https://github.com/cedrickchee/capsnet-tensorflow

A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules

https://github.com/cedrickchee/capsnet-tensorflow

Keywords

Repository

A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules

https://github.com/cedrickchee/CapsNet-Tensorflow/blob/master/

# CapsNet-Tensorflow

[![Contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=plastic)](CONTRIBUTING.md)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg?style=plastic)](https://opensource.org/licenses/Apache-2.0)
![completion](https://img.shields.io/badge/completion%20state-90%25-blue.svg?style=plastic)
[![Gitter](https://img.shields.io/gitter/room/nwjs/nw.js.svg?style=plastic)](https://gitter.im/CapsNet-Tensorflow/Lobby)

A Tensorflow implementation of CapsNet in Hinton's paper [Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829)

> **Status:**
> 1. The code runs, issue #8 fixed.
> 2. some results of the tag v0.1 version has been pasted out, but not effective as the results in the paper

> **Daily task**
> 1. Adjust margin 
> 2. Improve the eval pipeline, integrate it into training pipeline: all you need is ``git clone``, ``cd`` and ``python main.py``

> **Others**
> 1. [Here()](https://zhihu.com/question/67287444/answer/251460831) is my understanding of section 4 of the paper (the core part of CapsNet), it might be helpful for understanding the code.
> 2. If you find out any problems, please let me know. I will try my best to 'kill' it as quickly as possible.

In the day of waiting, be patient: Merry days will come, believe. ---- Alexander PuskinIf :blush:


## Requirements
- Python
- NumPy
- [Tensorflow](https://github.com/tensorflow/tensorflow) (I'm using 1.3.0, others should work, too)
- tqdm (for showing training progress info)
- scipy (for saving image)

## Usage

### Training
**Step 1.** 
Clone this repository with ``git``.

```
$ git clone https://github.com/naturomics/CapsNet-Tensorflow.git
$ cd CapsNet-Tensorflow
```

**Step 2.** 
Download [MNIST dataset](http://yann.lecun.com/exdb/mnist/), ``mv`` and extract them into ``data/mnist`` directory.(Be careful the backslash appeared around the curly braces when you copy the ``wget `` command to your terminal, remove it)

```
$ mkdir -p data/mnist
$ wget -c -P data/mnist http://yann.lecun.com/exdb/mnist/{train-images-idx3-ubyte.gz,train-labels-idx1-ubyte.gz,t10k-images-idx3-ubyte.gz,t10k-labels-idx1-ubyte.gz}
$ gunzip data/mnist/*.gz
```

**Step 3.** 
Start training with command line:
```
$ pip install tqdm  # install it if you haven't installed yet
$ python train.py
```

the tqdm package is not necessary, just a tool for showing the training progress. If you don't want it, change the loop ``for in step ...`` to ``for step in range(num_batch)`` in ``train.py``

### Evaluation
```
$ python eval.py --is_training False
```


## Results
Results for the 'wrong' version(Issues #8):

- training loss
![total_loss](imgs/total_loss.png)

![margin_loss](imgs/margin_loss.png)
![reconstruction_loss](imgs/reconstruction_loss.png)

- test acc

|Epoch|49|51|
|:----:|:----:|:--:|
|test acc|94.69|94.71|

![test_img1](results/test_000.png)
![test_img2](results/test_015.png)
![test_img3](results/test_030.png)
![test_img4](results/test_045.png)
![test_img5](results/test_075.png)

------------
Results after fix Issues #8: 


> My simple comments for capsule
> 1. A new version neural unit(vector in vector out, not scalar in scalar out)
> 2. The routing algorithm is similar to attention mechanism
> 3. Anyway, a great potential work, we can do a lot of work on it

------------
### TODO:
- Finish the MNIST version of capsNet (progress:90%)
- Do some different experiments for capsNet:
 * Using other datasets
 * Adjusting model structure
 
- There is [another new paper](https://openreview.net/pdf?id=HJWLfGWRb) about capsules(submitted to ICLR 2018), follow-up.

## My weChat:
 ![my_wechat](/imgs/my_wechat_QR.png)

- We have a WeChat group, welcome to join us.

Owner

GitHub Events