33 lines
1.4 KiB
Markdown
33 lines
1.4 KiB
Markdown
# DARTS: Differentiable Architecture Search
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DARTS: Differentiable Architecture Search is accepted by ICLR 2019.
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In this paper, Hanxiao proposed a differentiable neural architecture search method, named as DARTS.
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Recently, DARTS becomes very popular due to its simplicity and performance.
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## Run DARTS on the NAS-Bench-201 search space
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/DARTS-V2.sh cifar10 1 -1
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/algos/GDAS.sh cifar10 1 -1
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```
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## Run the first-order DARTS on the NASNet/DARTS search space
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This command will start to use the first-order DARTS to search architectures on the DARTS search space.
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/DARTS1V-search-NASNet-space.sh cifar10 -1
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```
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After searching, if you want to train the searched architecture found by the above scripts, you need to add the config of that architecture (will be printed in log) in [genotypes.py](https://github.com/D-X-Y/AutoDL-Projects/blob/main/lib/nas_infer_model/DXYs/genotypes.py).
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In future, I will add a more eligent way to train the searched architecture from the DARTS search space.
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# Citation
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```
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@inproceedings{liu2019darts,
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title = {{DARTS}: Differentiable architecture search},
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author = {Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
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booktitle = {International Conference on Learning Representations (ICLR)},
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year = {2019}
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}
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```
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