deepHAR-master下载

x## deepHAR Code repository for experiments on deep architectures for HAR in ubicomp. Using this code you will be able to replicate some of the experiments described in our IJCAI 2016 paper: ``` @article{hammerla2016deep, title={Deep, convolutional, and recurrent models for human activity recognition using wearables}, author={Hammerla, Nils Y and Halloran, Shane and Ploetz, Thomas}, journal={IJCAI 2016}, year={2016} } ``` ## Disclaimer This code is still incomplete. At the moment only the bi-directional RNN will work on the opportunity data-set. ## Installation ``` git clone https://github.com/torch/distro.git ~/torch --recursive cd ~/torch; bash install-deps; ./install.sh # after installation, we need some additional packages #HDF5 luarock sudo apt-get install libhdf5-serial-dev hdf5-tools git clone https://github.com/deepmind/torch-hdf5 cd torch-hdf5 luarocks make hdf5-0-0.rockspec LIBHDF5_LIBDIR="/usr/lib/x86_64-linux-gnu/" # json luarocks install json # RNN support luarocks install torch luarocks install nn luarocks install dpnn luarocks install torchx luarocks install rnn # we use python3 pip3 install h5py pip3 install simplejson pip3 install numpy ``` ## Usage First download and extract the Opportunity dataset. Then use the provided python script in the `data` directory to prepare the training/validation/test sets. ``` cd data python3 data_reader.py opportunity /path/to/OpportunityUCIDataset ``` This will generate two hdf5-files that are read by the lua scripts, `opportunity.h5` and `opportunity.h5.classes.json`. To train the bi-directional RNN that we have found to work best on this set run the following commands: ``` cd models/RNN th main_brnn.lua -data ../../data/opportunity.h5 -cpu \ -layerSize 179 -maxInNorm 2.283772707 \ -learningRate 0.02516758 -sequenceLength 81 \ -carryOverProb 0.915735543 -numLayers 1 \ -logdir EXP_brnn ``` This will train a model only using your CPUs, which will take a while (make sure you have some form of BLAS library installed). On my laptop this will take approx. 5 min per epoch, and it will likely not converge before epoch 60. If your environment is set up for gpu-based computation, try using `-gpu 1` instead of the `-cpu` flag for a significant speedup. ## Other models The python-based `data_reader.py` is new and substitutes for the original but unmaintainable Matlab-scripts used previously. So far it only supports `opportunity` and sample-based evaluation, which will be addressed shortly.