https://github.com/cn-tu/bayesianrecurrentnn
Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al
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Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al
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# Bayesian Recurrent Neural Networks
This is a replication of the paper 'Bayesian Recurrent Neural Networks' by Meire Fortunato, Charles Blundell, Oriol Vinyals.
Paper: https://arxiv.org/abs/1704.02798
Status: Basic model replicated.
# Requirements
- Python 3.5
- [TensorFlow 1.3.0](https://www.tensorflow.org/)
# Usage
$ sh download_ptb.sh
$ python bayesian_rnn.py -model medium -log_sigma1 -1.0 -log_sigma2 -7.0 -prior_pi 0.25
### To-do:
- Implement posterior sharpening.
- Implement image captioning experiment.
### Acknowledgements
Thanks to Meire Fortunato for providing the Bayes by Backprop/cell code and @alexkrk for an initial implementation.
Owner
- Name: CN Group, Institute of Telecommunications, TU Wien
- Login: CN-TU
- Kind: organization
- Location: Vienna, Austria
- Repositories: 16
- Profile: https://github.com/CN-TU
Communication Networks Group, TU Wien