NOTE

This implementation is fork of https://github.com/XifengGuo/CapsNet-Keras , applied to IMDB texts reviews dataset.

CapsNet-Keras

A Keras implementation of CapsNet in the paper: Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules. NIPS 2017

Requirements

Keras
matplotlib

Usage

Training

Step 1. Install Keras:

$ pip install keras

Step 2. Clone this repository with git.

$ git clone https://github.com/streamride/CapsNet-keras-imdb.git
$ cd CapsNet-Keras

Step 3. Training:

$ python capsulenet.py

Training with one routing iteration (default 3).

$ python capsulenet.py --num_routing 1

Other parameters include batch_size, epochs, lam_recon, shift_fraction, save_dir can passed to the function in the same way. Please refer to capsulenet.py

Testing

Suppose you have trained a model using the above command, then the trained model will be saved to result/trained_model.h5. Now just launch the following command to get test results.

$ python capsulenet.py --is_training 0 --weights result/trained_model.h5

It will output the testing accuracy and show the reconstructed images. The testing data is same as the validation data. It will be easy to test on new data, just change the code as you want (Of course you can do it!!!)

Other Implementations

Kaggle (this version as self-contained notebook):
- MNIST Dataset running on the standard MNIST and predicting for test data
- MNIST Fashion running on the more challenging Fashion images.
TensorFlow:
- naturomics/CapsNet-Tensorflow
  Very good implementation. I referred to this repository in my code.
- InnerPeace-Wu/CapsNet-tensorflow
  I referred to the use of tf.scan when optimizing my CapsuleLayer.
- LaoDar/tf_CapsNet_simple
PyTorch:
MXNet:
- AaronLeong/CapsNet_Mxnet
Lasagne (Theano):
- DeniskaMazur/CapsNet-Lasagne
Chainer:
- soskek/dynamic_routing_between_capsules