2
$\begingroup$

I have a neural network that starts to overfit in that the validation loss begins to increase while the training loss stays ~ flat with epochs.

Is there a generic algorithm - obvious or otherwise, well-known or not - to stop the training early if overfitting is somehow detected?

I note that catboost implements such an algorithm but I have found it nowhere else.

https://catboost.ai/docs/concepts/overfitting-detector.html

Is this all simply a matter of rolling my own callback function and stopping when the training and validation losses start to diverge..?

Preference for TF, Keras, python3, ...

Thanks as ever

$\endgroup$
2
$\begingroup$

Sounds like you're just looking for EarlyStopping, which will stop training when validation loss does not improve for N epochs. It's the same as Iter in catboost.

| improve this answer | |
$\endgroup$
  • $\begingroup$ Perfect, thank you so much! $\endgroup$ – jtlz2 May 18 at 8:48

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.