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@ -32,6 +32,7 @@ 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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CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path $TORCH_HOME/ILSVRC2012 --checkpoint GDAS-V1-C50-N14-ImageNet.pth
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```
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Train the searched RNN
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@ -48,10 +49,11 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh GDAS
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Some training logs can be found in `./data/logs/`, and some pre-trained models can be found in [Google Driver](https://drive.google.com/open?id=1Ofhc49xC1PLIX4O708gJZ1ugzz4td_RJ).
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### Experimental Results
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<img src="data/imagenet-results.png" width="600">
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<img src="data/imagenet-results.png" width="700">
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Figure 2. Top-1 and top-5 errors on ImageNet.
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### Citation
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If you find that this project (GDAS) helps your research, please cite the paper:
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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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