JuliaAI / JuliaAI/MLJIteration.jl

Feedback from a serious user

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Dominant language
Julia
Stars
12
Forks
3
PR merge metrics
No merged PRs in 30d

Description

I received some feedback from a valued user which I thought might be useful to post here for the record:

I don't remember having many problems with IteratedModel, so I was able to do all I wanted with little effort. It provides a lot of options off-the-shelf in an intuitive way. I only needed to spend some time thinking about two things:

  • How to do learning-rate (LR) scheduling through this feature. A complication is that the Adam type is immutable, so whenever I wanted to update the LR, I had to get its value, alter it, and create a new Adam instance with it.
  • The difference between "control cycles" and Step(n) and how they relate to training batches and epochs when using NNs. Understanding all these "progress measures" is needed to define how often the LR will be updated. When using decay, if updates are too frequent, the LR vanishes too fast and training halts

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Review the IteratedModel and Adam usage described in the issue, focusing on learning-rate scheduling and the relationship between control cycles, Step(n), batches, and epochs. Done should make these concepts and scheduling choices understandable to users without requiring the original investigation.

Written by the indexing model from the issue text.

Assessment

Tech stack
julia
Domain
machine-learning
Issue type
Documentation
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
35/100

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