I was training a model on my own bug data and hit a wall with overfitting

I mean, my pest control business has years of local infestation records, so I tried to make a simple AI to predict seasonal spikes. After about 500 entries, it just memorized my old data and failed on new stuff. I switched to using a dropout layer in TensorFlow with a 0.3 rate, and that actually helped it generalize better. Has anyone else found a specific trick that worked when your model was learning the training set too well?

4 comments

4 Comments

mary4142mo ago

Dropout is a great start! You might also try adding some noise to your training data, like small random changes to the dates or counts. That can stop the model from latching onto exact numbers. Early stopping helped me too, just cutting off training once the validation loss stops going down.

the_jana2mo ago

Adding noise to the data is a solid idea from @mary414. It forces the model to learn the real patterns instead of just memorizing the numbers. Early stopping is a must too, saves so much time and compute. The combo of those two usually fixes a lot of overfitting issues for me.

dakota4152mo ago

Adding noise like @mary414 said really saved my last project.

the_lee1mo ago

What about messing with the order of your training data every time? Shuffle it up real good each round so the model can't just learn the sequence. I tried that once when noise alone wasn't cutting it, and it broke the model out of a rut. It's like making it solve the same puzzle but with the pieces in a different spot each time.