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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 run
python 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)