Added PrediNet to deepmind_research repo

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PrediNet/README.md

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+# The PrediNet Architecture and Relations Game Datasets
+
+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*](https://arxiv.org/pdf/1905.10307.pdf).
+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](https://console.cloud.google.com/storage/browser/relations-game-datasets)
+
+## Running the experiments
+
+The easiest way to run the code is using the publicly available [Colab](https://colab.research.google.com) kernel.
+You can run simply by clicking on [PrediNet Notebook](https://colab.research.google.com/github/deepmind/deepmind-research/PrediNet/blob/master/PrediNet.ipynb)
+
+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](http://jupyter.org/install.html). If you do this you will
+also have to set up a local kernel that includes the libraries above.
+
+## Citing Predicate Networks
+
+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.
+
+
+## Contact
+
+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.
+
+## Disclaimer
+
+This is not an officially supported Google product.