Comments (4)
lena-kashtelyan
commented on April 19, 2024
Now that you are able to plot the outcomes, do the results look like they make more sense, @glenn-jocher?
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glenn-jocher
commented on April 19, 2024
@lena-kashtelyan ah, unfortunately the plotting problem #83 persists (it seems to not be numpy related, which was solved in #82). You can reproduce the plotting problem directly by running the tutorial notebook, which I have in a Google Colab:
https://colab.research.google.com/drive/1P6TvA9UZDtLf9dMFTcYWm_RUBW0wpsiV#scrollTo=DMsfBROgGSBK
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lena-kashtelyan
commented on April 19, 2024
@glenn-jocher, let's wait until we have the plots of this? I think it's just fine that the results are different, since optimization is stochastic and there is likely more than one optimal hyperparameter setting in your setup.
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glenn-jocher
commented on April 19, 2024
@lena-kashtelyan yes, sure I agree. Hyperparameter tuning will always be a bit mystifying I'm afraid, at least for the foreseeable future.
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Related Issues (20)
- Question: SEBO optimization with parameter dependency | logistic parameter constrains HOT 4
- Tutorial Request: Deploying Runners on Clusters, Debugging Runners/Schedulers HOT 4
- Issue with tolerance for floating point and its relevance when using log_scale = True HOT 7
- Question: does Ax support working with Tensorflow models? HOT 2
- Feature Request: Conditional Parameter Constraints HOT 5
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