Chainer port of NeuralCompressor (nncompress)
Original paper: Compressing Word Embeddings via Deep Compositional Code Learning
Author's implementation: https://github.com/zomux/neuralcompressor
- Python 3.5.1+
- Chainer 4.1.0+
- CuPy 4.1.0+
- logzero
- Prepare data
> bash scripts/download_glove_data.sh
- Convert the GloVe embeddings to numpy format
> python scripts/convert_glove2numpy.py data/glove.6B.300d.txt
- Train the embedding quantization model
> python train.py -b 128 -gpu 0 -O Adam --M 8 --K 8 --tau 1.0 --input-matrix ./data/glove.6B.300d.npy
...
63 198000 16.7019 17.0396 0.788086 0.788811
63 198000 16.7019 17.0396 0.788086 0.788811
63 199000 17.7445 17.0096 0.777498 0.786644
63 199000 17.7445 17.0096 0.777498 0.786644
64 200000 16.878 17.0317 0.789763 0.787662
64 200000 16.878 17.0317 0.789763 0.787662
[I 180528 09:42:24 train:114] Training complete!!
[I 180528 09:42:24 resource:113] EXIT TIME: 20180528 - 09:42:24
[I 180528 09:42:24 resource:115] Duration: 0:26:48.503415
...
- Export the word codes and the codebook matrix
> python decode.py --gpu 0 --model result/20180510_224328_model_seed_0_optim_Adam_tau_0.1_batch_64_M_32_K_16/iter_1211000.npz --vocab data/glove.6B.300d.word --embed data/glove.6B.300d.npy
It will generate two files in result dir:
- iter_199000.npz.codebook.npy
- this contains parameters of the compressed embedding matrix
- iter_199000.npz.codes
- this contains code representation of each word
- Check the codes
> head -100 iter_199000.npz.codes
...
human 5 2 7 2 4 7 4 1
india 2 2 7 0 4 1 0 6
defense 2 2 7 0 4 4 0 1
asked 7 2 7 2 4 4 2 6
total 2 0 7 4 4 3 0 5
october 2 0 7 4 4 7 7 7
players 0 0 7 6 4 4 0 6
bill 7 2 7 2 4 7 2 0
...
You can see that similar words have similar code representations.
dog 2 3 7 6 4 4 2 1
dogs 2 3 7 6 4 4 2 6
cat 4 3 7 1 4 7 2 7
penguin 6 3 7 2 6 7 2 7
@inproceedings{shu2018compressing,
title={Compressing Word Embeddings via Deep Compositional Code Learning},
author={Raphael Shu and Hideki Nakayama},
booktitle={International Conference on Learning Representations (ICLR)},
year={2018},
url={https://openreview.net/forum?id=BJRZzFlRb},
}
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