PrivacyScanData/truncate/new2/wide_networks_using_binary_tree-master

What's this

Implementation of Wide networks using binary tree [1] by chainer

Dependencies

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

How to run

python main.py -p ./ -g 0 -trb 4 -teb 4

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
  0.0005

• Batch size
  128

• lr
  Initial learning rate is 0.2 and is multiplied by 0.2 at [60, 120, 160] epochs. Total epochs is 200.

Cifar10 result

network	d	k	n	number of parameters	total accuracy (%)
[1]	4	6	2	1.7M	95.23
my implementation	4	6	2	1.67M	94.82

References

Truncating Wide Networks using Binary Tree Architectures[1]