PyTensorForge

PyTensorForge is a lightweight Python machine learning framework designed for learning, experimentation, and rapid prototyping. It provides a modular core for tensor operations, neural network layers, activation functions, optimizers, loss metrics, and model definitions.

Key Features

• Core tensor and scalar abstractions for numerical computation
• Layer-based neural network support with dense and sequential model building
• Common activation functions such as ReLU, Sigmoid, Tanh, ELU, and SELU
• Standard optimization algorithms with SGD and batch optimization
• Loss functions including mean squared error, mean absolute error, and binary cross-entropy
• Weight initialization utilities with Glorot, He, LeCun, and zeros initializers
• Model implementations for regression and logistic classification

Project Structure

• src/core/ – Tensor, scalar, matrix, and vector primitives
• src/neural/ – Neural layer abstractions, parameters, and dense layers
• src/activations/ – Activation function implementations
• src/optimizer/ – Optimizer base classes and SGD implementation
• src/loss/ – Loss function abstractions and implementations
• src/initializers/ – Weight initializer utilities
• src/models/ – High-level model APIs for regression, classification, and sequential models
• src/scaling/ – Data scaling utilities
• predict/ – Prediction utilities and example datasets
• test/ – Unit tests and example usage scripts

Installation

1. Clone the repository:

   git clone https://github.com/<your-username>/PyTensorForge.git
   cd PyTensorForge

2. Install dependencies (if any):

   pip install -r requirements.txt

Note: This repository is intended for educational use and may require additional dependencies depending on the development environment.

Usage

Import components from the package and build models using the provided modules. Example:

from src.neural.Dense import Dense
from src.neural.Layer import Layer
from src.optimizers.SGD import SGD
from src.loss.MSE import MSE

Refer to the test/ directory for example scripts and usage patterns.

Contribution

Contributions are welcome. Please open an issue or submit a pull request for feature requests, bug fixes, or documentation improvements.

License

This project is provided under the terms of the repository license. Check the project root for license details.