.
├── datasets/
│ ├── <dataset_name>/
│ └── ...
├── doc/
├── src/
│ ├── models/
│ │ ├── <model_name.py>
│ │ └── ...
│ ├── parsers/
│ │ ├── <parser_name.py>
│ │ └── ...
│ ├── train.py
│ └── test.py
└── var/ (not committed)
├── train/
│ └── <model_name>/
│ └── <dataset_name>/
└── wordvec/
└── <dataset_name>/
make.py [-h] [--model MODEL] [--dataset DATASET] [--parser PARSER] [--log-level LOG_LEVEL] TASK
TASK
: Task to perform{preprocess, train, test}
--model MODEL
: Model to use (src/models/MODEL.py
)--dataset DATASET
: Dataset to use (dataset/DATASET/
)--parser PARSER
: Data parser to use (src/parsers/PARSER.py
)--log-level LOG_LEVEL
: Logging level (10 for testing, and 40 for production. Default: 30)
preprocess.py
:- Learns word vectors, and saves them to
var/wordvec/...
- Learns word vectors, and saves them to
train.py
:- Trains the model, and saves weights to
var/train/...
- Logs training reports to
var/log/train/...
- Trains the model, and saves weights to
test.py
:- Loads weights from
var/train/...
, and predicts the labels for test data - Logs training reports to
var/log/test/...
- If labels are known (validation), then scores are reported.
- Loads weights from
models/MODEL.py
class Model
implements the CNN.
parsers/PARSER.py
- Implements classes to lazy-load data, and for generating word vectors using the trained word vector vocab.
Check Readme in src/models/
Adding a parser for new datasets - to handle preprocessing and conversion to word vectors.
Check Readme in src/parsers/
Add datasets to the datasets
folder. Parsers should know the corresponding internal directory structure.
Team: \sigma \sqrt -1
- Arjun P
- Gokul B Nair
- Soumya Vadlamannati
- Sai Anurudh Reddy Peduri