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tensorflow-isan-rnn

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Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. http://proceedings.mlr.press/v70/foerster17a/foerster17a.pdf

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// repository documentation

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# Input Switched Affine Networks: An RNN Architecture Designed for Interpretability (ICML 2017)
[![license](https://img.shields.io/badge/License-Apache-brightgreen.svg)](https://github.com/philipperemy/keras-attention-mechanism/blob/master/LICENSE) [![dep1](https://img.shields.io/badge/Tensorflow-1.2+-brightgreen.svg)](https://www.tensorflow.org/)
> There exist many problem domains where the
interpretability of neural network models is essential
for deployment. Here we introduce a recurrent
architecture composed of input-switched
affine transformations – in other words an RNN
without any explicit nonlinearities, but with inputdependent
recurrent weights. This simple form
allows the RNN to be analyzed via straightforward
linear methods: we can exactly characterize
the linear contribution of each input to the model
predictions; we can use a change-of-basis to disentangle
input, output, and computational hidden
unit subspaces; we can fully reverse-engineer
the architecture’s solution to a simple task. Despite
this ease of interpretation, the input switched
affine network achieves reasonable performance
on a text modeling tasks, and allows greater computational
efficiency than networks with standard
nonlinearities. --Abstract
## Parenthesis Task
The implementation was trained on the Parenthesis task. Here is the result:
<div align="center">
  <img src="assets/run.1.png" width="500"><br><br>
  <p><i>Loss as a function of the number of steps</i></p>
</div>

## Resources
- The paper is available at http://proceedings.mlr.press/v70/foerster17a/foerster17a.pdf

## Contributing
Thanks to Justin Gilmer, one of the authors of the paper for providing some source code under the Apache license.

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