Thank you for answering the question! I will try this.
I have a follow-up question: Using 10 dataloaders is the best way to do this? Because then there is much copy-paste code — maybe there is a cleaner way?
Also 2nd follow-up question: If I don’t want the same distribution for each part (so not stratified) but want to have a random distribution, then this is still easy to do (just changing the argument of the sklearn function).
But what if I want to create my own distributions, e.g. let’s say data part 1 consisting of: 50% of class 3, 10% of class 7, and remaining 8 classes have 5% each. Data part 2 consisting of: 15% of class 4, 15% of class 5, 15% of class 9, 15% of class 10, and the remaining 6 classes have 5% each. Data part 3 consting of: 30% of class 9, 20% … and so on, I think you know what I mean.
Is there a way to create this detailed data split in PyTorch or Sklearn? I guess the best way to do this data split/data preparation is not in PyTorch but with Numpy, Pandas, Vanilla Python whatever. And then load the 10 data parts into PyTorch. Can you confirm it?