tensorflow / tensorflow/probability
Best way to use/implement MixtureLogistic layer with tfp in VAE setting
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 4.4k
- Forks
- 1.1k
- PR merge metrics
- No merged PRs in 30d
Description
Hi,
I would be interested in using a discretized mixture of logistic layer for a Decoder in a VAE settings (similar to NVAE). Exploring the tfp API, it seems like the most appropriate layer would be the MixtureLogistic layer. Does it make sense? Would it work in tf.keras model built with keras functional API?
I will just try but wanted to ask whether you would suggest any other approach, or whether, for that purpose, I should go on and re-write the layer in TF (saw some implementations in pytorch around).
Thank you for any feedback!
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
The issue names the TFP MixtureLogistic layer, tf.keras Functional API, and the NVAE paper, but no repository files or tests. Start by reviewing the linked layer API and the NVAE decoder usage; a useful outcome would be a confirmed integration approach or a documented alternative.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100