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Interactive web visualisation for handwritting detection using a simple neural network
| Date | Stars |
|---|---|
| 2026-07-31 | 666 |
| 2026-08-06 | 666 |
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# MNIST MLP Visualizer  Interactive web visualisation for a compact multi-layer perceptron trained on the MNIST handwritten digit dataset. Draw a digit, watch activations propagate through the network in 3D, and inspect real-time prediction probabilities. ## WIP This is still in a rough state and under active development. If you want something useable for a museum etc check back later. I have a couple of features in mind (like being able to connect a tablet to draw a number) to make this a good educational visualisation. ## Repository Layout - `index.html` / `assets/` – Static Three.js visualiser and UI assets. - `exports/mlp_weights.json` – Default weights with timeline snapshots (generated from the latest training run). - `training/mlp_train.py` – PyTorch helper to train the MLP (with Apple Metal acceleration when available) and export weights for the front-end. ## Quick Start 1. (Only for training) **Install Python dependencies** (PyTorch + torchvision): ```bash python3 -m pip install torch torchvision ``` 2. **Launch a static file server** from the repository root (any server works; this example uses Python): ```bash python3 -m http.server 8000 ``` 3. Open `http://localhost:8000` in your browser. Draw on the 28×28 grid (left-click to draw, right-click to erase) and explore the 3D network with the mouse or trackpad. ## Training & Exporting New Weights `training/mlp_train.py` trains a small MLP on MNIST and writes a JSON export the front-end consumes. Metal (MPS) is used automatically when available on Apple Silicon; otherwise the script falls back to CUDA or CPU. Typical usage: ```bash python3 training/mlp_train.py \ --epochs 5 \ --hidden-dims 128 64 \ --batch-size 256 \ --export-path exports/mlp_weights.json ``` Key options: - `--hidden-dims`: Hidden layer sizes (default `128 64`). Keep the network modest so the visualisation stays responsive. - `--epochs`: Minimum training epochs (default `5`). The script will automatically extend the run so the timeline hits the 50× dataset milestone. - `--batch-size`: Mini-batch size (default `128`). - `--device`: Force `mps`, `cuda`, or `cpu`. By default the script picks the best available backend. - `--skip-train`: Export the randomly initialised weights without running training (useful for debugging the pipeline). After training, update `VISUALIZER_CONFIG.weightUrl` in `assets/main.js` if you export to a different location/name. Refresh the browser to load the new weights. ### Training timeline export Every exported JSON now includes a `timeline` array spanning 35 checkpoints: densely spaced early snapshots (≈50, 120, 250, 500, 1k, 2k, 3.5k, 5.8k, 8.7k, 13k, 19.5k, 28.5k, 40k images), followed by dataset-multiple milestones from 1× through 50×. The JSON manifest stays small; each snapshot’s weights are stored separately as float16-encoded files under `exports/<stem>/NNN_<id>.json`, and the front-end streams them on demand so you can scrub the timeline without downloading the entire 50× run up front. Re-export the weights with the updated script to generate fresh timeline data for your own runs. ## Notes & Tips - The visualiser highlights the top-N (configurable) strongest incoming connections per neuron to keep the scene legible. - Colors encode activation sign and magnitude (cool tones for negative/low, warm tones for strong positive contributions). - The default export (`exports/mlp_weights.json`) already includes timeline milestones from a multi-epoch training run. Retrain (and re-export) if you want to showcase a different progression. - If you adjust the architecture, ensure the JSON export reflects the new layer sizes; the front-end builds the scene dynamically from that metadata. ## Deployment Keep production files under the repo-local `releases/` tree (ideal for `/srv/server/nn-vis` on the new ho
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matched fp:b70e8c3940f17002, llm:Repository description: 'Interactive web visualisation for handwritting detection using a simple neural network' (JavaScript)
matched fp:b70e8c3940f17002, llm:Repository description: 'Interactive web visualisation for handwritting detection using a simple neural network' (JavaScript)
matched fp:b70e8c3940f17002, llm:Repository description: 'Interactive web visualisation for handwritting detection using a simple neural network' (JavaScript)