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Sparse_Topology_Distance

This repo contains code accompaning the paper, Topological Insights into Sparse Neural Networks (Liu et al., ECMLPKDD 2020) . It includes code and topology example for measuring the sparse topology distance between two sparse neural networks. The sparse topology used in this paper refers to the topology of a sub-network (a fraction of parameters) of a dense network. And the sparse topology distance measured in this paper refers to the distance between two sub-networks.

Requirements

This code requires the following:

Linux

Python 3.*

Sscipy 1.4.1

matplotlib 3.1.3

Code Structure and Usage

The repo provide an example implementation of NNSTD, a method to measure the distance between the graphs spanned over the network weights.

The main script is sparse_topology_distance.py which contains the code to compare different sparse topologies.

The sparse networks are obtained by Sparse Evolutionary Training (SET) with their efficient code. SET proposed a novel concept, sparse neural networks with adaptive sparse connectivity to maintain sparsity during training, allowing training sparse neural networks from the beginning.

We upload the topologies used to reproduce the results of Figure 4 in the original paper in the folder topo/.

To reproduce the results of Figure 4 from the paper, run

python sparse_topology_distance.py --epoch 0 --layer 0

Results from the Paper

We initialize one sparse network with a density level of 0.6%. Then, we generate 9 networks by iteratively changing 1% of the connections from the previous generation step. By doing this, the density of these networks is the same, whereas thetopologies vary a bit. Measured by our method, we find that similar initial topologies gradually evolve to very different topologies while training with adaptive sparse connectivity shown as following:

References

If you use this code, please consider citing the following paper:

@inproceedings{liu2020topological,
title={Topological Insights into Sparse Neural Networks},
author={Liu, Shiwei and Van der Lee, Tim and Yaman, Anil and Atashgahi, Zahra and Ferraro, Davide and Sokar, Ghada and Pechenizkiy, Mykola and Mocanu, Decebal Constantin},
booktitle={ECMLPKDD},
year={2020}
}