Automated feed-forward artificial neural network
Features:
- formats (atm): csv
- different methods for parsing dataset: url or file (multipart)
- model training (clasification or regression)
- results for training or testing set
- statistical indicators
- correlation matrix (image)
- feature (input) encoding and scaling
- custom architecture (layers, activation functions, number of nodes, weight initializers)
- hyperparameters configuration (algorithm, optimizer, learning rate)
- different metrics
- fill NaNs
- mean
- median
- most frequent
- fill with constant (number or string)
- modify dataset
- real-time reporting using sockets
- Download repo
git clone https://github.com/TodorovicSrdjan/AnnUTO.git
- Set current working directory to downloaded folder (
AnnUTO
) - (Optional) Create virtual environment and activate it
python -m venv path/to/some/folder
// linux; bash
source path/to/some/folder/bin/activate
- Install project dependencies
pip install -r requirements.txt
- Run app
python src/ann_server.py
You can see API specification at: localhost:10003/docs
API (atm) does not have it's own database. After parsing dataset (url or file) API returns dataset in internal json format.
Other endpoints can only work with that returned dataset. It's provided as url for
stored_dataset
parameter.
You can configure some things like urls and ports in file config.py