rlworkgroup/garage

Add support for multiple layers for recurrent network models

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#709 创建于 2019年6月1日

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 (0 评论) (0 反应) (0 负责人)Python (1,728 star) (297 fork)batch import
featuregood first issuetf

描述

Current recurrent network models, e.g. LSTMModel, only take single recurrent layer, which may not be sufficient for learning complex tasks. We should support multiple layers.

贡献者指南

技术栈
python
领域
machine learningbackend
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。
3
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。
1-2 days
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。
mostly clear
前置要求
Python programmingRecurrent neural networksgarage codebase structure
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。
40
研究方向
Examine the current implementation of LSTMModel and other recurrent models in the garage codebase (likely in garage/torch/models). Understand how the single recurrent layer is used. Add a 'num layers' parameter to the model constructor and modify the forward pass to stack multiple RNN layers. Ensure backward compatibility and add unit tests. Check for any existing discussions or PRs related to this issue.
Add support for multiple layers for recurrent network models · rlworkgroup/garage#709 | Good First Issue