deforce (Metaheuristic-optimized Multi-Layer Perceptron) is a Python library that implements variants and the traditional version of Multi-Layer Perceptron models. These include Metaheuristic-optimized MLP models (GA, PSO, WOA, TLO, DE, ...) and Gradient Descent-optimized MLP models (SGD, Adam, Adelta, Adagrad, ...). It provides a comprehensive list of optimizers for training MLP models and is also compatible with the Scikit-Learn library. With deforce, you can perform searches and hyperparameter tuning using the features provided by the Scikit-Learn library.
- Free software: GNU General Public License (GPL) V3 license
- Provided Estimator: CfnRegressor, CfnClassifier, DfoCfnRegressor, DfoCfnClassifier
- Total Metaheuristic-based MLP Regressor: > 200 Models
- Total Metaheuristic-based MLP Classifier: > 200 Models
- Total Gradient Descent-based MLP Regressor: 12 Models
- Total Gradient Descent-based MLP Classifier: 12 Models
- Supported performance metrics: >= 67 (47 regressions and 20 classifications)
- Supported objective functions (as fitness functions or loss functions): >= 67 (47 regressions and 20 classifications)
- Documentation: https://deforce.readthedocs.io
- Python versions: >= 3.8.x
- Dependencies: numpy, scipy, scikit-learn, pandas, mealpy, permetrics, torch, skorch
.. toctree:: :maxdepth: 4 :caption: Quick Start: pages/quick_start.rst
.. toctree:: :maxdepth: 4 :caption: Models API: pages/deforce.rst
.. toctree:: :maxdepth: 4 :caption: Support: pages/support.rst