activations.py 文件源码

def derivative(self, input=None):
        """The derivative of :meth:`tanh` functions is

        .. math:: \\frac{d}{dx} tanh(x) & = \\frac{d}{dx} \\frac{sinh(x)}{cosh(x)} \\\\
                  & = \\frac{cosh(x) \\frac{d}{dx}sinh(x) - sinh(x) \\frac{d}{dx}cosh(x) }{ cosh^2(x)} \\\\
                  & = \\frac{ cosh(x) cosh(x) - sinh(x) sinh(x) }{ cosh^2(x)}  \\\\
                  & = 1 - tanh^2(x) 

        Returns
        -------
        float32 
            The derivative of tanh function.
        """
        last_forward = self.forward(input) if input else self.last_forward
        return 1 - np.power(last_forward, 2)


# tanh-end
# relu-start