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Implementation of the capsule network using (https://github.com/XifengGuo/CapsNet-Keras). Slightly altered with a custom generator for the data

License: MIT License

Python 100.00%

capsnet_keras's Introduction

CapsNet-Keras

License

A Keras implementation of CapsNet in the paper:
Sara Sabour, Nicholas Frosst, Geoffrey E Hinton. Dynamic Routing Between Capsules. NIPS 2017

Differences with the paper:

  • We use the learning rate decay with decay factor = 0.9 and step = 1 epoch,
    while the paper did not give the detailed parameters (or they didn't use it?).
  • We only report the test errors after 50 epochs training.
    In the paper, I suppose they trained for 1250 epochs according to Figure A.1? Sounds crazy, maybe I misunderstood.
  • We use MSE (mean squared error) as the reconstruction loss and the coefficient for the loss is lam_recon=0.0005*784=0.392.
    This should be equivalent with using SSE (sum squared error) and lam_recon=0.0005 as in the paper.

Install Keras>=2.0.7 with TensorFlow>=1.2 backend.**

pip install tensorflow-gpu
pip install keras

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