All of the files to run the experiments reside in Experiment folder. The name of the python files start with the experiment dataset, followed by the type of experiment. For instance, you can run the Health experiment, and get the average accuracy by running:
python Experiment/health_average_accuracy.py
In general, you can run the experiments using the following rule:
python Experiment/<dataset>_<experiment-type>.py
where <dataset> is either health, cifar, or imagenet (Note that, camera is only for experimental purposes with a distribtued neural network that is both vertically and horizontally split.), and <experiment-type> is either average_accuracy, hyperconnection_weight, failout_rate, or skiphyperconnection_sensitivity.
The datasets and the preprocessing methods are explained in the paper. The experiments are as follows:
average_accuracy: (Section 3.3). Obtains average accuracy, in addition to accuracy for individual physical node failures.hyperconnection_weight: (Section 3.4.1) Obtains results for different choices of hyperconnection weights.failout_rate: (Section 3.4.2) Obtains results for different rates of failout. ``skiphyperconnection_sensitivity`: (Section 3.4.3) Obtains results regarding which skip hyperconnections are more critical.
The following python packages are required to run these experiments:
- Keras
- sklearn
- networkx
- pandas
- cv2
Once you run an experiments, you will see the output in the console (e.g. accuracy). When the experiment finished running, new folders will be created. These folders keep the results and the models associated with each experiment.
/results keeps all of the result text and log files from training.
/models keeps all of the saved models after training.