Demonstrate and learn cellular automata using convolutional neural networks in TensorFlow
This code is associated with the ArXiv preprint: Gilpin, William. "Cellular automata as convolutional neural networks" 2018. https://arxiv.org/abs/1809.02942
For now, code is only in archival form for testing and analysis; future versions of this repository will significantly re-factor code into a general-purpose tool for cellular automaton analysis. All versions until a 1.0/PyPI release are thus tentative.
The package contains the following libraries
train_ca : requires TensorFlow
ca_funcs : requires TensorFlow
utils : minor functions that support the main methods. Requires numpy only.
- Python >3.4
- TensorFlow
- numpy
- matplotlib
- Jupyter notebooks (for demos)
- Add methods for simulating totalistic CA
- Add methods for Moore neighborhood CA
- Add demos recreating classic experiments, such as the results in Langton. Physica D, 1990.
- Add statistical physics calculations such as an efficient calculation of "activity" for a CA
- CA on graphs using an adjacency matrix --> grid convolutional operator