Artificial Hippocampal Algorithm in Pytorch

ATTN: This is a work in progress.

Pytorch implementation of AHA! an ‘Artificial Hippocampal Algorithm’ for Episodic Machine Learning by Kowadlo, Rawlinson and Ahmed (2019).

Getting Started

Use Pipenv to install dependencies and create compatible virtual environment. (https://thoughtbot.com/blog/how-to-manage-your-python-projects-with-pipenv)

• Requires Python version >= 3.6

Running

Weights should already be trained for the VC module, so to run predictions, do the following:

1. run /eval.py
2. Save training set image.
3. run /test.py
4. Save test set image, save predictions image.

In /experiments/train/params.json:

• modify test_seed or test_shift values to change the test set variations.

Built With

• Pytorch - Artificial neural network framework
• Pipenv - Dependency Management

Authors

• Jacob Krajewski - Pytorch implementation

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• Thanks to Gideon Kowadlo and David Rawlinson for guiding me through some of the more difficult elements of the ANN.
• Thanks to @ptrblk on the Pytorch forums for walking me through some of the more confusing aspects of Pytorch.