CapsNet-Keras

License

A Keras (branch tf2.2 supports TensorFlow 2) implementation of CapsNet in the paper:
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules. NIPS 2017
The current average test error = 0.34% and best test error = 0.30%.

Differences with the paper:

Warnning

Please use Keras==2.0.7 with TensorFlow==1.2 backend, or the K.batch_dot function may not work correctly.

However, if you use Tensorflow>=2.0, then checkout branch tf2.2

Usage

Step 1. Clone this repository to local.

git clone https://github.com/XifengGuo/CapsNet-Keras.git capsnet-keras
cd capsnet-keras
git checkout tf2.2 # Only if use Tensorflow>=2.0

Step 2. Install Keras==2.0.7 with TensorFlow==1.2 backend.

pip install tensorflow-gpu==1.2
pip install keras==2.0.7

or install Tensorflow>=2.0

pip install tensorflow==2.2

Step 3. Train a CapsNet on MNIST

Training with default settings:

python capsulenet.py

More detailed usage run for help:

python capsulenet.py -h

Step 4. Test a pre-trained CapsNet model

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 -t -w 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.

You can also just download a model I trained from https://pan.baidu.com/s/1sldqQo1 or https://drive.google.com/open?id=1A7pRxH7iWzYZekzr-O0nrwqdUUpUpkik

Step 5. Train on multi gpus

This requires Keras>=2.0.9. After updating Keras: 

python capsulenet-multi-gpu.py --gpus 2

It will automatically train on multi gpus for 50 epochs and then output the performance on test dataset. But during training, no validation accuracy is reported.

Results

Test Errors

CapsNet classification test error on MNIST. Average and standard deviation results are reported by 3 trials. The results can be reproduced by launching the following commands. 

 python capsulenet.py --routings 1 --lam_recon 0.0    #CapsNet-v1   
 python capsulenet.py --routings 1 --lam_recon 0.392  #CapsNet-v2
 python capsulenet.py --routings 3 --lam_recon 0.0    #CapsNet-v3 
 python capsulenet.py --routings 3 --lam_recon 0.392  #CapsNet-v4
Method Routing Reconstruction MNIST (%) Paper
Baseline -- -- -- 0.39
CapsNet-v1 1 no 0.39 (0.024) 0.34 (0.032)
CapsNet-v2 1 yes 0.36 (0.009) 0.29 (0.011)
CapsNet-v3 3 no 0.40 (0.016) 0.35 (0.036)
CapsNet-v4 3 yes 0.34 (0.016) 0.25 (0.005)

Losses and accuracies:

Training Speed

About 100s / epoch on a single GTX 1070 GPU.
About 80s / epoch on a single GTX 1080Ti GPU.
About 55s / epoch on two GTX 1080Ti GPU by using capsulenet-multi-gpu.py.

Reconstruction result

The result of CapsNet-v4 by launching 

python capsulenet.py -t -w result/trained_model.h5

Digits at top 5 rows are real images from MNIST and digits at bottom are corresponding reconstructed images.

Manipulate latent code

python capsulenet.py -t --digit 5 -w result/trained_model.h5

For each digit, the ith row corresponds to the ith dimension of the capsule, and columns from left to right correspond to adding [-0.25, -0.2, -0.15, -0.1, -0.05, 0, 0.05, 0.1, 0.15, 0.2, 0.25] to the value of one dimension of the capsule.

As we can see, each dimension has caught some characteristics of a digit. The same dimension of different digit capsules may represent different characteristics. This is because that different digits are reconstructed from different feature vectors (digit capsules). These vectors are mutually independent during reconstruction.

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