Capsule Networks

Implemented using TensorFlow
Based on the paper "Dynamic Routing Between Capsules"

Requirements

• python3
• TensorFlow
• NumPy
• Matplotlib

Dataset

• MNIST datasets You can download at download link and locate them in the './MNIST_data' directory.

How to run Training & Testing

Locate the MNIST datasets in the './MNIST_data' directory and just run capsNet.ipynb

Results

Accuracy

• The best error

%	Training	Validation	Test
Implemented	0.0273	0.641	0.6611
Paper	-	-	0.25

The performance measurement was done with a CapsNet with 3 routing iterations and reconstruction.

Sample MNIST Test Reconstruction

Reconstructions with less training(1 epoch)

Reconstructions with more trained(50 epoch)

• Sample MNIST test reconstructions of a CapsNet with 3 routing iterations.
• The images on the first row are the input images, and that on the second row are the reconstructions.
• We can see that the reconstructions preserves many of the details of the input while smoothing the noise.
• We can get more detailed reconstruction with more traind model.
  
  

What the individual dimensions of a capsule represent

• We can see what the individual dimensions represent by feeding a perturbed version of the activity vector to the decoder network and see how the perturbation affects the reconstruction.
• By the experiment, the representations of each dimension can be translated as follows.
• The dimension of the DigitCaps representation is tweaked by intervals of 0.05 in the range [−0.25, 0.25].

• Localized skew

• Scale of thickness

• Width