FluxML/Flux.jl

More robust testing for AlphaDropout

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#1,851 opened on Jan 27, 2022

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 (6 comments) (0 reactions) (0 assignees)Julia (619 forks)batch import
good first issuehelp wantedtesting

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Description

We currently borrow from https://github.com/pytorch/pytorch/blob/v1.10.0/test/cpp/api/modules.cpp#L1337-L1338, but some sort of goodness-of-fit test would be more robust than a simple range. Sampling the layer outputs should have a negligible impact on test runtime.

Contributor guide

Research direction
Implement a goodness of fit test for the AlphaDropout layer in Flux.jl. This involves writing a statistical test (e.g., Chi squared or Kolmogorov Smirnov) to verify that the output distribution matches the expected distribution. The test should be placed in the existing test suite for modules.
Tech stack
None
Domain
machine learning
Issue type
Test
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
Half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
JuliaFlux.jlstatistical testing
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
75