blinear

Bilinear Neural Network Library for Deep Learning and Biomedical Signal Classification.

blinear provides custom bilinear neural network layers and hybrid CNN–bilinear architectures built with TensorFlow/Keras. The library focuses on nonlinear feature interaction modeling using Hirota-inspired bilinear operators for signal processing, sequence learning, and classification tasks.

Features

• Hirota Bilinear Layer
• Convolutional Hirota Bilinear Layer
• OGD-based Bilinear Classification Layer
• CNN + Bilinear Hybrid Models
• Double Bilinear Architectures
• Binary and Multiclass Classification Support
• TensorFlow / Keras Compatible

Installation

pip install blinear

1. Hirota CNN Model

CNN feature extraction followed by a Hirota bilinear interaction block.

Usage

from blinear import BilinearLayer

2. ConvHirota Bilinear Model

End-to-end convolutional bilinear feature extraction using ConvHirotaBilinear.

Usage

from blinear import ConvBilinear

3. Online Gradient Descent (OGD) Layer Guide

The OGDLayer in blinear is a deep bilinear representation layer designed for multiclass classification tasks.

from blinear import OGDLayer

Requirements

• Python >= 3.9
• TensorFlow >= 2.x
• NumPy

License

MIT License

Author

Developed for nonlinear bilinear deep learning research and biomedical AI applications.