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

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(知乎) 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 😊

• Python
• NumPy
• Tensorflow (I'm using 1.3.0, others should work, too)
• tqdm (for showing training progress info)
• scipy (for saving image)

Step 1. Clone this repository with git.

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

Step 2. Download MNIST dataset, 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

$ python eval.py --is_training False

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

• training loss

• test acc

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

• 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 about capsules(submitted to ICLR 2018), follow-up.

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