Code for paper "Full-Capacity Unitary Recurrent Neural Networks"
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matlab Adding TIMIT prediction code. Apr 4, 2017
README.md Update README.md Apr 28, 2017
config_mnist_LSTM256_lr0-001_patience5.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_LSTM256_lr0-001_permuted_patience5.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_LSTM_lr0-001_patience5.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_LSTM_lr0-001_permuted_patience5.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_fulluRNN116_lr0-001_lrng0-000001_patience5_natGradRMS.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_fulluRNN116_lr0-001_lrng0-000001_permuted_patience5_natGradRMS.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_fulluRNN512_lr0-0001_lrng0-000001_patience5_natGradRMS.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_fulluRNN512_lr0-0001_lrng0-000001_permuted_patience5_natGradRMS.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_restricteduRNN_lr0-0001_patience5.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_restricteduRNN_lr0-0001_permuted_patience5.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_restricteduRNNfast_lr0-0001_patience5.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
config_mnist_restricteduRNNfast_lr0-0001_permuted_patience5.yaml MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
custom_layers.py MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
custom_optimizers.py MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
download_and_unzip_matlab_code.py Adding TIMIT prediction code. Apr 4, 2017
fftconv.py Adding TIMIT prediction code. Apr 4, 2017
memory_problem.py Fixing a small bug in memory problem. Feb 23, 2017
mnist.py MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
models.py MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
optimizations.py MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
plot_results.ipynb MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
recon_timitpred.py Adding TIMIT prediction code. Apr 4, 2017
run_memory_problem.sh Adding memory problem code. Nov 22, 2016
run_mnist.sh MNIST experiment code for "Full-Capacity URNNs" Oct 28, 2016
run_timitpred_LSTM.sh Adding TIMIT prediction code. Apr 4, 2017
run_timitpred_fullCapacityURNN.sh Adding TIMIT prediction code. Apr 4, 2017
run_timitpred_restrictedCapacityURNN.sh Adding TIMIT prediction code. Apr 4, 2017
timit_dev_spk.list Adding TIMIT prediction code. Apr 4, 2017
timit_prediction.py Adding TIMIT prediction code. Apr 4, 2017
timit_test_spk.list Adding TIMIT prediction code. Apr 4, 2017
util.py Adding TIMIT prediction code. Apr 4, 2017

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.

Instructions for TIMIT prediction experiment

  1. Downsample the TIMIT dataset to 8ksamples/sec using Matlab by running downsample_audio.m from the matlab directory. Make sure you modify the paths in downsample_audio.m for your system.

  2. Download Matlab evaluation code using download_and_unzip_matlab_code.py, which should download and unzip all the required toolboxes to the matlab folder.

  3. Run the experiments using the shell scripts: run_timit_prediction_<model>.sh, which will train the model and score the resulting audio using the Matlab evaluation toolboxes.