This is a PyTorch implementation of Neural Arithmetic Logic Units from DeepMind.
The goal of this paper is to count using neural networks. Two networks are presented, NAC and NALU. The first is used for addition & substraction while the second is used for multiplication, division, square, and square root.
The paper was implemented in the python module nalu.py.
Several tests are shown in the paper, I've only implemented what they called the Static (non-recurrent) arithmetic tests (appendix B) where the network must learn several kinds of operations.
It is first train on a range of number, then it is tested on a range of number the network never saw: interpolation & extrapolation.
I've used the range [1, 100] for the first task and [101, 200] for the second.
The following image shows the results. See the jupyter notebook train.ipynb if
you want to know the full train procedure.
@misc{1808.00508,
Author = {Andrew Trask and Felix Hill and Scott Reed and Jack Rae and Chris Dyer and Phil Blunsom},
Title = {Neural Arithmetic Logic Units},
Year = {2018},
Eprint = {arXiv:1808.00508},
}