Deconstructing XOR: How Neural Networks Warp Space

2-2-1 Architecture Interactive Educational Laboratory

Epoch:0
Loss (MSE):0.000
Accuracy:0%

1. Input Space & Boundary (x₁, x₂)

Class 0 (0.0) Class 1 (1.0)

Linear perceptrons fail here because no single straight line can separate diagonal classes.

2. Network Synapses & Activations 2 → 2 → 1

■ Positive Weight (+) ■ Negative Weight (−)

3. Hidden Feature Space (h₁, h₂)

The hidden layer folds coordinate space, allowing the output neuron to cut them with a single line.