Optimizer classes lack clear documentation of expected Tensor interface
Nobody has claimed this yet.
Assessment
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Newbie friendliness
- 88/100
- Issue type
- Documentation
- Clarity
- Clearly specified
- Activity status
- Active
- Tech stack
- python
- Domain
- ai, documentation, machine-learning
Research direction
Open leanpass/optim.py and add class/method docstrings on SGD and Adam. State that params must expose .data and .grad, that gradients accumulate in param.grad, and whether weight_decay is L2 or decoupled. Done when those interfaces and semantics are explicit in the docstrings.
Written by the indexing model from the issue text.
Description
File: leanpass/optim.py
Both SGD and Adam in leanpass/optim.py assume the passed params are objects with .data and .grad attributes, but this is not documented. Users may pass raw NumPy arrays or other objects and encounter obscure attribute errors. Add docstrings that explicitly state the required interface, mention that gradients are accumulated in param.grad, and clarify the semantics of weight_decay (L2 vs decoupled).
Filed automatically by ai-issue-scan.
- Dominant language
- Python
- Stars
- 16
- Forks
- 1
- Avg merge
- 2d 8h
- Merged PRs (30d)
- 5
Getting set up
- No Dockerfile or Docker Compose file
- Has a pull request template
- Read the contributing 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.
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