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• 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 byDL HACKS

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

• 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 runpython tune_params.py --help.

• 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)