DeepStochLog is a neuro-symbolic framework that combines grammars, logic, probabilities and neural networks. By writing a DeepStochLog program, one can train a neural network with the given background knowledge. One can express symbolic information about subsymbolic data in DeepStochLog and help train neural networks more efficiently this way. For example, if the training data is made up of handwritten digit images, and we know the sum of these digits but not the individual numbers, one can express this relation in DeepStochLog and train the neural networks much faster.
DeepStochLog uses a stochastic logic approach to encoding the probabilistic logic, and is thus faster than and can deal with longer inputs than its sibling DeepProbLog in our experiments.
DeepStochLog requires SWI Prolog to run. Run the following commands to install:
sudo apt-add-repository ppa:swi-prolog/stable
sudo apt-get update
sudo apt-get install swi-prolog
To install DeepStochLog itself, run the following command:
pip install deepstochlog
To see DeepStochLog in action, please first install SWI Prolog (as explained about),
as well as the requirements listed in requirements.txt
pip install -r requirements.txt
The datasets used in the tasks used to evaluate DeepStochLog can be found in our initial release.
To see DeepStochLog in action, navigate to examples/addition and run addition.py.
The neural definite clause grammar specification is provided in addition.pl.
The addition(N) predicate specifies/recognises that two handwritten digits N1 and N2 sum to N.
The neural probability nn(number, [X], Y, digit) makes the neural network with name number (a MNIST classifier) label input image X with the digit Y.
If use this work in an academic context, please consider citing the following paper:
The paper is also accepted to AAAI22. Please cite that version of the paper when the proceedings are out.
@article{winters2021deepstochlog,
title={Deepstochlog: Neural stochastic logic programming},
author={Winters, Thomas and Marra, Giuseppe and Manhaeve, Robin and De Raedt, Luc},
journal={arXiv preprint arXiv:2106.12574},
year={2021}
}