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 and mxboard with pip

pip install mxboard tensorflow

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 (1.2.0)
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