The source codes for the paper "Network of Recurrent Neural Networks: Design for Emergence":
@inproceedings{wang2018network,
title={Network of Recurrent Neural Networks: Design for Emergence},
author={Wang, Chaoming and Zeng, Yi},
booktitle={International Conference on Neural Information Processing},
pages={89--102},
year={2018},
organization={Springer}
}
or
@article{Wang2017Network,
title={Network of Recurrent Neural Networks},
author={Wang, Chao Ming},
journal={arXiv preprint arXiv:1710.03414},
year={2017},
}
The experiment requirements are:
tensorflow-gpu=1.2
python>=3.5
xlwt>=1.0
tqdm
numpy
1, Install minconda ,
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
2, Type this in ~/.bash_profile
export PATH="$HOME/miniconda3/bin:$PATH"
3, Then try launching a new terminal and create an environment named py3k:
conda create -n py3k anaconda python=3.5
source activate py3k
4, Let's install tensorflow-gpu=1.2:
conda install numpy
conda install tensorflow-gpu=1.2
1, download data
You can download CoNLL-2003 from this repository NER/corpus/CoNLL-2003/.
Or, you can directly use the data we have downloaded in directory data/.
At the same time, you should download the MNIST AND put it into the directory data/.
2, build data
Run ner_build_data.py.
python ner_eval_ner.py --glove_filename ${glove_data_path}/glove.6B.300d.txt
1, If you want to suppress I log messages in tensorflow, please
type following commands in Terminal:
export TF_CPP_MIN_LOG_LEVEL=2
Meanwhile, if you want to specify what GPUs you are going to use, please type the following command in terminal:
export CUDA_VISIBLE_DEVICES=1,2
The above command means to use GPUs which ID is 1 or 2.
2, Run Named Entity Recognition task for single experiment:
python ner_eval.py --lr 0.001 \
--lr_decay 0.95 \
--nepoch_no_imprv 5 \
--nepochs 40 \
--model_name rnn-relu \
--hidden_size 197
Then you will see following similar outputs:
Epoch 2 out of 40
703/703 [==============================] - 19s - train loss: 2.7989
- dev acc 96.38 - f1 82.98
- new best score!
Epoch 3 out of 40
703/703 [==============================] - 20s - train loss: 2.2628
- dev acc 96.83 - f1 84.91
- new best score!
Epoch 4 out of 40
703/703 [==============================] - 21s - train loss: 1.9565
- dev acc 97.08 - f1 86.21
- new best score!
3, Run MNIST Classification task for single experiment:
python mnist_eval.py --lr 0.001 \
--dropout 0.5 \
--lr_decay 0.9 \
--hidden 198 \
--model irnn
And you will see the same output as the above NER task.
4, Also, you can type following commands:
cd run/
bash ner-20w.sh
bash mnist-test.sh
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