bentengma / nas-without-training

Neural Architecture Search Without Training

Neural Architecture Search Without Training

This repository contains code for replicating our paper, NAS Without Training.

Setup

1. Download the datasets.
2. Download NAS-Bench-201.
3. Install the requirements in a conda environment with conda env create -f environment.yml.

We also refer the reader to instructions in the official NAS-Bench-201 README.

Reproducing our results

To reproduce our results:

conda activate nas-wot
./reproduce.sh 3 # average accuracy over 3 runs
./reproduce.sh 500 # average accuracy over 500 runs (this will take longer)

Each command will finish by calling process_results.py, which will print a table. ./reproduce.sh 3 should print the following table:

Method	Search time (s)	CIFAR-10 (val)	CIFAR-10 (test)	CIFAR-100 (val)	CIFAR-100 (test)	ImageNet16-120 (val)	ImageNet16-120 (test)
Ours (N=10)	1.75	89.50 +- 0.51	92.98 +- 0.82	69.80 +- 2.46	69.86 +- 2.21	42.35 +- 1.19	42.38 +- 1.37
Ours (N=100)	17.76	87.44 +- 1.45	92.27 +- 1.53	70.26 +- 1.09	69.86 +- 0.60	43.30 +- 1.62	43.51 +- 1.40

./reproduce 500 will produce the following table:

Method	Search time (s)	CIFAR-10 (val)	CIFAR-10 (test)	CIFAR-100 (val)	CIFAR-100 (test)	ImageNet16-120 (val)	ImageNet16-120 (test)
Ours (N=10)	1.67	88.61 +- 1.58	91.58 +- 1.70	67.03 +- 3.01	67.15 +- 3.08	39.74 +- 4.17	39.76 +- 4.39
Ours (N=100)	17.12	88.43 +- 1.67	91.24 +- 1.70	67.04 +- 2.91	67.12 +- 2.98	40.68 +- 3.41	40.67 +- 3.55

To try different sample sizes, simply change the --n_samples argument in the call to search.py, and update the list of sample sizes this line of process_results.py.

Note that search times may vary from the reported result owing to hardware setup.

Plotting histograms

In order to plot the histograms in Figure 1 of the paper, run:

python plot_histograms.py

to produce:

The code is licensed under the MIT licence.

Acknowledgements

This repository makes liberal use of code from the AutoDL library. We also rely on NAS-Bench-201.

Citing us

If you use or build on our work, please consider citing us:

@misc{mellor2020neural,
    title={Neural Architecture Search without Training},
    author={Joseph Mellor and Jack Turner and Amos Storkey and Elliot J. Crowley},
    year={2020},
    eprint={2006.04647},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}