A Keras implementation of CapsNet in the paper:
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules. NIPS 2017

Differences with the paper:

• We use the learning rate decay with decay factor = 0.9 and step = 1 epoch,
  while the paper did not give the detailed parameters (or they didn't use it?).
• We only report the test errors after 50 epochs training.
  In the paper, I suppose they trained for 1250 epochs according to Figure A.1? Sounds crazy, maybe I misunderstood.
• We use MSE (mean squared error) as the reconstruction loss and the coefficient for the loss is lam_recon=0.0005*784=0.392.
  This should be equivalent with using SSE (sum squared error) and lam_recon=0.0005 as in the paper.

Install Keras>=2.0.7 with TensorFlow>=1.2 backend.**

pip install tensorflow-gpu
pip install keras

• PyTorch:

  • XifengGuo/CapsNet-Pytorch
  • timomernick/pytorch-capsule
  • gram-ai/capsule-networks
  • nishnik/CapsNet-PyTorch
  • leftthomas/CapsNet

• TensorFlow:

  • naturomics/CapsNet-Tensorflow
    I referred to some functions in this repository.
  • InnerPeace-Wu/CapsNet-tensorflow
  • chrislybaer/capsules-tensorflow

• MXNet:

  • AaronLeong/CapsNet_Mxnet

• Chainer:

  • soskek/dynamic_routing_between_capsules

• Matlab:

  • yechengxi/LightCapsNet