tensorflow / tensorflow/probability
how do you make variable-sized ("ragged") output distributions with tfp.layers?
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Description
We'd like to model a molecular problem with variable number of atoms.
How do you make a variable-sized / ragged distribution shape with tfp layers?
Time series doesnt make sense here. We'd like a distribution output so we can use the nice built in features of tfp distribution like entropy, etc. Tried Convolution1DFlipout but this retuens a tensor, not a distribution. Do we need to set batch_size=n_atoms?
Sorry if this is (another) noob question
Bionicles
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Research direction
Start by reading the tfp.layers distribution-producing layers and the Convolution1DFlipout API mentioned in the issue. Check how variable-sized or ragged inputs and distribution outputs are currently handled, then determine whether the requested behavior needs documentation or a new feature. Done means establishing a supported approach for variable numbers of atoms or clearly documenting that it is not supported.
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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
- 20/100