- This is a multi-class text classification (sentence classification) problem.
- The goal of this project is to classify Kaggle San Francisco Crime Description into 39 classes.
- This model was built with CNN, RNN (LSTM and GRU) and Word Embeddings on Tensorflow.
-
Input: Descript
-
Output: Category
-
Examples:
Descript Category GRAND THEFT FROM LOCKED AUTO LARCENY/THEFT POSSESSION OF NARCOTICS PARAPHERNALIA DRUG/NARCOTIC AIDED CASE, MENTAL DISTURBED NON-CRIMINAL AGGRAVATED ASSAULT WITH BODILY FORCE ASSAULT ATTEMPTED ROBBERY ON THE STREET WITH A GUN ROBBERY
- Command: python3 train.py train_data.file train_parameters.json
- Example:
python3 train.py ./data/train.csv.zip ./training_config.json
- Command: python3 predict.py ./trained_results_dir/ new_data.csv
- Example:
python3 predict.py ./trained_results_1478563595/ ./data/small_samples.csv
- Command: python3 server.py ./trained_results_dir
- Open
http://127.0.0.1:5000/ (Press CTRL+C to quit)
- Implement a cnn for text classification in tensorflow
- See .staging/notes/REFERENCES.md
======================
This is Docker Compose example. You can boot a container based on this
repository image with Jupyter port 8888, its password 'foobar' and sharing
files in notebook
directory with the container by just running
docker-compose up
To stop it, please press 'Ctrl-C'.