larger weights, while the class with more data points (majority class) should get
smaller weights. This way, on average, we’ll end up with mini-batches containing
roughly the same number of data points in each class: A balanced dataset.
"How are the weights computed?"
First, we need to find how imbalanced the dataset is; that is, how many data points
belong to each label. We can use PyTorch’s unique() method on our training set
labels (y_train_tensor), with return_counts equals True, to get a list of the
existing labels and the corresponding number of data points:
classes, counts = y_train_tensor.unique(return_counts=True)
print(classes, counts)
Output
tensor([0., 1.]) tensor([ 80, 160])
Ours is a binary classification, so it is no surprise we have two classes: zero (not
diagonal) and one (diagonal). There are 80 images with lines that are not diagonal,
and 160 images with diagonal lines. Clearly, an imbalanced dataset.
Next, we compute the weights by inverting the counts. It is as simple as that:
weights = 1.0 / counts.float()
weights
Output
tensor([0.0125, 0.0063])
The first weight (0.0125) corresponds to the negative class (not diagonal). Since
this class has only 80 out of 240 images in our training set, it is also the minority
class. The other weight (0.0063) corresponds to the positive class (diagonal), which
has the remaining 160 images, thus making it the majority class.
290 | Chapter 4: Classifying Images