# Model converge at high loss (good fit bad performance?)

**URL:** <https://discuss.pytorch.org/t/model-converge-at-high-loss-good-fit-bad-performance/181410>\
**Category:** Uncategorized\
**Created:** [June 5, 2023, 12:23am UTC](https://discuss.pytorch.org/t/model-converge-at-high-loss-good-fit-bad-performance/181410 "2023-06-05T00:23:28Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Gears\_Gears](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/gears_gears/32/60932_2.png) [@Gears\_Gears](https://discuss.pytorch.org/u/Gears_Gears)\
**Post date:** [June 5, 2023, 12:23am UTC](https://discuss.pytorch.org/t/model-converge-at-high-loss-good-fit-bad-performance/181410/1 "2023-06-05T00:23:29Z")

</div>

Hi!

a: My model converges and looks like a good fit (training and validation loss follow each other closely)  
The problem is, they stop (go horizontal) at a value (0.15 something) that’s higher than I would like (0.05!)  
Since the loss converged, I can’t reduce it by training more (e.g. if I increase patience of early stopping, that just lets the model overfit)  
b: my model is a good fit only in cross-validation. But when I go to testing, the model overfits (5 to 10 times the loss)  
I’m confused about this, since the point of cross-validation is to make sure that the model generalizes well. In this case, cross-validation tells me the model generalizes well, but the testing tells me the model does not generalize well.

For problems ‘a’ and ‘b’ above, what should I try?

Many Thanks!