Hi guys, I have what could be a stupid question, but I see that I’m encountering this issue regularly and would like to know your opinion: so yesterday I was trying to improve my ML model in order to improve its accuracy, and found out that it was performing worse. Why? I checked the previous model architecture (saved with Keras plot_model) and saw what I did differently last week. No problem, I will just revert to that architecture and test again. Model overfits in half the epochs now. Damn, I also changed the dataset augmentation pipeline, now I cannot recreate those specific scores.

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Basically this is my issue, I happen to develop a model for n-days, test it, save it etc. then after a couple of weeks I try to revert to “that good model setup I was having” and I cannot get the same results anymore as I changed too much stuff. I marginally fixed it by saving the model architecture as png using Keras in order to have a quick visual comparison, It’s not the end of the world, but I don’t have a clean way to deal with this issue. How do you guys avoid such problems?

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Thank you!