mpezeshki/pytorch_forward_forward

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1500 stars · 144 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of Geoffrey Hinton's forward-forward (FF) training algorithm, an alternative to back-propagation that computes gradients locally without backpropagating errors. It trains layer-by-layer with a local objective on positive and negative samples, demonstrated on MNIST.

Use cases

• implement the forward-forward algorithm in pytorch
• train neural networks without backpropagation
• experiment with local learning objectives
• reproduce Hinton's forward-forward MNIST results
• study alternatives to backpropagation
• learn layer-wise training with positive and negative samples

When to choose

• you want a reference implementation of the forward-forward algorithm
• you are researching backprop-free or local learning methods
• you want a small, readable PyTorch codebase to extend for FF experiments

When to avoid

• you need state-of-the-art accuracy on large-scale vision or NLP tasks
• you need a maintained, production-ready training framework
• you need GPU-optimized, scalable training beyond MNIST-scale demos

Facets

library · maturity maintenance

machine-learning deep-learning machine-learning deep-learning python forward-forward-algorithm pytorch neural-networks research-code mnist hinton algorithms

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