Kinho
Kinho’s Neural repository provides an API for deep learning and feedforward neural network models.
About Kinho
Kinho's Neural is a Python API for creating feedforward neural networks. The Neural class, imported from the Kinho package, supports defining layer sizes, loading a pre-trained .brain file, running inference with send(), training with learn() using backpropagation, and exporting models.
It requires Python below 3.12 and requires either sizes or brain_path to initialize. It also provides settings for learning rate, GPU use, mini-batch size, and multilabel output.
Key features
- Define feedforward network by layer sizes in constructor
- Load pre-trained model from .brain file path
- Run inference with send() returning per-label probabilities
- Train with learn() using backpropagation to update weights
- Export trained model to file with filename and path
- Configure learning rate, GPU use, mini-batch size, multilabel
Best for: Python developers who want to define, train, and run feedforward neural networks in code.
Good for
- Installs with pip install Kinho
- Combines model definition, training, inference, and export in one class
- Supports pre-trained .brain files with GPU and mini-batch settings
Watch out for
- Requires Python version below 3.12
- Initialization fails with type error without sizes or brain_path
- If both sizes and brain_path are passed, sizes is ignored
Kinho at a glance
- Website
- github.com/kinhosz/Neural
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Kinho FAQ
How do I install Kinho?
Use pip install Kinho with Python version below 3.12.
What is required to create a Neural network?
You must pass either sizes or brain_path, otherwise a type error is generated. If both are passed, brain_path takes priority.
What does send() return?
A list with the same size as the output layer, with a probability for each label; the highest probability is the prediction.
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