DongjunLee / Text Cnn Tensorflow
Convolutional Neural Networks for Sentence Classification(TextCNN) implements by TensorFlow
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text-cnn
This code implements Convolutional Neural Networks for Sentence Classification models.
- Figure 1: Illustration of a CNN architecture for sentence classification
Requirements
- Python 3.6
- TensorFlow 1.4
- hb-config (Singleton Config)
- tqdm
- requests
- Slack Incoming Webhook URL
Project Structure
init Project by 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, Dataset, experiments_fn, EstimatorSpec
- Dataset : rt-polarity, Sentiment Analysis on Movie Reviews
Todo
- apply embed_type
- CNN-rand
- CNN-static
- CNN-nonstatic
- CNN-multichannel
Config
example: kaggle_movie_review.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
✅ : Working
◽️ : Not tested yet.
- ✅
evaluate
: Evaluate on the evaluation data. - ◽️
extend_train_hooks
: Extends the hooks for training. - ◽️
reset_export_strategies
: Resets the export strategies with the new_export_strategies. - ◽️
run_std_server
: Starts a TensorFlow server and joins the serving thread. - ◽️
test
: Tests training, evaluating and exporting the estimator for a single step. - ✅
train
: Fit the estimator using the training data. - ✅
train_and_evaluate
: Interleaves training and evaluation.
Tensorboard
tensorboard --logdir logs
- Category Color
- rt-polarity (binary classification)
- kaggle_movie_review (multiclass classification)
Reference
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