This repository contains a notebook implementation of the PrediNet architecture, as well as the baselines and sample datasets.
The code was written by Murray Shanahan, Marta Garnelo and Kyriacos Nikiforou.
The PrediNet.ipynb notebook includes an overview of the PrediNet
architecture and the code to reproduce the multi-task experiment presented in the paper
An Explicitly Relational Neural Network Architecture.
Additional details of the model and experiments can be found in the paper.
Six .npz files contain downsampled versions of the datasets required to train
and evaluate the various models on the multi-task experiment. The training set (*_pentos.npz),
containing pentominoes, is a NPZ NumPy archive with the following fields:
images: (50000 x 36 x 36, 3) Images in RGB.labels: (50000 x 2) Labels for the images.tasks: (50000 x 1) Task ids that denote which relation must hold between the objects in the images.
Two additional .npz files are provided for each task, one with hexominoes (*_hexos.npz)
and one with striped squares (*_stripes.npz). These can be used for testing.
The full Relations Game datasets composed of 250,000 samples for pentominoes - and 50,000 samples for hexominoes and stripes - can be found here
The easiest way to run the code is using the publicly available Colab kernel. You can run simply by clicking on PrediNet Notebook
Colaboratory is a free Jupyter notebook environment provided by Google that requires no setup and runs entirely in the cloud. You will need the following dependencies to run the code, with instructions on how to install them. The code was tested with the versions shown in brackets
- TensorFlow 2 (2.0.0-rc0 with gpu support) - not installed You can install TensorFlow 2.0 beta version by running the following command in Colab:
!pip install "tensorflow-gpu>=2.0.0rc0" --pre
- Sonnet 2 (2.0.0b0) - not installed You can install Sonnet 2 by running the following comment in Colab.
!pip install "dm-sonnet>=2.0.0b0" --pre
Alternatively, you can open the .ipynb files using
Jupyter notebook. If you do this you will
also have to set up a local kernel that includes the libraries above.
If you use this code in your work, please cite us as follows:
Shanahan, M., Nikiforou, K., Creswell, A., Kaplanis, C., Barrett, D., & Garnelo, M. (2019). An Explicitly Relational Neural Network Architecture. arXiv preprint arXiv:1905.10307.
If you have any feedback, or would like to get in touch with us, you can reach out to us at mshanahan@google.com and knikiforou@google.com.
This is not an officially supported Google product.