Fast ResNet for CIFAR

This ResNet spends 3 hours to train on a modern CPU. It can reach 63% on CIFAR100 coarse 20 classes task.
This residual neural network is different from the original paper in there ways:

• Only have 13 layers, which the original paper didn't studied.
• No ReLU before subsampling convolutional layer, which improve accuracy by 3%
• BatchNorm is done before addition, which improve accuracy a little bit.

This ResNet-13 can't beat ResNet-18/34/150/1000 layers residual nets in the long run, however, more efficient with non-sufficient training time, interestingly.
Details are shown here. Archtecture shown at the bottom.

results on CIFAR-10/CIFAR-100

Traning 3 hours on CPU:

Acc.	CIFAR-10	CIFAR-100
Alexnet	82%	-
Mimic Learning	-	50%
2-layer NN with PCA and Kmeans	78%	56%
ResNet-13 (this repo)	84%	63%

How to run

python redo.py /path/to/CIFAR100python/

Features

• Output layers contain 20 labels
• Using tensorflow
• number of filters
• iterations
• learning rate
• batch size
• regularization strength
• number of layers
• optimization methods
  • Mimic Learning
• drop out
• initialization
  • LSUV init
  • Kaiming He's initialization
• hidden neurons
• filter size of convolution layers
• filter size of pooling layers

architecture