Install

Clone the repository

git clone git@github.com:awni/ecg.git

If you don't have virtualenv, install it with

pip install virtualenv

Make and activate a new Python 2.7 environment

virtualenv -p Python2.7 ecg_env
source ecg_env/bin/activate

Install the requirements (this may take a few minutes).

For CPU only support run

./setup.sh

To install with GPU support run

env TF=gpu ./setup.sh

Training

In the repo root direcotry (ecg) make a new directory called saved.

mkdir saved

In the same directory download and unpack the data into a folder called data.

unzip data.zip

Then run

python ecg/train.py

After each epoch the model is saved in ecg/saved/default/<experiment_id>/<model_id>.hdf5.

NB: this model is only trained on 128 examples. This is far too few to see good generalization performance, but the code should run and produce a valid model.

Testing

After training the model for a few epochs, you can make predictions and evaluate performance.

python ecg/predict.py configs/test_reviewer.json saved/default/<experiment_id>/<model_id>.hdf5

And to print some metrics run:

python ecg/evaluate.py saved/predictions/<experiment_id>

NB: These instructions evaluate the model on the training set since we do not include an independent test set in the sample data.