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cprnn's Introduction

CP-RNN

This repository is the official implementation of Paper Title.

Installation

Install package and dependencies:

python setup.py install
pip install -r requirements.txt

Build data

To generate Penn Tree Bank dataset, Anna dataset or a Toy dataset run:

python cprnn/features/build_features.py -d [toy/ptb/anna]

Train

Enter training configs in configs.yaml then run the command below. Results will be saved in runs folder.

python train.py

Visualize

To visualize the training process run:

tensorboard --logdir=runs

Reproducing results

To run all experiments in the figure below run:

bash job.sh

Then visualize using:

python cprnn/visualization/bpc_vs_params.py

img.png

Sample Outputs

CP-RNN Rank 128 @ Epoch 50

The took said a come in investment it were all admines in the coming thrual and spock industrial stock o

CP-RNN Rank 16 @ Epoch 50

Then they in monced this and the as anessue otton thestences a n't as big sayernment agricens is costsha

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Contributors

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