LuxDL/Lux.jl

Higher level recurrent layers

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#972 创建于 2024年10月7日

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 (2 评论) (0 反应) (0 负责人)Julia (700 star) (84 fork)batch import
good first issue

描述

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!

贡献者指南

技术栈
无
领域
machine learning
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
4
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
over 1 week
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
stale
清晰度议题是否清楚说明期望改动、验收标准和下一步。
unclear
前置要求
Julia programmingRecurrent neural networksLux.jl internals
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
30
研究方向
Research the existing Lux layer API in the Lux.jl source code to understand how new layers are added. Study PyTorch's RNN implementation for reference on handling bidirectionality and dropout. Engage with maintainers in the issue comments to clarify requirements and expected API design.