CapsNet-Tensorflow

# CapsNet-Tensorflow [](CONTRIBUTING.md) [](https://opensource.org/licenses/Apache-2.0)  [](https://gitter.im/CapsNet-Tensorflow/Lobby) A Tensorflow implementation of CapsNet based on Geoffrey Hinton's paper [Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829)   > **Status:** > 1. The capsule of MNIST version is finished. Now we have two version: > a) tag v0.1, not work well due to Issue #8; > b) the current version, see details in Results section > **Daily task** > 1. multi-GPU support > 2. Improving the reusability of ``capsLayer.py``, what you need is ``import capsLayer.fully_connected`` or ``import capsLayer.conv2d`` in your code > **Others** > 1. [Here(知乎)](https://zhihu.com/question/67287444/answer/251460831) is an answer explaining my understanding of Section 4 of the paper (the core part of CapsNet). It may be helpful in understanding the code. > 2. If you find out any problems, please let me know. I will try my best to 'kill' it ASAP. ## Requirements - Python - NumPy - [Tensorflow](https://github.com/tensorflow/tensorflow) (I'm using 1.3.0, not yet tested for older version) - tqdm (for displaying training progress info) - scipy (for saving images) ## Usage **Step 1.** Clone this repository with ``git``. ``` $ git clone https://github.com/naturomics/CapsNet-Tensorflow.git $ cd CapsNet-Tensorflow ``` **Step 2.** Download the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), ``mv`` and extract it 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 the training: ``` $ pip install tqdm # install it if you haven't installed yet $ python main.py ``` > The default parameters of batch size is 128, and epoch is 50. You may need to modify the ``config.py`` file or use command line parameters to suit your case. In my case, I run ``python main.py --test_sum_freq=200 --batch_size=48`` for my 4G GPU(~10min/epoch) ## Results - training loss    - test acc  > 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, a lot to be built upon ------------ ### TODO: - Finish the MNIST version of capsNet (progress:90%) - Do some different experiments for capsNet: * Try Using other datasets * Adjusting the model structure - There is [another new paper](https://openreview.net/pdf?id=HJWLfGWRb) about capsules(submitted to ICLR 2018), a follow-up of the CapsNet paper. ## My weChat:   - Our WeChat group is growing fast, and [@nb312](https://github.com/nb312) is helping me with the wechat request. The left one is my wechat QR, but if you just want to join our group, please contact [@nb312](https://github.com/nb312) by the QR in the right side. ### Reference - [XifengGuo/CapsNet-Keras](https://github.com/XifengGuo/CapsNet-Keras): referred for code optimization