This software is free for non-commercial use under a CC BY-NC 4.0 license. For commercial licensing inquiries, please contact Michael Stiber.

Mini-Torch

A Python framework designed for computer science students building neural networks from scratch.

This repository contains and documents the architecture for a "Mini-Torch" framework, designed for computer science students building neural networks from scratch. It mirrors the PyTorch API using only numpy, matplotlib, select elements of scipy, and standard Python, emphasizing manual gradient calculations and batch-first row-vector notation.

Package Usage

After installing the package (for example with pip install -e .), you can import core components using the mini_torch package namespace:

from mini_torch import Module, Dataset
from mini_torch import DataLoader, Loss, Optimizer, Activation

# or import a submodule directly:
import mini_torch.Module

To improve encapsulation and modularity, this framework incorporates the following major architectural elements: an Optimizer base class (parameter updating), a Loss base class (error and initial gradient calculation), a Dataset/DataLoader pipeline (base classes to structure and iterate through data), a Module base class (layers, forward and backward passes), and a Sequential container (Module subclass that manages chaining of multiple layers).

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This work has been partially generated with use of a language model (AI), and the author has read through and tested the resulting content to ensure it accurately reflects the original intent. All errors are the responsibility of the author.