##Tenary weight network implement using tensorflow

author: Yadongwei (XJTU)

Training Deep Neural Networks with Weights and Activations Constrained to +1,0 or -1. implementation in tensorflow (https://arxiv.org/abs/1605.04711)

This is incomplete training example for BinaryNets using Binary-Backpropagation algorithm as explained in

on following datasets: Cifar10/100.

My implementation is based on work in : https://github.com/AngusG/tensorflow-xnor-bnn

Data

This implementation supports cifar10/cifar100

Dependencies

tensorflow version 1.2.1

Training

• Train cifar10 model using gpu:

Full presion:

python main_full.py  

accuracy:

83.3%(10 epoches, learning rate:0.01) 

87.0%(50 epoches, learning rate:0.005)

Binaried the weight and output

python main_for_bnn.py  

accuracy:

79.5%(10 epoches, learning rate:0.01) 

80% (50 epoches, learning rate:0.005)

Binaried the weight:

python main_for_bnn1.py 

accuracy:

82.5% (10 epoches, learning rate:0.01)

86.1% (50 epoches, learning rate:0.005)

Ternaried the weight:

python main.py          

accuracy:

83% (10 epoches, learning rate:0.01)

85.7% (50 epoches, learning rate:0.005)

• Train cifar10 model using cpu:

if you did not own a GPU which can speed up the training, you just need to change the GPU in main.py into True

Results

Cifar10 should reach at least 88% top-1 accuracy