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https://github.com/icoxfog417/tensorflow_qrnn
QRNN implementation for TensorFlow
https://github.com/icoxfog417/tensorflow_qrnn
natural-language-processing tensorflow
Last synced: about 1 month ago
JSON representation
QRNN implementation for TensorFlow
- Host: GitHub
- URL: https://github.com/icoxfog417/tensorflow_qrnn
- Owner: icoxfog417
- License: mit
- Created: 2016-12-11T09:43:56.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2023-03-24T21:54:35.000Z (over 1 year ago)
- Last Synced: 2024-05-03T06:20:53.198Z (5 months ago)
- Topics: natural-language-processing, tensorflow
- Language: Python
- Size: 217 KB
- Stars: 238
- Watchers: 9
- Forks: 40
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Tensorflow QRNN
QRNN implementation for TensorFlow. Implementation refer to below blog.
[New neural network building block allows faster and more accurate text understanding](http://metamind.io/research/new-neural-network-building-block-allows-faster-and-more-accurate-text-understanding/)
## Dependencies
* TensorFlow: 0.12.0
* scikit-learn: 0.18.1 (for working check)
## How to run
**Forward Test**
To confirm forward propagation, run below script.
```
python test_tf_qrnn_forward.py
```
**Working Check**
To confirm the performance of QRNN compare with baseline(LSTM), run below script.
Dataset is [scikit-learn's digit dataset](http://scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html).
```
python test_tf_qrnn_work.py
```
You can check the calculation result by [TensorBoard](https://www.tensorflow.org/versions/r0.12/how_tos/summaries_and_tensorboard/index.html).
For example.
```
tensorboard --logdir=./summary/qrnn
```
## Experiments
```
Baseline(LSTM) Working check
Iter 0: loss=2.473149299621582, accuracy=0.1171875
Iter 100: loss=0.31235527992248535, accuracy=0.921875
Iter 200: loss=0.1704500913619995, accuracy=0.9453125
Iter 300: loss=0.0782063901424408, accuracy=0.9765625
Iter 400: loss=0.04097321629524231, accuracy=1.0
Iter 500: loss=0.023687714710831642, accuracy=0.9921875
Iter 600: loss=0.07718617469072342, accuracy=0.9765625
Iter 700: loss=0.02005828730762005, accuracy=0.9921875
Iter 800: loss=0.006271282210946083, accuracy=1.0
Iter 900: loss=0.007853344082832336, accuracy=1.0
Testset Accuracy=0.9375
takes 15.83749008178711 seconds.
```
```
QRNN Working check
Iter 0: loss=6.942812919616699, accuracy=0.0703125
Iter 100: loss=1.6366937160491943, accuracy=0.59375
Iter 200: loss=0.7058627605438232, accuracy=0.796875
Iter 300: loss=0.3940553069114685, accuracy=0.8984375
Iter 400: loss=0.2623080909252167, accuracy=0.9375
Iter 500: loss=0.3940059542655945, accuracy=0.921875
Iter 600: loss=0.1395827978849411, accuracy=0.96875
Iter 700: loss=0.11944477260112762, accuracy=0.984375
Iter 800: loss=0.1389300674200058, accuracy=0.9765625
Iter 900: loss=0.09582504630088806, accuracy=0.96875
Testset Accuracy=0.9140625
takes 13.540465116500854 seconds.
```