Stochaster: A Neural Network Framework

Introduction

Stochaster is a versatile and user-friendly artificial neural network framework. Designed with clarity, conciseness, and readability in mind, it aims to simplify the process of creating and understanding neural networks.

Features

• Autograd Engine: Implements reverse-mode autodifferentiation for efficient backpropagation.

  • Customizable tensors via the Tensor class.
  • An autodiff wrapper for custom functions through the Function class.

• Neural Network Library: A brief yet powerful collection of tools for building neural networks.

  • Supports common layer types and optimizers.
  • Currently provides MNIST-level capabilities.

Paradigms

• Code Readability: The engine's source code prioritizes the Tensor and Function abstractions to ensure that anyone familiar with the mathematics of backpropagation can easily understand and work with the framework.
• API Design: The API for both the engine and neural network library is designed to be like PyTorch.

Stochaster Org. also provides secondary-level educational Jupyter Notebooks on ANNs in Notebooks covering from top-level APIs (e.g. PyTorch) to linear algebra/calculus present in basic ANNs.

Installation/Build

The only option for using Stochaster is building from source for now. PyPi package (via pip) will be available after succesful tests

Example Usage

• Engine Example: Section to be completed.
• Neural Network Examples: Check out the examples folder for practical implementations of Stochaster's NN and Engine libraries. Currently, it features an MNIST classifier implemented using cross-entropy loss and SGD for optimization.

Tracing and Visualization

• Upcoming Features: Tracing for backpropagation and its visualization to enhance understanding and debugging capabilities.

Running Tests

To run the unit tests you will have to install PyTorch. Then simply:

python -m pytest

License

Stochaster is made available under the MIT License, promoting open and permissive software use and redistribution.