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