文件列表:

.travis.yml (242, 2019-04-04)
ANN (0, 2019-04-04)
ANN\test_ann.py (11638, 2019-04-04)
ANN\text_ann.py (5895, 2019-04-04)
ANN\train_ann.py (18020, 2019-04-04)
CNN (0, 2019-04-04)
CNN\test_cnn.py (11833, 2019-04-04)
CNN\text_cnn.py (7551, 2019-04-04)
CNN\train_cnn.py (18334, 2019-04-04)
CRNN (0, 2019-04-04)
CRNN\test_crnn.py (11934, 2019-04-04)
CRNN\text_crnn.py (9424, 2019-04-04)
CRNN\train_crnn.py (18519, 2019-04-04)
FastText (0, 2019-04-04)
FastText\test_fast.py (11558, 2019-04-04)
FastText\text_fast.py (5313, 2019-04-04)
FastText\train_fast.py (18102, 2019-04-04)
HAN (0, 2019-04-04)
HAN\test_han.py (11734, 2019-04-04)
HAN\text_han.py (8326, 2019-04-04)
HAN\train_han.py (18173, 2019-04-04)
LICENSE (11357, 2019-04-04)
RCNN (0, 2019-04-04)
RCNN\test_rcnn.py (11934, 2019-04-04)
RCNN\text_rcnn.py (9727, 2019-04-04)
RCNN\train_rcnn.py (18519, 2019-04-04)
RNN (0, 2019-04-04)
RNN\test_rnn.py (11734, 2019-04-04)
RNN\text_rnn.py (10908, 2019-04-04)
RNN\train_rnn.py (18173, 2019-04-04)
SANN (0, 2019-04-04)
SANN\test_sann.py (12013, 2019-04-04)
SANN\text_sann.py (12163, 2019-04-04)
SANN\train_sann.py (18600, 2019-04-04)
data (0, 2019-04-04)
data\data_sample.json (795, 2019-04-04)
requirements.txt (215, 2019-04-04)
... ...

# Deep Learning for Multi-Label Text Classification [](https://www.python.org/downloads/) [](https://travis-ci.org/RandolphVI/Multi-Label-Text-Classification) [](https://www.codacy.com/app/chinawolfman/Multi-Label-Text-Classification?utm_source=github.com&utm_medium=referral&utm_content=RandolphVI/Multi-Label-Text-Classification&utm_campaign=Badge_Grade) [](https://www.apache.org/licenses/LICENSE-2.0) [](https://github.com/RandolphVI/Multi-Label-Text-Classification/issues) This repository is my research project, and it is also a study of TensorFlow, Deep Learning (Fasttext, CNN, LSTM, etc.). The main objective of the project is to solve the multi-label text classification problem based on Deep Neural Networks. Thus, the format of the data label is like [0, 1, 0, ..., 1, 1] according to the characteristics of such a problem. ## Requirements - Python 3.6 - Tensorflow 1.1 + - Numpy - Gensim ## Innovation ### Data part 1. Make the data support **Chinese** and English (Which use `jieba` seems easy). 2. Can use **your own pre-trained word vectors** (Which use `gensim` seems easy). 3. Add embedding visualization based on the **tensorboard**. ### Model part 1. Add the correct **L2 loss** calculation operation. 2. Add **gradients clip** operation to prevent gradient explosion. 3. Add **learning rate decay** with exponential decay. 4. Add a new **Highway Layer** (Which is useful according to the model performance). 5. Add **Batch Normalization Layer**. ### Code part 1. Can choose to **train** the model directly or **restore** the model from the checkpoint in `train.py`. 2. Can predict the labels via **threshold** and **top-K** in `train.py` and `test.py`. 3. Can calculate the evaluation metrics --- **AUC** & **AUPRC**. 4. Add `test.py`, the **model test code**, it can show the predicted values and predicted labels of the data in Testset when creating the final prediction file. 5. Add other useful data preprocess functions in `data_helpers.py`. 6. Use `logging` for helping to record the whole info (including **parameters display**, **model training info**, etc.). 7. Provide the ability to save the best n checkpoints in `checkmate.py`, whereas the `tf.train.Saver` can only save the last n checkpoints. ## Data See data format in `data` folder which including the data sample files. ### Text Segment You can use `jieba` package if you are going to deal with the Chinese text data. ### Data Format This repository can be used in other datasets (text classification) in two ways: 1. Modify your datasets into the same format of the sample. 2. Modify the data preprocess code in `data_helpers.py`. Anyway, it should depend on what your data and task are. ### Pre-trained Word Vectors You can pre-training your word vectors (based on your corpus) in many ways: - Use `gensim` package to pre-train data. - Use `glove` tools to pre-train data. - Even can use a **fasttext** network to pre-train data. ## Network Structure ### FastText  References: - [Bag of Tricks for Efficient Text Classification](https://arxiv.org/pdf/1607.01759.pdf) --- ### TextANN  References: - **Personal ideas ** --- ### TextCNN  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) --- ### TextRNN **Warning: Model can use but not finished yet ¤!**  #### TODO 1. Add BN-LSTM cell unit. 2. Add attention. References: - [Recurrent Neural Network for Text Classification with Multi-Task Learning](http://www.***i.org/ocs/index.php/AAAI/AAAI15/paper/download/9745/9552) --- ### TextCRNN  References: - **Personal ideas ** --- ### TextRCNN  References: - **Personal ideas ** --- ### TextHAN References: - [Hierarchical Attention Networks for Document Classification](https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf) --- ### TextSANN **Warning: Model can use but not finished yet ¤!** #### TODO 1. Add attention penalization loss. 2. Add visualization. References: - [A STRUCTURED SELF-ATTENTIVE SENTENCE EMBEDDING](https://arxiv.org/pdf/1703.03130.pdf) --- ## About Me é¨Randolph SCU SE Bachelor; USTC CS Master Email: chinawolfman@hotmail.com My Blog: [randolph.pro](http://randolph.pro) LinkedIn: [randolph's linkedin](https://www.linkedin.com/in/randolph-%E9%BB%84%E5%A8%81/)

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：