This implementation is fork of https://github.com/XifengGuo/CapsNet-Keras , applied to IMDB texts reviews dataset.
A Keras implementation of CapsNet in the paper: Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules. NIPS 2017
Step 1. Install Keras:
$ pip install keras
Clone this repository with
$ 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
Suppose you have trained a model using the above command, then the trained model will be
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!!!)
Kaggle (this version as self-contained notebook):