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.

Date Created 2016-11-26 (7 years ago)

Commits 67 (last one 2 years ago)

Stargazers 319 (0 this week)

Watchers 19 (0 this week)

Forks 100

License apache-2.0

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guillaume-chevalier/HAR-stacked-residual-bidir-LSTMs has 1 open pull request on Github, 4 pull requests have been merged over the lifetime of the repository.

Github issues are enabled, there are 2 open issues and 5 closed issues.

Homepage URL: https://arxiv.org/abs/1708.08989

All Topics

rnn lstm tensorflow stacked-layers residual-layers residual-lstm-cells bidirectional-lstm-cells human-activity-recognition

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updated: 2024-10-29 @ 10:43pm, id: 74831258 / R_kgDOBHXVmg