A Tensorflow implementation of convolutional neural network to classify sentences

This implementation uses Tensorflow's tf.conv2d to perform 1D convolution on word sequences. It also supports using Google News word2vec pre-trained vectors to initialize word embeddings, which boosts the performance on movie review dataset from ~76% to ~81%.

The original theano implementation of this model by the author is here. Another tensorflow implementation that does not support loading pretrained vectors is here.

Dependency

• python2.7+
• numpy
• tensorflow 1.0+

Data

The data in data/mr/ are movie review polarity data provided here. The current data/word2vec directory is empty. To use the pretrained word2vec embeddings, download the Google News pretrained vector data from this Google Drive link, and unzip it to the directory. It will be a .bin file.

Usage

Preprocess the data

python text_input.py

Train

python train.py

By default the pretrained vectors will be loaded and used to initialize the embeddings. To suppress this, use

python train.py --use_pretrain False

Evaluate

python eval.py

By default evaluation is run over test set. To evaluate over training set, run

python eval.py --train_data

References

1. Kim, Yoon. "Convolutional neural networks for sentence classification." arXiv preprint arXiv:1408.5882 (2014). link

License

MIT