Neural Network Inside Viewer with 3D Visualization

[konard]

Neural Network Inside Viewer

This PR implements a complete solution for issue #618: loading neural network topologies with weights into a Doublets database and visualizing how they look from inside.

Features

1. Neural Network to Doublets Converter

• Python-based loader (neural-network-loader.py) that:
  • Parses neural networks from JSON format
  • Converts network structure to Doublets representation
  • Creates links for networks, layers, neurons, connections, weights, and biases
  • Exports visualization-ready data

2. Interactive 3D Visualization

• Web-based viewer (neural-network-viewer.html) featuring:
  • Three.js-powered 3D graphics with smooth camera controls
  • Color-coded layers: Input (green), Hidden (blue), Output (magenta)
  • Connection visualization: Weight magnitude shown via line opacity
  • Interactive controls: Rotate, zoom, pan, adjust spacing
  • Real-time statistics: Network info, link counts, layer/neuron counts
  • Optional animation: Pulsing effect to show network activity

3. Sample Networks

Three example networks demonstrating different complexities:

• XOR Network (2-3-1): Simple logic function
• Simple Classifier (4-6-4-3): Multi-layer classifier
• Deep Network (3-8-6-4-3-2): Deep architecture with 5 hidden layers

Doublets Representation Design

The system encodes neural networks in Doublets as follows:

Network (root link)
  ├─ Layer links (connected to network root)
  │    ├─ Neuron links (within each layer)
  │    │    ├─ Bias links (neuron properties)
  │    │    └─ Connection links (to neurons in previous layer)
  │    │         └─ Weight values (connection properties)

Each component type uses semantic markers:

• NetworkType, LayerType, NeuronType
• ConnectionType, WeightType, BiasType

This allows querying patterns like:

• "Find all neurons with bias > 0"
• "Find all connections with negative weights"
• "Count total parameters in the network"

Usage

# Convert a network to Doublets
cd examples/neural-network-viewer
python3 neural-network-loader.py sample-networks/xor-network.json

# Open neural-network-viewer.html in a browser
# Click "Load Network" and select the generated *-doublets.json file

Test Results

All sample networks successfully convert and visualize:

• XOR Network: 27 total links (2 input, 3 hidden, 1 output, 7 connections)
• Simple Classifier: 93 total links (4 input, 6+4 hidden, 3 output, 52 connections)
• Deep Network: 161 total links (3 input, 8+6+4+3 hidden, 2 output, 99 connections)

Future Enhancements

The foundation supports future additions:

• ONNX format import
• PyTorch/TensorFlow model import
• Training history visualization
• Gradient flow animation
• Network diff comparison
• Interactive editing
• Export to other formats

Files Added

• examples/neural-network-viewer/README.md - Comprehensive documentation
• examples/neural-network-viewer/neural-network-loader.py - Converter script
• examples/neural-network-viewer/neural-network-viewer.html - 3D viewer
• examples/neural-network-viewer/sample-networks/*.json - Example networks

Screenshots

The viewer provides an immersive 3D experience showing the internal structure of neural networks stored as Doublets links, making it easy to understand and explore how neural networks are represented in associative memory.

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Fixes #618

Generated with Claude Code

Co-Authored-By: Claude noreply@anthropic.com

[konard]

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