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Efficient Architecture Search by Network Transformation, in AAAI 2018
Efficient Architecture Search by Network Transformation
Code for the paper Efficient Architecture Search by Network Transformation in AAAI 2018.
Reference
@inproceedings{cai2018efficient,
title={Efficient Architecture Search by Network Transformation},
author={Cai, Han and Chen, Tianyao and Zhang, Weinan and Yu, Yong and Wang, Jun},
booktitle={AAAI},
year={2018}
}
Related Projects
Dependencies
- Python 3.6
- Tensorflow 1.3.0
Top Nets
nets | test accuracy (%) | Dataset |
---|---|---|
C10+_Conv_Depth_20 | 95.77 | C10+ |
C10+_DenseNet_Depth_76 | 96.56 | C10+ |
C10_DenseNet_Depth_70 | 95.34 | C10 |
SVHN_Conv_Depth_20 | 98.27 | SVHN |
For checking these networks, please download the corresponding model files and run the following command under the folder of code:
$ python3 main.py --test --path=<nets path>
For example, by running
$ python3 main.py --test --path=../final_nets/C10+_Conv_Depth_20
you will get
Testing...
mean cross_entropy: 0.210500, mean accuracy: 0.957700
test performance: 0.9577
Acknowledgement
The DenseNet part of this code is based on the repository by Illarion. Many thanks to Illarion.