https://github.com/adambielski/capsnet-pytorch

PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules
https://github.com/adambielski/capsnet-pytorch

capsnet capsules deep-learning dynamic-routing-between-capsules machine-learning mnist pytorch

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PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules

• Host: GitHub
• URL: https://github.com/adambielski/capsnet-pytorch
• Owner: adambielski
• License: bsd-3-clause
• Created: 2017-11-28T22:33:57.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2021-04-13T11:39:54.000Z (over 3 years ago)
• Last Synced: 2024-10-15T12:11:44.413Z (29 days ago)
• Topics: capsnet, capsules, deep-learning, dynamic-routing-between-capsules, machine-learning, mnist, pytorch
• Language: Python
• Size: 283 KB
• Stars: 489
• Watchers: 11
• Forks: 73
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Dynamic Routing Between Capsules - PyTorch implementation

PyTorch implementation of NIPS 2017 paper [Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829) from Sara Sabour, Nicholas Frosst and Geoffrey E. Hinton.

The hyperparameters and data augmentation strategy strictly follow the paper.

## Requirements

Only [PyTorch](http://pytorch.org/) with torchvision is required (tested on pytorch 0.2.0 and 0.3.0). Jupyter and matplotlib is required to run the notebook with visualizations.

## Usage

Train the model by running

    python net.py

Optional arguments and default values:

```

  --batch-size N          input batch size for training (default: 128)

  --test-batch-size N     input batch size for testing (default: 1000)

  --epochs N              number of epochs to train (default: 250)

  --lr LR                 learning rate (default: 0.001)

  --no-cuda               disables CUDA training

  --seed S                random seed (default: 1)

  --log-interval N        how many batches to wait before logging training

                          status (default: 10)

  --routing_iterations    number of iterations for routing algorithm (default: 3)

  --with_reconstruction   should reconstruction layers be used

```

MNIST dataset will be downloaded automatically.

## Results

The network trained with reconstruction and 3 routing iterations on MNIST dataset achieves **99.65%** accuracy on test set. The test loss is still slightly decreasing, so the accuracy could probably be improved with more training and more careful learning rate schedule.

## Visualizations

We can create visualizations of digit reconstructions from DigitCaps (e.g. Figure 3 in the paper)

![Reconstructions](images/reconstructions.png)

We can also visualize what each dimension of digit capsule represents (Section 5.1, Figure 4 in the paper). 

Below, each row shows the reconstruction when one of the 16 dimensions in the DigitCaps representation is tweaked by intervals of 0.05 in the range [−0.25, 0.25].

![Perturbations](images/perturbations.png)

We can see what individual dimensions represent for digit 7,  e.g. dim6 - stroke thickness, dim11 - digit width, dim 15 - vertical shift.

Visualization examples are provided in a [jupyter notebook](reconstruction_visualization.ipynb)