Setting weight to the NLLLoss for imbalanced data

[Aman_Singh]

WeightedRandomSampler might be helpful to you.
Here’s a discussion about it.

Balanced Sampling between classes with torchvision DataLoader

Ok, I found some other issues. I changed your code a bit. This should work: numDataPoints = 1000 data_dim = 5 bs = 100 data = torch.FloatTensor(numDataPoints, data_dim) target = np.hstack((np.zeros(int(numDataPoints * 0.9), dtype=np.int32), np.ones(int(numDataPoints * 0.1), dtype=np.int32))) dataset_x = data.numpy() dataset_y = target from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(dataset_x, …