How do I use class weights in PyTorch for imbalanced datasets?

How do I use class weights in PyTorch for imbalanced datasets?

You can use the weight parameter in the loss function to apply class weights. First, calculate the weights based on the class distribution.

Can you show me an example of calculating class weights?

Sure! You can use sklearn.utils.class_weight to compute the weights. Here's a quick example:

from sklearn.utils import class_weight
import torch

classes = [0, 1]
class_weights = class_weight.compute_class_weight('balanced', classes, y)
weights = torch.tensor(class_weights, dtype=torch.float)

How do I apply these weights in my loss function?

You can pass the weights to the loss function like this:

criterion = nn.CrossEntropyLoss(weight=weights)

Is there anything else I should consider when using class weights?

Yes, make sure to monitor your model's performance on both classes, as class weights can sometimes lead to overfitting on the minority class.