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High School Lab

Neural Net Internals Explorer

Inspect every weight and activation of a small network — then train it with real gradient descent.

Activation Function

σ(z) = 1 / (1 + e⁻ᶻ) — smooth, output in (0, 1), derivative = a(1−a)

Input Values

0.000
1.000

Input → Hidden Weights (W)

-0.525
0.138
-0.648
-0.437
-0.369
-0.575

Hidden Biases (b_h)

0.085
0.216
0.073

Hidden → Output Weights (v)

-0.640
-0.394
-0.246

Output Bias (b_o)

-0.045

Network Diagram

Line thickness = |weight|; cyan = positive, red = negative. Circle fill = activation level.

Live Activations

Inputs: x₁ = 0.000, x₂ = 1.000

Hidden Layer

h1: z = -0.3516a = 0.4130
h2: z = -0.1536a = 0.4617
h3: z = -0.5021a = 0.3770

Output

z_o = -0.5837ŷ = 0.3581

The Math (Forward Pass)

For each hidden neuron j (1..3):

z_j = w[x1→hj]·x1 + w[x2→hj]·x2 + b_hj

a_j = act(z_j)

Output:

z_o = v1·a1 + v2·a2 + v3·a3 + b_o

ŷ = act(z_o)