gzilt-playground / char-cnn-text-classification-tensorflow

Simple Convolutional Neural Network (CNN) for sentiment classification of Chinese movie reviews.

char-cnn-text-classification-tensorflow

Simple Convolutional Neural Network (CNN) for text classification at character level. In this project we will implement a Chinese Movie Sentiment (Positive/Negative) Classifier with CNN using TensorFlow. A manually labeled small corpus (HK movie reviews) has been uploaded for you to try out the network.

Mostly reused code from https://github.com/dennybritz/cnn-text-classification-tf which was posted by Denny Britz. Check out his great blog post on CNN classification http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/.

Things are modified from the original author:

• modified the data helpers to support Chinese to train the embeddings at character level
• added Tensorboard visualization for embeddings
• added sample.py which will generate prediction output
• added serve.py which will serve the classifier via Flask

Requirements

• Tensorflow 1.0 or up

Basic Usage

To train with the Chinese movie reviews dataset, run: python train.py

To visualize results and embeddings, run: tensorboard --logdir ./runs/1492654198/summaries/

To predict from a trained model, run with checkpoint_dir argument python sample.py --checkpoint_dir=./runs/1492654198/checkpoints/ --text="套戲好鬼悶"

To serve the model via a Flask API, run: python serve.py --checkpoint_dir=./runs/1492656039/checkpoints/ then send a HTTP POST request to http://localhost:5000/predict with the following json body: {"text": "套戲好鬼悶"}

TODO

Some updates will be published soon.

• Train the embeddings at word level using jieba's tokenizer or other pre-trained word embeddings
• Use high level APIS such as tf.layers or tf.keras(will be available in TensorFLow 1.2)