Code for the paper "Modeling Electrical Motor Dynamics using Encoder-Decoder with Recurrent Skip Connection" (Accepted in AAAI 2020)
This is the github repository containing the code for the paper "Modeling Electrical Motor Dynamics using Encoder-Decoder with Recurrent Skip Connection" by Sagar Verma, Nicolas Henwood, Marc Castella, Francois Malrait, and Jean-Christophe Pesquet.
**Note: All the tables in the paper are for models trained for predicting individual quantities from three input quantities. It is possible to use the model for any input-ouput combinations by passing appropriate arguments. Also MAE is first computed on normalized output and then aggregated and normalized. Ignore sign in the reported MAE, RMSLE, and RMSE.
The code has been tested on:
- 2xNvidia V100 GPU
- Ubuntu 18.04 LTS on 48 vCPUs and 186 GB of RAM
- Python 3.6.10
- Pytorch v1.4.0
Installation
git clone https://github.com/INRIA-OPIS/MotorNN.git
git checkout AAAI2020_release
pip install -r requirements.txt
pip install -e .
Download and extract dataset. Create weights and logs path.
To train a model use following
cd MotorNN
python motor_dynamics/summoner.py --gpu=0 --task=train --train_sim_dir={DATA_PATH}/train_sim/ --val_sim_dir={DATA_PATH}/val_sim/ --weights_dir={WEIGHTS_PATH} --logs_dir={LOGS_PATH} --model=deep_cnn --epochs=100 --batch_size=512 --lr=0.1 --inp_quants='voltage_d,voltage_q,speed' --out_quants='current_d' --stride=1 --window=100 --act=relu --loss=mse
For any queries, please contact
Sagar Verma: sagar15056@iiitd.ac.in