Tuesday, January 31st, 2023 - Saturday, February 4th, 2023

Save the resampled features of every subject separately to lift the restrictions of the tf.data buffer size.

Save different versions of .ipynb files and rename them into generator, backup, and dev.

Refactor the code by using sklearn.model_selection.LeaveOneGroupOut to two for loops.

Theoretically, it should give almost the same outputs. But the outputs from the for loops are slightly worse than those from the LeaveOneGroupOut. And the training time is about two times of the file using LeaveOneGroupOut. Damn!

Put the prediction parameter preds out of the training.py file and do the spotting and evaluation when all training loops are finished. By doing this, I can debug the spotting and evaluation without repeating the training step.

The training for macro expression spotting costs more than a day. I don't know what to do.