The sensor-based-human-activity-recognition-deepconvlstm-pytorch from yunniyyy

sensor-based-human-activity-recognition-deepconvlstm-pytorch's Introduction

Overview

Implementation of DeepConvLSTM model in pytorch and python3. To train the model open up jupyter notebook under notebooks directory and follow the instructions.

This implementation is based on "Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition" paper avaiable at http://www.mdpi.com/1424-8220/16/1/115/html

The original source code has been implemented using Lasange framework which is available at https://github.com/sussexwearlab/DeepConvLSTM

Dependencies

Python 3
Pytorch

Project Organization

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks.
│   └── 1.0-dsp-DeepConvLSTM.ipynb  <- Jupyter notebook file with step by step instructions
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├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    │
    └── data           <- Scripts to download or generate data

Project based on the cookiecutter data science project template. #cookiecutterdatascience

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