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The Statistical Recurrent Unit in Pytorch
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README.md
main.py
models.py
sru_tutorial.ipynb
tune_params.py

README.md

The Statistical Recurrent Unit

  • authors: Junier B. Oliva, Barnabas Poczos, Jeff Schneider
  • arxiv: https://arxiv.org/abs/1703.00381
  • Pytorch implemention of the experiment of SRU with pixel-by-pixel sequential MNIST.
  • Powered by DL HACKS

Requirements

  • environment: python3.5
  • pytorch 0.2.0
  • hyperopt 0.1
  • numpy 1.13.1
  • scikit-learn 0.18.2

Implement

  • python main.py sru: trainning RNNs with fixed parameters.
  • python tune_params.py sru : tuning hyper parameters with hyperopt.
  • Choose your model from [sru, gru, lstm]
  • If you need more information, please run python tune_params.py --help.

notes

  • I choose Adam for optimization, though SGD is used in the paper. (It might converge faster)
  • weight_decay is used. (The paper doesn't refer to it)
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