Awesome
NeurIPS 2019 Reproducibility Project
Paper reproduced: pdf
Getting Started
Pre-requisite
Install NVIDIA Docker
Building & Running the docker image
nvidia-docker build --rm --tag on_mixup:latest .
nvidia-docker run -p 8000:8000 -v $PATH/TO/PROJECT_DIR:/home/on_mixup --rm --name on_mixup -it on_mixup:latest
Generating The Density Plots
Firstly, start the jupyter notebook
jupyter notebook --ip=0.0.0.0 --port=8000 --allow-root --NotebookApp.token='' --NotebookApp.password=''
There are two notebooks corrsponding to scenarios of Mixup and no Mixup. Details on how to run them are provided in the notebooks itself.
Training
There are files named:
- cifar.py
- fmnist.py
- stl_10.py
To the train the neural network on the particular dataset execute the corresponding script, for example,
python cifar.py
Evaluation
evaluation.py
takes as an argument a config.yaml file. Sample configs are in the eval_config
folder. To run an evaluation on the dataset, execute the following command
python evaluation.py --config eval_config/cifar.yaml
The fields of the config are self explanatory.