# Suggestion on parameters for training scvi model

**URL:** <https://discourse.scverse.org/t/suggestion-on-parameters-for-training-scvi-model/1936>\
**Category:** scvi-tools\
**Tags:** integration, scvi\
**Created:** [December 4, 2023, 10:51am UTC](https://discourse.scverse.org/t/suggestion-on-parameters-for-training-scvi-model/1936 "2023-12-04T10:51:04Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![martinkim0](https://yyz1.discourse-cdn.com/flex035/user_avatar/discourse.scverse.org/martinkim0/32/881_2.png) [@martinkim0](https://discourse.scverse.org/u/martinkim0)\
**Post date:** [December 4, 2023, 5:35pm UTC](https://discourse.scverse.org/t/suggestion-on-parameters-for-training-scvi-model/1936/2 "2023-12-04T17:35:03Z")

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Hey, thanks for your question! I’d suggest passing in `check_val_every_n_epoch=1` into `train()` in order to compute validation losses as well. If you find that `elbo_validation`, `validation_loss`, and `reconstruction_loss_validation` are converging at the end of training, then your model is probably fine. Otherwise, you can tweak the learning rate or number of epochs depending on what’s going on. Hope this helps!

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