Make the validation sets completely separate/not same sitting as the training data

[Sentdex]

Pulling random samples from the entire set causes over-fitting far too easily.
Pulling random 10 second sessions from the sets also causes over-fitting, just to a lesser degree.

Going to try instead to make the validation set the latest n sessions, with the goal of having the validation set be a completely different day/setting as any of the training data.

[rcox771]

hey @Sentdex . I didn't have much luck escaping over-fitting either, but I figured I'd still share my results.

I took a slightly different approach and used the average across the channel dimension, resized the resulting image to a 13x13 instead, and flattened that image for input into a mlp. Here's my notebook as a gist.

I also tried:

• averaging the matrices out over 5, 10, and 30 minute intervals; and
• fitting UMAPs to everything, but couldn't get a decent separation on either set, so I didn't include them in the gist.

Anywho, here's 5 samples after the transform for each:

and my confusion matrices:

Looking forward to helping test out the next dataset iteration.

-Russell

[RaghavendraGaleppa]

Hello I am able to hit 70% validation accuracy on the data inside validation_data folder.
You can check it out here: https://colab.research.google.com/drive/1v_vis7XpTQE0ANDoxK1Jr8_mzBFSbjF-