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✂️ Neural Network Pruning Explained

An interactive 3D neural network you prune live — strip low-magnitude weights or whole neurons and watch sparsity rise while estimated accuracy holds steady, then falls off a cliff.

AI & Machine Learning3DAdvanced60 FPS

A layered 3D network where you set a pruning threshold and watch low-magnitude weights — or, in structured mode, entire neurons — fade out while a live estimate tracks how much accuracy the network would retain.

🔬 What It Demonstrates

Unstructured pruning strips individual weak connections for maximum sparsity; structured pruning removes whole neurons for a smaller, faster-to-run network. Both trade some accuracy for size once pushed past a "knee" in the curve.

🎮 How to Use

Drag the prune threshold to remove weak weights, toggle structured mode to prune whole neurons instead, resize the hidden layers, and watch sparsity and estimated accuracy update live. Regenerate weights to try a fresh random network.

💡 Did You Know?

Modern pruning pipelines can remove over 90% of a network's weights and still land within a percentage point of the original accuracy after a short fine-tuning pass — a key trick for shrinking models onto phones and edge devices.

⚙ Under the hood

An interactive 3D neural network you prune live — strip low-magnitude weights or whole neurons and watch sparsity rise while estimated accuracy holds steady, then falls off a cliff.

neural networksmachine learningpruningsparsitydeep learningartificial intelligenceThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install