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Tests
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.
References
@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},
}
