Hello ptrblck,
Thanks so much.
That is exactly my problem. The model does not predict the same results if :
A. I divide my train and test sets and train + model.eval() it by batches
B. I divide my train and test sets and train by batches and create a loop to test it with model.eval() day-by-day
C. I divide my train and test sets and train by batches, save it, re-load it and test it with model.eval()
BUT… I didn’t use model.eval() whose utility I discovered thanks to you, hence the instability.
I also faced another source of instability, even with model.eval(), and that comes from the dropout in LSTM. Removing it made the results stable, but less appealing.
The next task is to get back the quality of results I had before introducing model.eval() while keeping the reproducibility even in daily predictions.
THX again !