forked from apache/mxnet
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Updated capsnet example (apache#12934)
* Updated capsnet * trigger CI * Update README.md
- Loading branch information
1 parent
9c611e2
commit 3e42e51
Showing
2 changed files
with
413 additions
and
414 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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 | ||
| ``` | ||
|
|
||
|  | ||
|
|
||
| | 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 | ||
| ``` | ||
|
|
||
|  | ||
|
|
||
| | 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 | |
Oops, something went wrong.