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You are training a two‑layer neural network (2 inputs → 2 hidden neurons → 1 o...

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You are training a two‑layer neural network (2 inputs → 2 hidden neurons → 1 output neuron) to learn the XOR gate using sigmoid activation and Gradient Descent.

Tasks:

  1. Forward Pass

    • Compute hidden layer pre‑activations

      zh

      and activations

      h

      .

    • Compute output pre‑activation

      zo

      and prediction

      y^

      .

    • Round each value to two decimals.

  2. Error

    • Compute loss:

E=12(yy^)2

  • Round to two decimals.

  1. Backward Pass (Chain Rule)

    • Compute output delta:

δo=(yy^)y^(1y^)

  • Compute hidden deltas:

δh=δoWoh(1h)

  • Round all deltas to two decimals.

  1. Weight Updates

    • Update output weights and bias:

WoWo+ηδoh

bobo+ηδo

  • Update hidden weights and biases:

WhWh+ηδhX

bhbh+ηδh

  • Round all updated parameters to two decimals.

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