Allow turning off saving best model after epochs (DONE) and regression prediction instability (TODO)
See original GitHub issueSaving the current best model after epochs does not consume too much time if the training is for big models, but it could be a high cost for training small models. I have found no way to turn off the saving best model feature. Tried to set skip_save_progress_weights=True, skip_save_processed_input=True
but it did not help. Thanks
Issue Analytics
- State:
- Created 5 years ago
- Comments:9
Top Results From Across the Web
Allow turning off saving best model after epochs (DONE) and ...
I have found no way to turn off the saving best model feature. ... after epochs (DONE) and regression prediction instability (TODO) #96....
Read more >Keras Callbacks and How to Save Your Model from Overtraining
In this article, you will learn how to use the ModelCheckpoint callback in Keras to save the best version of your model during...
Read more >Use Early Stopping to Halt the Training of Neural Networks At ...
Early stopping is a method that allows you to specify an arbitrary large number of training epochs and stop training once the model...
Read more >AutoGluon Predictors
In this case, the 'best' model used by default for prediction will also be decided based on a weighted version of evaluation metric....
Read more >Saving your weights for each epoch — Keras callbacks
An interesting approach to solving this problem is to save your weights for each epoch or always save the best result, but how...
Read more >Top Related Medium Post
No results found
Top Related StackOverflow Question
No results found
Troubleshoot Live Code
Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start FreeTop Related Reddit Thread
No results found
Top Related Hackernoon Post
No results found
Top Related Tweet
No results found
Top Related Dev.to Post
No results found
Top Related Hashnode Post
No results found
Top GitHub Comments
Yes, it saves the best weights if the performance on validation improves. I can think about adding a
--skip_save_best_model
parameter, but then the model will never be saved. There could be some use cases where that could be useful (hyperparameters search for instance). I may also try to find a way to keep the best model weights copied in memory and just saved at the end. Will add this to the list of enhancements.The regression isntability issue should have been solved. Please reopen if it is still an issue.