tensorflow / tensorflow/probability
No Layer Benchmarks Available
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.4k
- Forks
- 1.1k
- PR merge metrics
- No merged PRs in 30d
Description
We want to try TFP layers, but there are many, and it's unclear from docs which are better for what scenarios
Instead of each user guessing / experimenting on layer choices (multiple days * many people), would it be possible to make standard benchmarks for the layers against deterministic counterparts from tf.keras.layers?
Just simple standard datasets in vision / nlp / data science would be super handy
This way newbs avoid wasting days grid-searching over different classes of layers
Thanks in advance,
Bionicles
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- 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.
Research direction
No files, tests, entry points, datasets, or benchmark criteria are named. Start by reviewing the TFP layers and their deterministic counterparts in tf.keras.layers, then define standard vision, NLP, and data-science comparisons. Done would mean documented benchmarks that clarify which layer choices suit each scenario.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, keras
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100