The division(sample)-weight will be calculated for every sample/recording ahead of time, so I guess it can also be returned with DataLoader.
The division count varies just because each recording is a different length, with the largest being about 60% longer than the smallest. It’s just the nature of the data.
So, in my previous models using keras, I did use a windowed approach. However, because tensorflow requires the complete tensor to exist ahead of time, this was a massive waste of memory, since almost all of the data is duplicated using a sliding window. And this massive waste is what led to keeping the windows smaller than I would’ve liked; since the model could only see a small window at a time, it could never grasp the long-range trends and cycles that happen in the data. I’m exploring a combination of spatial pyramid pooling (SPP) and the [TCN] architecture as a possible solution to the issue that the 1st feature for each division is very long (and in its unprocessed form, also varies in length (the sampling rate is different)), and I would also like the network to train on the entire recording at the same time, so it can “see” the long-range cycles.
Hopefully that all made sense.
(https://github.com/locuslab/TCN/tree/master/TCN 35)