Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. implementation in tensorflow (https://papers.nips.cc/paper/6573-binarized-neural-networks)

This is incomplete training example for BinaryNets using Binary-Backpropagation algorithm as explained in "Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, on following datasets: Cifar10/100.

Note that in this folder I didn’t implemented (yet...) shift-base BN , shift-base AdaMax (instead I just use the vanilla BN and Adam). Likewise, I use deterministic binarization and I don't apply the initialization coefficients from GLorot&Bengio 2010. Finally "sparse_softmax_cross_entropy_with_logits" loss is used instead if the SquareHingeLoss.

The implementation is based on https://github.com/eladhoffer/convNet.tf but the main idea can be easily transferred to any tensorflow wrapper. I'll probably change it to keras soon. (e.g., slim,keras)

This implementation supports cifar10/cifar100

tensorflow version 1.2.1

• Train cifar10 model using gpu: python main.py --model BNN_cifar10 --save BNN_cifar10 --dataset cifar10 --gpu True
• Train cifar10 model using cpu: python main.py --model BNN_cifar10 --save BNN_cifar10 --dataset cifar10

Cifar10 should reach at least 88% top-1 accuracy