Parameters:
0
Activation Sum:
0.00
Gradient Flow:
0.00
Constraints:
0
Satisfaction:
0.00
Violations:
0
Input Controls
Training
Training Loss Comparison
Traditional NN
Constraint NN
Traditional Neural Network
- Neurons: Weighted sum + activation function
- Learning: Gradient descent on loss function
- Forward Pass: y = σ(Wx + b)
- Backprop: Chain rule for gradients
- Parameters: Weights + biases per connection
Constraint Theory Network
- Neurons: Geometric constraint satisfaction points
- Learning: Constraint optimization
- Forward Pass: Pythagorean snapping to constraints
- Backprop: Constraint violation propagation
- Parameters: Geometric constraints (distances, angles)
Key Differences
- Activation: σ(x) vs geometric snapping
- Weights: Matrix vs constraint edges
- Optimization: Gradient descent vs constraint satisfaction
- Interpretability: Black box vs geometric intuition
- Robustness: Sensitive vs geometric invariants