activations.py 文件源码

def derivative(self, input=None):
        """The derivative of sigmoid is

        .. math:: \\frac{dy}{dx} & = (1-\\varphi(x)) \\otimes \\varphi(x)  \\\\
                  & = \\frac{e^{-x}}{(1+e^{-x})^2} \\\\
                  & = \\frac{e^x}{(1+e^x)^2}

        Returns
        -------
        float32
            The derivative of sigmoid function.
        """
        last_forward = self.forward(input) if input else self.last_forward
        return np.multiply(last_forward, 1 - last_forward)


# sigmoid-end
# tanh-start