v0.3.0 — Make weights visible

Weights are now part of the picture.

New

• Weight beams. Connections draw as beams. Thickness and color follow the weight magnitude. Wiring is truthful: pass-through ops (ReLU, Pool, Flatten) map neuron i to neuron i. Weighted layers (Conv, Linear) read every input, drawn as sampled bipartite fans.
• Per-neuron detail. Neurons glow by their incoming |w|. Click a neuron for its index, |w|, and rank. Click a layer for its weight histogram and stats.
• Selection chain. Click a neuron or a connection to highlight it. Shift+click builds a chain across layers. Esc clears. Camera drags never change the selection.
• Layouts by layer type. Conv layers draw as feature-map slabs. Dense layers draw as neuron walls.
• Collapse and expand. Double-click a layer to fold it into one marker.
• Themes. Dark and light, one click away.
• Server fixes. One thread per request: a slow parse no longer blocks other drops. nnviz serve also serves your generated HTML files. --port works. Ctrl+C stops clean.

Install

pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install onnx            # only if you use ONNX files
pip install -e .
nnviz model.pth             # writes model.html. Open it in a browser.
nnviz serve                 # drag & drop server at localhost:8000

Known limits

• Beam fans cap at 3000 per connection. Big walls show a sample. Level of detail arrives in v0.8.
• Transposed MatMul weights may swap in and out feature counts.
• safetensors files are detected but not parsed yet (v0.5).