https://github.com/fendouai/Chinese-Text-Classification

Chinese-Text-Classification，Tensorflow CNN（卷积神经网络）实现的中文文本分类。QQ群：522785813，微信群二维码：http://www.tensorflownews.com/
https://github.com/fendouai/Chinese-Text-Classification

chinese cnn cnn-text-classification jieba tensorflow text-classification

Last synced: about 1 year ago
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Chinese-Text-Classification，Tensorflow CNN（卷积神经网络）实现的中文文本分类。QQ群：522785813，微信群二维码：http://www.tensorflownews.com/

• Host: GitHub
• URL: https://github.com/fendouai/Chinese-Text-Classification
• Owner: fendouai
• License: apache-2.0
• Created: 2017-11-09T03:23:46.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-11-10T04:41:24.000Z (over 8 years ago)
• Last Synced: 2024-08-01T18:39:11.566Z (almost 2 years ago)
• Topics: chinese, cnn, cnn-text-classification, jieba, tensorflow, text-classification
• Language: Python
• Homepage: http://www.tensorflownews.com/
• Size: 5.68 MB
• Stars: 290
• Watchers: 24
• Forks: 89
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 用卷积神经网络基于 Tensorflow 实现的中文文本分类

这个项目是基于以下项目改写：

 **[cnn-text-classification-tf](https://github.com/dennybritz/cnn-text-classification-tf)**

 

 [关于 Chinese-Text-Classification 的问题欢迎来这里提问](http://tensorflow123.com/forum.php?mod=viewthread&tid=3)

主要的改动：

* 兼容 tensorflow 1.2 以上

* 增加了中文数据集

* 增加了中文处理流程

## 特性：

* 兼容最新 TensorFlow

* 中文数据集

* 基于 jieba 的中文处理工具

* 模型训练，模型保存，模型评估的完整实现

## 训练结果

![](training.png)

![](training2.png)

## 模型评估

![](eval.png)

以下为原项目的 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 > 1.2

- 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)