# Code for "Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions"
## Requirements: 
PyTorch, Python OT, GeomLoss

## How to run
The autoencoder_model.py file contains all models: WPCE, Siamese DeepSets (under PointEncoder), and ProductNet.
We include the hyperparameter configurations we used in our paper. To generate your own hyperparameters, use the parameters.py script
To train WPCE or Siamese DeepSets, use the train_autoencoder.py script.
To train ProductNet, use the train_productnet.py script
