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<h1><a href="https://nn.labml.ai/capsule_networks/index.html">Capsule Networks</a></h1>
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation/tutorial of
<a href="https://arxiv.org/abs/1710.09829">Dynamic Routing Between Capsules</a>.</p>
<p>Capsule network is a neural network architecture that embeds features
as capsules and routes them with a voting mechanism to next layer of capsules.</p>
<p>Unlike in other implementations of models, we’ve included a sample, because
it is difficult to understand some concepts with just the modules.
<a href="mnist.html">This is the annotated code for a model that uses capsules to classify MNIST dataset</a></p>
<p>This file holds the implementations of the core modules of Capsule Networks.</p>
<p>I used <a href="https://github.com/jindongwang/Pytorch-CapsuleNet">jindongwang/Pytorch-CapsuleNet</a> to clarify some
confusions I had with the paper.</p>
<p>Here’s a notebook for training a Capsule Network on MNIST dataset.</p>
<p><a href="https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/capsule_networks/mnist.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
<a href="https://app.labml.ai/run/e7c08e08586711ebb3e30242ac1c0002"><img alt="View Run" src="https://img.shields.io/badge/labml-experiment-brightgreen" /></a></p>
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