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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

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  • Jupyter Notebook 100.0%