Multi-Layer Neural Network
Adjust architecture, weights, biases, and training
Network Architecture
Input Size:
i
Hidden Layers:
i
Output Size:
i
Rebuild Network
Input Values
Forward Pass
Weight & Bias Controls
Randomize
Zero
Reset
Training
Learning Rate:
i
Training Speed:
i
Target Function:
i
Visualize
Sample Points:
i
Generate Training Data
Import Training Data
Add Data Point
Clear Data
Train 1 Step
Train 100 Steps
▶ Play
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Save/Load
Save Network
Load Network
3D Plot Settings
Normal Tolerance:
i
Surface Detail:
i
Click "Forward Pass" to see network output
Binary State Vectors
Plot Type:
2D Plot
Polyhedral Decomposition
Resolution:
70
Reset View
Colors show binary activation patterns. Drag to pan, scroll to zoom.
Dual Graph
Graph of adjacent regions
JSON
Python
Export Dual Graph
Training Loss History
Clear History