Welcome to MetaPerceptron’s documentation!¶
MetaPerceptron (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 MetaPerceptron, 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: MlpRegressor, MlpClassifier, MhaMlpRegressor, MhaMlpClassifier
Provided Utility: MhaMlpTuner and MhaMlpComparator
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 utility functions: GPU for Gradient-based models, Scikit-learn compatibility, and more
Documentation: https://metaperceptron.readthedocs.io
Python versions: >= 3.8.x
Dependencies: numpy, scipy, scikit-learn, pytorch, mealpy, pandas, permetrics.
Quick Start:
Models API:
- MetaPerceptron Package
- metaperceptron.core package
- metaperceptron.helpers package
Support: