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Differentiable architecture search for convolutional and recurrent networks


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Differentiable Architecture Search

Code accompanying the paper

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


The algorithm is based on continuous relaxation and gradient descent in the architecture space. It is able to efficiently design high-performance convolutional architectures for image classification (on CIFAR-10 and ImageNet) and recurrent architectures for language modeling (on Penn Treebank and WikiText-2). Only a single GPU is required.


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.


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.

Pretrained models

The easist way to get started is to evaluate our pretrained DARTS models.

CIFAR-10 (

cd cnn && python --auxiliary --model_path
  • Expected result: 2.63% test error rate with 3.3M model params.


cd rnn && python --model_path
  • Expected result: 55.68 test perplexity with 23M model params.

ImageNet (

cd cnn && python --auxiliary --model_path
  • Expected result: 26.7% top-1 error and 8.7% top-5 error with 4.7M model params.

Architecture search (using small proxy models)

To carry out architecture search using 2nd-order approximation, run

cd cnn && python --unrolled     # for conv cells on CIFAR-10
cd rnn && python --unrolled     # for recurrent cells on PTB

Note the validation performance in this step does not indicate the final performance of the architecture. One must train the obtained genotype/architecture from scratch using full-sized models, as described in the next section.

Also be aware that different runs would end up with different local minimum. To get the best result, it is crucial to repeat the search process with different seeds and select the best cell(s) based on validation performance (obtained by training the derived cell from scratch for a small number of epochs). Please refer to fig. 3 and sect. 3.2 in our arXiv paper.

progress_convolutional_normal progress_convolutional_reduce progress_recurrent

Figure: Snapshots of the most likely normal conv, reduction conv, and recurrent cells over time.

Architecture evaluation (using full-sized models)

To evaluate our best cells by training from scratch, run

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

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

The CIFAR-10 result at the end of training is subject to variance due to the non-determinism of cuDNN back-prop kernels. It would be misleading to report the result of only a single run. By training our best cell from scratch, one should expect the average test error of 10 independent runs to fall in the range of 2.76 +/- 0.09% with high probability.

cifar10 ptb ptb

Figure: Expected learning curves on CIFAR-10 (4 runs), ImageNet and PTB.


Package graphviz is required to visualize the learned cells

python DARTS

where DARTS can be replaced by any customized architectures in


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

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