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%andaverage 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 |