demianw/tutorials

Jupyter notebooks to get started with Logic Tensor Networks (LTN)

Logic Tensor Networks (LTN) Tutorials

Dependencies

The following is what we are using for development. Basically similar versions should run fine.

• python3.6
• tensorflow >=1.8
• numpy >= 1.13.3
• matplotlib >= 2.1

Installing dependencies is easy. Just use pip install tensorflow numpy matplotlib or use a virtualenv. If you want to run the notebooks you need a Jupyter installation.

Repository structure

• logictensornetworks.py -- core system for defining constants, variables, predicates, functions and formulas.
• logictensornetworks_wrapper.py -- a simple wrapper that allows to express constants, variables, predicates, functions and formulas using strings.
• logictensornetworks_library.py -- a collection of useful functions.
• *.ipynb are jupyter notebooks

Tutorials

• grounding
• grounding and learning
• regression linear
• binary classification
• multilabel classification
• multilabel classification axiomatized
• relations
• smokes/friends/cancer

Documentation

Checkout recent tutorials on Logic Tensor Networks (LTN)

• IJCNN 2018 tutorial
• IJCAI 2018 tutorial

License

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

Acknowledgments

LTN has been developed thanks to active contributions and discussions with the following people:

• Alessandro Daniele (FBK)
• Artur d’Avila Garces (City)
• Francesco Giannini (UniSiena)
• Giuseppe Marra (UniSiena)
• Ivan Donadello (FBK)
• Lucas Brukberger (UniOsnabruck)
• Luciano Serafini (FBK)
• Marco Gori (UniSiena)
• Michael Spranger (Sony CSL)
• Michelangelo Diligenti (UniSiena)