tensorflow / tensorflow/recommenders
Which metric of model should I focus to stop training. Validation top N accuracy or validation loss ?
@maciejkula is already working on this.
Since Jun 3, 2022.
- Dominant language
- Python
- Stars
- 2k
- Forks
- 300
- PR merge metrics
- No merged PRs in 30d
Description
During training my model, I am facing some issues to stop training on right time.
Image-1 Green -- validation , pink -- training
Image-2 Green -- validation , pink -- training
As you can see in image-1 training and validation accuracy is increasing continuously, however loss graph (Image-2) seems strange to me. After epoch 20 validation loss started to rise although training loss is still dropping , which seems like overfitting .
My question is , should I train model as long as validation accuracy increases , or stop it as soon as validation loss starts to increase ?
Thanks for answers !
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Assessment
This issue has not been assessed yet.