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CNN for Chinese Text Classification in Tensorflow

Sentiment classification forked from dennybritz/cnn-text-classification-tf, make the data helper supports Chinese language and modified the embedding from word-level to character-level, though that increased vocabulary size, and also i've implemented the Character-Aware Neural Language Models network structure which CNN + Highway network to improve the performance, this version can achieve an accuracy of 98% with the Chinese corpus

This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post.

It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.

Requirements

  • Python 2.7
  • Tensorflow 0.9.0
  • Numpy

Running

Print parameters:

./train.py --help
optional arguments:
  -h, --help            show this help message and exit
  --embedding_dim EMBEDDING_DIM
                        Dimensionality of character embedding (default: 128)
  --filter_sizes FILTER_SIZES
                        Comma-separated filter sizes (default: '1,2,3,4,5,6,8')
  --num_filters NUM_FILTERS
                        Number of filters per filter size (default: '50,100,150,150,200,200,200')
  --l2_reg_lambda L2_REG_LAMBDA
                        L2 regularizaion lambda (default: 0.0)                        
  --dropout_keep_prob DROPOUT_KEEP_PROB
                        Dropout keep probability (default: 0.5)
  --batch_size BATCH_SIZE
                        Batch Size (default: 32)
  --num_epochs NUM_EPOCHS
                        Number of training epochs (default: 100)
  --evaluate_every EVALUATE_EVERY
                        Evaluate model on dev set after this many steps
                        (default: 100)
  --checkpoint_every CHECKPOINT_EVERY
                        Save model after this many steps (default: 100)
  --allow_soft_placement ALLOW_SOFT_PLACEMENT
                        Allow device soft device placement
  --noallow_soft_placement
  --log_device_placement LOG_DEVICE_PLACEMENT
                        Log placement of ops on devices
  --nolog_device_placement

Train:

./train.py

References

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