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CapsNet-MXNet

This example is MXNet implementation of CapsNet:
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules. NIPS 2017

  • The current best test error is 0.29% and average test error is 0.303%
  • The average test error on paper is 0.25%

Log files for the error rate are uploaded in repository.


Usage

Install scipy with pip

pip install scipy

Install tensorboard with pip

pip install tensorboard

On Single gpu

python capsulenet.py --devices gpu0

On Multi gpus

python capsulenet.py --devices gpu0,gpu1

Full arguments

python capsulenet.py --batch_size 100 --devices gpu0,gpu1 --num_epoch 100 --lr 0.001 --num_routing 3 --model_prefix capsnet

Prerequisities

MXNet version above (0.11.0)
scipy version above (0.19.0)


Results

Train time takes about 36 seconds for each epoch (batch_size=100, 2 gtx 1080 gpus)

CapsNet classification test error on MNIST

python capsulenet.py --devices gpu0,gpu1 --lr 0.0005 --decay 0.99 --model_prefix lr_0_0005_decay_0_99 --batch_size 100 --num_routing 3 --num_epoch 200

Trial Epoch train err(%) test err(%) train loss test loss
1 120 0.06 0.31 0.0056 0.0064
2 167 0.03 0.29 0.0048 0.0058
3 182 0.04 0.31 0.0046 0.0058
average - 0.043 0.303 0.005 0.006

We achieved the best test error rate=0.29% and average test error=0.303%. It is the best accuracy and fastest training time result among other implementations(Keras, Tensorflow at 2017-11-23). The result on paper is 0.25% (average test error rate).

Implementation test err(%) ※train time/epoch GPU Used
MXNet 0.29 36 sec 2 GTX 1080
tensorflow 0.49 ※ 10 min Unknown(4GB Memory)
Keras 0.30 55 sec 2 GTX 1080 Ti