https://github.com/dennybritz/cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow
https://github.com/dennybritz/cnn-text-classification-tf

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Convolutional Neural Network for Text Classification in Tensorflow

• Host: GitHub
• URL: https://github.com/dennybritz/cnn-text-classification-tf
• Owner: dennybritz
• License: apache-2.0
• Created: 2015-11-24T15:43:25.000Z (about 9 years ago)
• Default Branch: master
• Last Pushed: 2024-04-15T19:15:44.000Z (8 months ago)
• Last Synced: 2024-11-27T15:02:47.751Z (15 days ago)
• Language: Python
• Size: 1.08 MB
• Stars: 5,651
• Watchers: 281
• Forks: 2,770
• Open Issues: 110
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
**[This code belongs to the "Implementing a CNN for Text Classification in Tensorflow" blog post.](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/)**

It is slightly simplified implementation of Kim's [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) paper in Tensorflow.

## Requirements

- Python 3

- Tensorflow > 0.12

- Numpy

## Training

Print parameters:

```bash

./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: '3,4,5')

  --num_filters NUM_FILTERS

                        Number of filters per filter size (default: 128)

  --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: 64)

  --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:

```bash

./train.py

```

## Evaluating

```bash

./eval.py --eval_train --checkpoint_dir="./runs/1459637919/checkpoints/"

```

Replace the checkpoint dir with the output from the training. To use your own data, change the `eval.py` script to load your data.

## References

- [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882)

- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1510.03820)