guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs
Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 different datasets.
PythonApache-2.0
Stargazers
- ahhaa
- akansal1
- akshatdewan
- amitdo
- aslfuChina
- carsonfarmer@textileio @tablelandnetwork
- cfwin
- chinakook
- fly51flyPRIS
- gladuoBaidu
- goodloopShanghai
- gupengjuChengdu,China
- gzqhappy
- indiejosephSoft Butter Studio
- jayvischenghttp://git.oschina.net/designer357;http://blog.chinaunix.net/uid/29689451.html
- jiangplusshenzhen, china
- lucabellucciniFrance
- lx865712528@Microsoft Research
- marionleborgneAurora Innovation
- ngaloppo@apple
- PapaMadeleine2022
- pawelmilka
- rajshah4@snowflakedb
- sabyasm
- SnoopyBoyang电子科技大学,University of Electronic Science and Technology of China
- tensortalkYou're on TensorTalk.com!
- Timopheym@merantix
- twnming
- Vesperal-HunterUniversity of Birmingham
- wanxiaoyi
- wellbeing18
- wittmamz
- xspring14
- zhangbinchao
- zhaowenyi94Los Angeles, CA
- zhaoyu611xi'an