https://github.com/dragen1860/capsnet-pytorch
Pytorch version of Hinton's Capsule Theory paper: Dynamic Routing Between Capsules
https://github.com/dragen1860/capsnet-pytorch
capsnet-pytorch deep-learning machine-learning pytorch
Last synced: 5 months ago
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Pytorch version of Hinton's Capsule Theory paper: Dynamic Routing Between Capsules
- Host: GitHub
- URL: https://github.com/dragen1860/capsnet-pytorch
- Owner: dragen1860
- Created: 2017-11-02T09:48:15.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-01-19T02:16:47.000Z (over 7 years ago)
- Last Synced: 2025-03-30T16:12:06.587Z (6 months ago)
- Topics: capsnet-pytorch, deep-learning, machine-learning, pytorch
- Language: Python
- Homepage:
- Size: 65.8 MB
- Stars: 45
- Watchers: 4
- Forks: 19
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[](https://github.com/dragen1860/CapsNet-Pytorch.git)
# CapsNet-Pytorch
Pytorch version of Hinton's paper: [Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829)
> Some implementations of CapsNet online have potential problems and it's uneasy to realize the bugs since MNIST is too simple to achieve satisfying accuracy.
# Network
> Corresponding pipeline: Input > Conv1 > Caps(cnn inside) > Route > Loss
# Screenshots
- running
- training loss
# Highlights
- Highly abstraction of `Caps` layer, by re-writing the function `create_cell_fn` you can implement your own sub-network inside `Caps` Layer
```python
def create_cell_fn(self):
"""
create sub-network inside a capsule.
:return:
"""
conv1 = nn.Conv2d(self.conv1_kernel_num, self.caps1_conv_kernel_num, kernel_size = self.caps1_conv_kernel_size, stride = self.caps1_conv1_stride)
#relu = nn.ReLU(inplace = True)
#net = nn.Sequential(conv1, relu)
return conv1
```
- Highly abstraction of routing layer by class `Route`, you can take use of `Caps` Layer and `Route` Layer to construct any type of network
- No DigitsCaps Layer, and we just just the output of `Route` layer.
## Status
- Currently we train our model for 30 epochs, which means it is potentially promising if more epochs are used to train
- We don not use reconstruction loss now, and will add it later
- The critical part of code is well commented with each dimension changes, which means you can follow the comments to understand the routing mechnism
## TODO
- add reconstruction loss
- test on more convincing dataset, such as ImagetNet
## About me
> I'm a Research Assistant @ National University of Singapre, before joinging NUS, I was a first-year PhD candidate in Zhejiang University and then quitted.
Contact me with email: dcslong@nus.edu.sg or wechat: dragen1860
# Usage
## Step 1. Install Conda, CUDA, cudnn and Pytorch
>conda install pytorch torchvision cuda80 -c soumith
## Step 2. Clone the repository to local
>git clone https://github.com/dragen1860/CapsNet-Pytorch.git
>cd CapsNet-Pytorch
## Step 3. Train CapsNet on MNIST
1. please modify the variable `glo_batch_size = 125` to appropriate size according to your GPU memory size.
2. run
>$ python main.py
3. turn on tensorboard
>$ tensorboard --logdir runs
## Step 4. Validate CapsNet on MNIST
OR you can comment the part of train code and test its performance with pretrained model `mdl` file.
# Results
| Model | Routing | Reconstruction | MNIST | |
|----------|---------|----------------|-------|---|
| Baseline | - | - | 0.39 | |
| Paper | 3 | no | 0.35 | |
| Ours | 3 | no | **0.34**| |
It takes about 150s per epoch for single GTX 970 4GB Card.
## Other Implementations
- Keras:
- [XifengGuo/Capsnet-Keras](https://github.com/XifengGuo/CapsNet-Keras) Well written.
- TensorFlow:
- [naturomics/CapsNet-Tensorflow](https://github.com/naturomics/CapsNet-Tensorflow.git) The first implementation online.
- [InnerPeace-Wu/CapsNet-tensorflow](https://github.com/InnerPeace-Wu/CapsNet-tensorflow)
- [LaoDar/tf_CapsNet_simple](https://github.com/LaoDar/tf_CapsNet_simple)
- MXNet:
- [AaronLeong/CapsNet_Mxnet](https://github.com/AaronLeong/CapsNet_Mxnet)
- Lasagne (Theano):
- [DeniskaMazur/CapsNet-Lasagne](https://github.com/DeniskaMazur/CapsNet-Lasagne)
- Chainer:
- [soskek/dynamic_routing_between_capsules](https://github.com/soskek/dynamic_routing_between_capsules)