Code for paper "Full-Capacity Unitary Recurrent Neural Networks"
Python Jupyter Notebook Shell
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README.md
config_mnist_LSTM256_lr0-001_patience5.yaml
config_mnist_LSTM256_lr0-001_permuted_patience5.yaml
config_mnist_LSTM_lr0-001_patience5.yaml
config_mnist_LSTM_lr0-001_permuted_patience5.yaml
config_mnist_fulluRNN116_lr0-001_lrng0-000001_patience5_natGradRMS.yaml
config_mnist_fulluRNN116_lr0-001_lrng0-000001_permuted_patience5_natGradRMS.yaml
config_mnist_fulluRNN512_lr0-0001_lrng0-000001_patience5_natGradRMS.yaml
config_mnist_fulluRNN512_lr0-0001_lrng0-000001_permuted_patience5_natGradRMS.yaml
config_mnist_restricteduRNN_lr0-0001_patience5.yaml
config_mnist_restricteduRNN_lr0-0001_permuted_patience5.yaml
config_mnist_restricteduRNNfast_lr0-0001_patience5.yaml
config_mnist_restricteduRNNfast_lr0-0001_permuted_patience5.yaml
custom_layers.py
custom_optimizers.py
fftconv.py
memory_problem.py
mnist.py
models.py
optimizations.py
plot_results.ipynb
run_memory_problem.sh
run_mnist.sh

README.md

urnn

Code for paper "Full-Capacity Unitary Recurrent Neural Networks." Based on the complex_RNN repository from github.com/amarshah/complex_RNN.

Code coming soon for other experiments.

If you find this code useful, please cite the following references:

[1] M. Arjovsky, A. Shah, and Y. Bengio, “Unitary Evolution Recurrent Neural Networks,” Proc. International Conference on Machine Learning (ICML), 2016, pp. 1120–1128.

[2] S. Wisdom, T. Powers, J.R. Hershey, J. Le Roux, and L. Atlas, "Full-Capacity Unitary Recurrent Neural Networks," Advances in Neural Information Processing Systems (NIPS), 2016.