update scripts-cluster

README.md

@@ -1,17 +1,16 @@
-# GDAS
-By Xuanyi Dong and Yi Yang
+# Searching for A Robust Neural Architecture in Four GPU Hours
 
-University of Technology Sydney
+We propose A Gradient-based neural architecture search approach using Differentiable Architecture Sampler (GDAS).
 
-Requirements
-- PyTorch 1.0
+## Requirements
+- PyTorch 1.0.1
 - Python 3.6
 - opencv
 ```
 conda install pytorch torchvision cuda100 -c pytorch
 ```
 
-## Algorithm
+## Usages
 
 Train the searched CNN on CIFAR
 ```
@@ -26,6 +25,11 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-imagenet.sh GDAS_F1 52 14
 CUDA_VISIBLE_DEVICES=0 bash ./scripts-cnn/train-imagenet.sh GDAS_V1 50 14
 ```
 
+Evaluate a trained CNN model
+```
+CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path  $TORCH_HOME/cifar.python --checkpoint ${checkpoint-path}
+CUDA_VISIBLE_DEVICES=0 python ./exps-cnn/evaluate.py --data_path  $TORCH_HOME/ILSVRC2012 --checkpoint ${checkpoint-path}
+```
 
 Train the searched RNN
 ```
@@ -36,3 +40,13 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh DARTS_V1
 CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh DARTS_V2
 CUDA_VISIBLE_DEVICES=0 bash ./scripts-rnn/train-WT2.sh GDAS
 ```
+
+## Citation
+```
+@inproceedings{dong2019search,
+  title={Searching for A Robust Neural Architecture in Four GPU Hours},
+  author={Dong, Xuanyi and Yang, Yi},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2019}
+}
+```