Capsule-Networks

In this repository I will simply explain and implement Capsule Networks on MNIST dataset using Pytorch. For more explanation please check my blog post

what are capsules?

• Briefly explaining it, capsules are small group of neurons where each neuron in a capsule represents various properties of a particular image part.

• Capsules represent relationships between parts of a whole object by using dynamic routing to weight the connections between one layer of capsules and the next and creating strong connections between spatially-related object parts, will be discussed later in the notebook or my bolg post

• The output of each capsule is a vector, this vector has a magnitude and orientation.

  • Magnitude : It is an indicates if that particular part of image is present or not. Basically we can summerize it as the probability of the part existance (It has to be between 0 and 1).

  • Oriantation : It changes if one of the properties of that particular image has changed.

Model Architecture

The capsule network is consisting of two main parts:

• A convolutional encoder.
• A fully connected, linear decoder.

Reconstructed output on test dataset

Project Instructions

Instructions

1. Clone the repository and navigate to the downloaded folder.

   	git clone https://github.com/noureldinalaa/Capsule-Networks/
   	cd Capsule-Networks 
   
   

2. Install packages like pytorch and torch vision and some pip packages in requirements text file :

   	conda install pytorch torchvision -c pytorch
           pip install -r requirements.txt
   

3. Open a terminal window and navigate to the project folder. Open the notebook and follow the instructions.

   	jupyter notebook Capsule network.ipynb
   

References

1. https://arxiv.org/pdf/1710.09829.pdf
2. https://cezannec.github.io/Capsule_Networks/
3. https://kndrck.co/posts/capsule_networks_explained/
4. https://github.com/cezannec/capsule_net_pytorch/blob/master/Capsule_Network.ipynb
5. https://www.youtube.com/watch?v=1dIEyZuZui0