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Implementation of GoogLeNet by chainer (Going Deeper with Convolutions: https://arxiv.org/abs/1409.4842)
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trainer @ 4a84c45 submodule Dec 4, 2016
.gitignore updated Dec 9, 2016
.gitmodules submodule Dec 4, 2016
LICENSE
README.md
accuracy.jpg result Dec 9, 2016
googlenet.py fixed model definition Dec 4, 2016
loss.jpg result Dec 9, 2016
main.py fixed bug Dec 4, 2016
nutszebra_optimizer.py optimizer Dec 4, 2016

README.md

What's this

Implementation of GoogLeNet by chainer

Dependencies

git clone https://github.com/nutszebra/googlenet.git
cd googlenet
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 [256, 512], then 224x224 patches are extracted randomly and are normalized locally. Horizontal flipping is applied with 0.5 probability.
    Test: Pictures are resized to 384x384, then they are normalized locally. Single image test is used to calculate total accuracy.

  • Auxiliary classifiers
    No implementation

  • Learning rate
    As [[1]][Paper] said, learning rate are multiplied by 0.96 at every 8 epochs. The description about initial learning rate can't be found in [[1]][Paper], so initial learning is setted as 0.0015 that is found in [[2]][Paper2].

  • Weight decay
    The description about weight decay can't be found in [[1]][Paper], so by using [[2]][Paper2] and [[3]][Paper3] I guessed that weight decay is 2.0*10^-4.

Cifar10 result

network depth total accuracy (%)
my implementation 22 91.33

loss

total accuracy

References

Going Deeper with Convolutions [[1]][Paper]
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift [[2]][Paper2]
Rethinking the Inception Architecture for Computer Vision [[3]][Paper3]
[paper]: https://arxiv.org/abs/1409.4842 "Paper" [paper2]: https://arxiv.org/abs/1502.03167 "Paper2" [paper3]: https://arxiv.org/abs/1512.00567 "Paper3"

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