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