A Keras implementation of CapsNet in Hinton's paper Dynamic Routing Between Capsules
Recent updates:
- Accelerate the code by optimizing Primary Capsule. Now it takes 110s per epoch.
TODO
- I will check the logic carefully in routing algorithm.
- The model has 8M parameters, while the paper said it should be 11M. I'll figure out what's the problem.
Contribution
- Your contribution to the repo is welcome. Open an issue or contact me with
guoxifeng1990@163.comor WeChat (微信号)wenlong-guo.
- Keras
- matplotlib
Step 1. Install Keras:
$ pip install keras
Step 2.
Clone this repository with git.
$ git clone https://github.com/xifengguo/CapsNet-Keras.git
$ cd CapsNet-Keras
Step 3. Training:
$ python CapsNet.py
Training without routing by setting num_routing=0.
$ python CapsNet.py --num_routing 0
Other parameters include batch_size, epochs, lam_recon, shift_fraction, save_dir can
passed to the function in the same way. Please refer to CapsNet.py
Main result by launching python CapsNet.py:
| Epoch | 1 | 5 | 10 | 20 |
|---|---|---|---|---|
| train_acc | 83.4 | 98.2 | 98.7 | 99.1 |
| vali_acc | 97.8 | 98.9 | 99.0 | 99.2 |
Losses and accuracies:
Results without routing algorithm
by launching python CapsNet.py --num_routing 0
The prior b is fixed to 0. But, interestingly, the model still gets good results, even better than using routing.
| Epoch | 1 | 2 | 5 | 10 | 20 |
|---|---|---|---|---|---|
| train_acc | 90.3 | 98.2 | 99.0 | 99.4 | 99.7 |
| vali_acc | 98.8 | 99.1 | 99.36 | 99.48 | 99.53 |
Every epoch consumes about 110s on a single GTX 1070 GPU.
-
TensorFlow:
- CapsNet-Tensorflow: Very good implementation. I referred to this repository in my code.
- CapsNet-tensorflow
-
PyTorch:
-
MXNet:
-
Chainer: