Loss/Validation Plot Callback
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 20/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Start by reviewing pull request #163 and the existing .fit() callback behavior mentioned in the issue. Determine whether the requested loss plotting needs implementation or only usage guidance; the work is complete when a supported way to visualize training and validation convergence during fitting is established and demonstrated.
Written by the indexing model from the issue text.
Description
- MLPrimitives version: 0.2.5
- Python version: 3.6.9
- Operating System: Ubuntu 18.04.6
Description
Is it possible to plot training and validation losses during the .fit() call?
I saw there is this pull request https://github.com/MLBazaar/MLPrimitives/pull/163.
I did not see any examples of using a callback to plot losses during training.
Any other recommendation on how to visualize if models are converging during training would be most appreciated.
Thank you
Chris
- Dominant language
- Python
- Stars
- 70
- Forks
- 37
- PR merge metrics
- No merged PRs in 30d
Contributor guide
First steps
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- 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.
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