/neural_architecture_search_with_reinforcement_learning_appendix_a

Implementation of Appendix A (Neural Architecture Search with Reinforcement Learning: https://arxiv.org/abs/1611.01578) by chainer

Primary LanguagePythonMIT LicenseMIT

What's this

Implementation of Appendix A (Neural Architecture Search with Reinforcement Learning) by chainer

Dependencies

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

How to run

python main.py -p ./ -g 0

Details about my implementation

All hyperparameters and network architecture are the same as in [1] except for some parts.

  • 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.

  • Learning rate schedule
    Learning rate is divided by 10 at [150, 170] epochs. The total number of epochs is 200.

  • batch
    128

Cifar10 result

network depth total accuracy (%)
my implementation   15   90.35 (I look for bugs)              
[1] 15 94.5

loss

total accuracy

References

Neural Architecture Search with Reinforcement Learning [1]