53 lines
1.7 KiB
Markdown
53 lines
1.7 KiB
Markdown
# Searching for A Robust Neural Architecture in Four GPU Hours
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We propose A Gradient-based neural architecture search approach using Differentiable Architecture Sampler (GDAS).
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## Requirements
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- PyTorch 1.0.1
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- Python 3.6
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- opencv
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```
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conda install pytorch torchvision cuda100 -c pytorch
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```
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## Usages
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Train the searched CNN on CIFAR
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-cifar.sh GDAS_FG cifar10 cut
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-cifar.sh GDAS_F1 cifar10 cut
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-cifar.sh GDAS_V1 cifar100 cut
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```
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Train the searched CNN on ImageNet
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-imagenet.sh GDAS_F1 52 14
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-imagenet.sh GDAS_V1 50 14
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```
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Evaluate a trained CNN model
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```
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CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/cifar.python --checkpoint ${checkpoint-path}
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CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/ILSVRC2012 --checkpoint ${checkpoint-path}
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```
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Train the searched RNN
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```
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-PTB.sh DARTS_V1
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-PTB.sh DARTS_V2
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-PTB.sh GDAS
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh DARTS_V1
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh DARTS_V2
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CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh GDAS
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```
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## Citation
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```
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@inproceedings{dong2019search,
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title={Searching for A Robust Neural Architecture in Four GPU Hours},
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author={Dong, Xuanyi and Yang, Yi},
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booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
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year={2019}
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}
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```
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