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Neural Net Learning with Most Decent Method part5

  1. Set initial value to weights and threshold(weight_input_hidden, weight_hidden_output, threshold_hidden), prepare num which is iteration number, δwhich is acceptable difference and learning rate α.
  2. Obtain below with input data x^p, p=1,・・・,P.
    y
    z
  3. Obtain gradients with teacher value d^p, p=1,・・・,P.
    weight_input_hidden
    weight_hidden_output
    weight_hidden_threshold
  4. Update parameters using gradient.
    update_weight_input_hidden
    update_weight_hidden_output
    update_theta_hidden
  5. When number of iteration reaches num or square error is lower than δ, stop calculation. If it’s not, go back to step2.

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