16 lines
623 B
TeX
16 lines
623 B
TeX
The activation function used for each neuron in the hidden and output layers is the \textit{sigmoid}, defined by the \texttt{sigmoid} function:
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\begin{verbatim}
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double sigmoid(double x) {
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if (x > 20) return 1.0;
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if (x < -20) return 0.0;
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double z = exp(-x);
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return 1.0 / (1.0 + z);
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}
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\end{verbatim}
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This function is bounded between 0 and 1, allowing for normalization of the activation values for each neuron. The derivative of the sigmoid, \texttt{sigmoid\_derivative}, is used in backpropagation to compute gradients:
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\begin{verbatim}
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double sigmoid_derivative(double x) {
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return x * (1.0 - x);
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}
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\end{verbatim}
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