This is the Code for the Paper Neural Architecture Optimization.

Authors: Renqian Luo*, Fei Tian*, Tao Qin, En-Hong Chen, Tie-Yan Liu. *=equal contribution

The codes and models in this repo are released under the GNU GPLv3 license.

If you find this work helpful in your research, please use the following BibTex entry to cite our paper.

@inproceedings{NAO,
  title={Neural Architecture Optimization},
  author={Renqian Luo and Fei Tian and Tao Qin and En-Hong Chen and Tie-Yan Liu},
  booktitle={Advances in neural information processing systems},
  year={2018}
}

This is not an official Microsoft product.

Tensorflow >= 1.4.0

Pytorch == 0.3.1

To search the CNN architectures for CIFAR-10 with weight sharing, please refer to:

cd NAO-WS/cnn
bash train_search.sh

Once the search is done, the final pool of architectures will be in models/child/arch_pool. You can choose top-5 architectures to run them using train_final.sh and pass in the arch by setting the fixed_arc argument.

To obtain the best architecture, we perform grid search on the hyper-parameters for the top-5 architectures discovered.

To train a fixed CNN architecture, for example, our best architecture discovered, please refer to:

and run:

cd NAO-WS/cnn
bash train_final.sh

If you want to run it with cutout, add --child_cutout_size=16 in the script.

To directly evaluate an architecture, for example, our best architecture discovered, please download the checkpoint above, move all the files to NAO-WS/cnn/models folder and run:

cd NAO-WS/cnn
bash test_final.sh    #This should give you an accuracy of 96.50% (error rate of 3.50%) without cutout

Please refer to details in ./NAO/README.md

Please download data at Google Drive Baidu Pan

You can train the best architecture discovered (show in Fig. 1 in the Appendix of the paper) using:

by running:

cd NAO/cnn
bash train_cifar10_final.sh
bash train_cifar100_final.sh

To directly evaluate an architecture, for example, our best architecture discovered, please download the checkpoint above, move all the files to NAO/cnn/models/cifar10 or NAO/cnn/models/cifar100/ , and run:

cd NAO/cnn
bash test_cifar10.sh     #This should give you an accuracy of 97.94% (error rate of 2.06%)
bash test_cifar100.sh    #This should give you an accuracy of 85.20% (error rate of 14.81%)

To search the RNN architectures for PTB with weight sharing, please refer to:

cd NAO-WS/rnn
bash train_search.sh

Once the search is done, the final pool of architectures will be in models/child/arch_pool. You can choose top-10 architectures to run them using train_final.sh and pass in the arch by setting the arch argument.

To train a fixed RNN architecture, for example, our best architecture discovered, please refer to:

cd NAO-WS/rnn
bash train_final.sh   #This should give you a test ppl of 56.66 at the end of training

To directly evaluate an architecture, for example, our best architecture discovered, please download the checkpoint above, move all the files to ./NAO/rnn/models folder and run:

cd NAO-WS/cnn
bash test_final.sh    #This should give you a test ppl of 56.66

Please refer to details in NAO/README.md

You can train the best architecture discovered (showin in Fig. 2 in the Appendix of the paper) using:

To directly evaluate an architecture, for example, our best architecture discovered, please download the checkpoint above, move all the files to NAO/rnn/models/ptb or NAO/rnn/models/wt2 , and run:

cd NAO/rnn
bash test_ptb.sh    #This should give you a test ppl of 56.02
bash test_wt2.sh    #This should give you a test ppl of 67.10

We thank Hieu Pham for the discussion on some details of ENAS implementation, and Hanxiao Liu for the code base of language modeling task in DARTS . We furthermore thank the anonymous reviewers for their constructive comments.