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https://github.com/dongjunlee/text-cnn-tensorflow
Convolutional Neural Networks for Sentence Classification(TextCNN) implements by TensorFlow
https://github.com/dongjunlee/text-cnn-tensorflow
classification deep-learning hb-experiment nlp sentiment-analysis tensorflow tensorflow-models text-cnn
Last synced: about 15 hours ago
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Convolutional Neural Networks for Sentence Classification(TextCNN) implements by TensorFlow
- Host: GitHub
- URL: https://github.com/dongjunlee/text-cnn-tensorflow
- Owner: DongjunLee
- Created: 2017-12-02T17:51:31.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-05-30T05:17:43.000Z (over 5 years ago)
- Last Synced: 2025-01-02T16:12:00.268Z (8 days ago)
- Topics: classification, deep-learning, hb-experiment, nlp, sentiment-analysis, tensorflow, tensorflow-models, text-cnn
- Language: Python
- Homepage:
- Size: 2.39 MB
- Stars: 252
- Watchers: 10
- Forks: 68
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# text-cnn [](https://github.com/hb-research)
This code implements [Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) models.
- Figure 1: Illustration of a CNN architecture for sentence classification
## Requirements
- Python 3.6
- TensorFlow 1.4
- [hb-config](https://github.com/hb-research/hb-config) (Singleton Config)
- tqdm
- requests
- [Slack Incoming Webhook URL](https://my.slack.com/services/new/incoming-webhook/)
## Project Structure
init Project by [hb-base](https://github.com/hb-research/hb-base)
.
├── config # Config files (.yml, .json) using with hb-config
├── data # dataset path
├── notebooks # Prototyping with numpy or tf.interactivesession
├── scripts # download or prepare dataset using shell scripts
├── text-cnn # text-cnn architecture graphs (from input to logits)
├── __init__.py # Graph logic
├── data_loader.py # raw_date -> precossed_data -> generate_batch (using Dataset)
├── hook.py # training or test hook feature (eg. print_variables)
├── main.py # define experiment_fn
├── model.py # define EstimatorSpec
└── predict.py # test trained model
Reference : [hb-config](https://github.com/hb-research/hb-config), [Dataset](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_generator), [experiments_fn](https://www.tensorflow.org/api_docs/python/tf/contrib/learn/Experiment), [EstimatorSpec](https://www.tensorflow.org/api_docs/python/tf/estimator/EstimatorSpec)
- Dataset : [rt-polarity](https://github.com/yoonkim/CNN_sentence), [Sentiment Analysis on Movie Reviews](https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data)
## Todo
- apply embed_type
- CNN-rand
- CNN-static
- CNN-nonstatic
- CNN-multichannel
## Config
example: kaggle\_movie\_review.yml
```yml
data:
type: 'kaggle_movie_review'
base_path: 'data/'
raw_data_path: 'kaggle_movie_reviews/'
processed_path: 'kaggle_processed_data'
testset_size: 25000
num_classes: 5
PAD_ID: 0
model:
batch_size: 64
embed_type: 'rand' #(rand, static, non-static, multichannel)
pretrained_embed: ""
embed_dim: 300
num_filters: 256
filter_sizes:
- 2
- 3
- 4
- 5
dropout: 0.5
train:
learning_rate: 0.00005
train_steps: 100000
model_dir: 'logs/kaggle_movie_review'
save_checkpoints_steps: 1000
loss_hook_n_iter: 1000
check_hook_n_iter: 1000
min_eval_frequency: 1000
slack:
webhook_url: "" # after training notify you using slack-webhook
```
## Usage
Install requirements.
```pip install -r requirements.txt```
Then, prepare dataset and train it.
```
sh prepare_kaggle_movie_reviews.sh
python main.py --config kaggle_movie_review --mode train_and_evaluate
```
After training, you can try typing the sentences what you want using `predict.py`.
```python python predict.py --config rt-polarity```
Predict example
```
python predict.py --config rt-polarity
Setting max_seq_length to Config : 62
load vocab ...
Typing anything :)
> good
1
> bad
0
```
### Experiments modes
:white_check_mark: : Working
:white_medium_small_square: : Not tested yet.
- :white_check_mark: `evaluate` : Evaluate on the evaluation data.
- :white_medium_small_square: `extend_train_hooks` : Extends the hooks for training.
- :white_medium_small_square: `reset_export_strategies` : Resets the export strategies with the new_export_strategies.
- :white_medium_small_square: `run_std_server` : Starts a TensorFlow server and joins the serving thread.
- :white_medium_small_square: `test` : Tests training, evaluating and exporting the estimator for a single step.
- :white_check_mark: `train` : Fit the estimator using the training data.
- :white_check_mark: `train_and_evaluate` : Interleaves training and evaluation.
### Tensorboard
```tensorboard --logdir logs```
- Category Color
- rt-polarity (binary classification)
- kaggle_movie_review (multiclass classification)
## Reference
- [Implementing a CNN for Text Classification in TensorFlow](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/) by Denny Britz
- [Paper - Convolutional Neural Networks for Sentence Classification](http://arxiv.org/abs/1408.5882) (2014) by Y Kim
- [Paper - A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://arxiv.org/pdf/1510.03820.pdf) (2015) Y Zhang