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 |
References
Neural Architecture Search with Reinforcement Learning [1]