Instructions:
• Set input values and target output
• Adjust learning rate to control step size
• Click "Step Optimization" to update weights
• Hover over table entries to highlight connections
• Watch loss decrease over time
Network: 2 inputs → 3 hidden (ReLU) → 1 output (linear)
Learning: MSE loss minimized via gradient descent
Input
1.00
0.50
Hidden
0.00
0.00
0.00
Output
0.00
Network Parameters
Weights
Input → Hidden Weights
From I1
From I2
Hidden → Output
→ H1:
-1.049
→ H2:
-0.618
→ H3:
-0.647
→ H1:
0.648
→ H2:
0.834
→ H3:
-0.395
H1 →:
0.248
H2 →:
0.625
H3 →:
-0.595
Gradients
Input → Hidden Gradients
From I1
From I2
Hidden → Output
→ H1:
0.000
→ H2:
0.000
→ H3:
0.000
→ H1:
0.000
→ H2:
0.000
→ H3:
0.000
H1 →:
0.000
H2 →:
0.000
H3 →:
0.000