Updated capsnet example

example/capsnet/README.md

@@ -1,66 +1,66 @@
-**CapsNet-MXNet**
-=========================================
-
-This example is MXNet implementation of [CapsNet](https://arxiv.org/abs/1710.09829):  
-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](/~https://github.com/samsungsds-rnd/capsnet.mxnet).  
-* * *
-## **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
-```
-
-![](result.PNG)
-
-| 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 |
+**CapsNet-MXNet**
+=========================================
+
+This example is MXNet implementation of [CapsNet](https://arxiv.org/abs/1710.09829):  
+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](/~https://github.com/samsungsds-rnd/capsnet.mxnet).  
+* * *
+## **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
+```
+
+![](result.PNG)
+
+| 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 |