Differentiable Architecture Search

Code accompanying the paper

DARTS: Differentiable Architecture Search
Hanxiao Liu, Karen Simonyan, Yiming Yang.
arXiv:1806.09055.

Requirements

Python >= 3.5.5, PyTorch == 0.3.1, torchvision == 0.2.0

NOTE: PyTorch 0.4 is not supported at this moment and would lead to OOM.

Datasets

Instructions for acquiring PTB and WT2 can be found here. While CIFAR-10 can be automatically downloaded by torchvision, ImageNet needs to be manually downloaded (preferably to a SSD) following the instructions here.

Architecture Search

To carry out architecture search using 1st-order approximation, run

cd cnn && python train_search.py     # for conv cells on CIFAR-10
cd rnn && python train_search.py     # for recurrent cells on PTB

2nd-order approximation can be enabled by adding the --unrolled flag.

Snapshots of the most likely convolutional & recurrent cells over time:

Architecture Evaluation

To evaluate our best cells, run

cd cnn && python train.py --auxiliary --cutout            # CIFAR-10
cd rnn && python train.py                                 # PTB
cd rnn && python train.py --data ../data/wikitext-2 \     # WT2
            --dropouth 0.15 --emsize 700 --nhidlast 700 --nhid 700 --wdecay 5e-7
cd cnn && python train_imagenet.py --auxiliary            # ImageNet

Customized architectures are supported through the --arch flag once specified in genotypes.py.

Expected performance on CIFAR-10 (4 runs) and PTB:

Visualization

Package graphviz is required to visualize the learned cells

python visualize.py DARTS

where DARTS can be replaced by any customized architectures in genotypes.py.

Citation

If you use any part of this code in your research, please cite our paper:

@article{liu2018darts,
  title={DARTS: Differentiable Architecture Search},
  author={Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
  journal={arXiv preprint arXiv:1806.09055},
  year={2018}
}