LuxDL/Lux.jl

Higher level recurrent layers

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#972 opened on Oct 7, 2024

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good first issue

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Description

Hi! While it is relatively straightforward to implement higher level constructions like LSTM and GRU it's also nice to have a layer provided by the library like PyTorch does, having details like bidirectionality and dropout taken care of. Are there any plans to include this in Lux as well?

Thanks!

Contributor guide

Research direction
Review Lux’s existing recurrent layer source code and compare with PyTorch’s RNN implementation. Identify gaps and propose a plan to add LSTM and GRU layers with support for bidirectionality and dropout.
Tech stack
julia
Domain
machine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
4
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
3-5 days
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
Juliadeep learning basics
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
35