Neural Arithmetic Logic Units

[WIP]

Overview

This is the code in this video on Youtube by Siraj Raval on Neural Arithmetic Logic Units. Credits for this code go to kevinzakka.

This is a PyTorch implementation of Neural Arithmetic Logic Units by Andrew Trask, Felix Hill, Scott Reed, Jack Rae, Chris Dyer and Phil Blunsom.

API

from models import *

# single layer modules
NeuralAccumulatorCell(in_dim, out_dim)
NeuralArithmeticLogicUnitCell(in_dim, out_dim)

# stacked layers
NAC(num_layers, in_dim, hidden_dim, out_dim)
NALU(num_layers, in_dim, hidden_dim, out_dim)

Experiments

To reproduce "Numerical Extrapolation Failures in Neural Networks" (Section 1.1), run:

python failures.py

This should generate the following plot:

To reproduce "Simple Function Learning Tasks" (Section 4.1), run:

python function_learning.py

This should generate a text file called interpolation.txt with the following results. (Currently only supports interpolation, I'm working on the rest)

	Relu6	None	NAC	NALU
a + b	4.472	0.132	0.154	0.157
a - b	85.727	2.224	2.403	34.610
a * b	89.257	4.573	5.382	1.236
a / b	97.070	60.594	5.730	3.042
a ^ 2	89.987	2.977	4.718	1.117
sqrt(a)	5.939	40.243	7.263	1.119