shake_shake

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Implementation of Shake-Shake by chainer (Shake-Shake regularization of 3-branch residual networks: https://openreview.net/forum?id=HkO-PCmYl)

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// repository documentation

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# What's this
Implementation of Shake-Shake [[1]][Paper] by chainer

# Dependencies

git clone https://github.com/nutszebra/shake_shake.git
    cd shake_shake
    git submodule init
    git submodule update

# How to run
    python main.py -p ./ -g 0

# Details about my implementation

* Data augmentation  
Train: Pictures are randomly resized in the range of [32, 36], then 32x32 patches are extracted randomly and are normalized locally. Horizontal flipping is applied with 0.5 probability.  
Test: Pictures are resized to 32x32, then they are normalized locally. Single image test is used to calculate total accuracy.

* Optimization  
Momentum SGD with 0.9 momentum

* Weight decay    
0.0001

* Batch size  
128

* Cosine annealing  
eta_max is 0.2 and eta_min is 0.002. The number of total epoch is 1800.

* Shake-Shake  
forward: Shake  
backward: Shake  
level: Image

# Cifar10 result

| network              | model(Shake-Shake-Image)  | total accuracy (%) |
|:---------------------|---------------------------|-------------------:|
| [[1]][Paper]         | 2x32d                     | 96.45              |
| [[1]][Paper]         | 2x64d                     | 97.02              |
| [[1]][Paper]         | 2x96d                     | 97.14              |
| my implementation    | 2x64d                     | 96.69              |

# References
Shake-Shake regularization of 3-branch residual networks [[1]][Paper]

[paper]: https://openreview.net/forum?id=HkO-PCmYl "Paper"

shake_shake

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