activations.py 文件源码

def forward(self, input):
        """This function is easily defined as the ratio between the hyperbolic 
        sine and the cosine functions (or expanded, as the ratio of the 
        half?difference and half?sum of two exponential functions in the 
        points :math:`z` and :math:`-z`):

        .. math:: tanh(z) & = \\frac{sinh(z)}{cosh(z)} \\\\
                  & = \\frac{e^z - e^{-z}}{e^z + e^{-z}}

        Fortunately, numpy provides :meth:`tanh` methods. So in our implementation,
        we directly use :math:`\\varphi(x) = \\tanh(x)`.

        Parameters
        ----------
        x : float32
            The activation (the summed, weighted input of a neuron).

        Returns
        -------
        float32 in [-1, 1]
            The output of the tanh function applied to the activation.
        """
        self.last_forward = np.tanh(input)
        return self.last_forward