JuliaDiff / JuliaDiff/ReverseDiff.jl
Potential bug all_results[1].value != fun(x)
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- Dominant language
- Julia
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- 393
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- Avg merge
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Description
I have runt into what I believe is a bug in ReverseDiff
I have made a gist with a program that should generate a weird behavior, along with the output of the program. I'm simply trying to fit a one layer neural network to some data. The function I differentiate is lossfun. When running the program, the output of all_results[1].value which I assume should be equal to lossfun, in fact diverges from lossfun after a while, having started out similar.
Program that reproduce bug, along with output: https://gist.github.com/baggepinnen/5c413f9ca12d5853672fd4e2d8dbdaea
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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
Start with the linked gist and its reproduced output, focusing on the lossfun calculation and all_results[1].value comparison. Trace when the values begin to diverge and identify whether ReverseDiff's differentiation or the surrounding neural-network fitting logic causes it. Done means the reproduced case no longer diverges, or the issue has a documented, verified cause.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100