lasagne_layers.py 文件源码

def get_output_for(self, input, **kwargs):
        """
        Given 2d input find the probability of each input in each of num_units
        Diagonal Gaussians using the formula from http://mathworld.wolfram.com/BivariateNormalDistribution.html
        """
        #make sure sigma is positive and nonzero softplus(x) (0, +inf)
        sigmas = T.nnet.softplus(self.sigmas)
        sigmainvs = 1.0 / sigmas
        sigmainvprods = sigmainvs[:, 0] * sigmainvs[:, 1]
        sigmas2 = sigmas ** 2
        mus = self.mus[np.newaxis, :, :]
        X = input[:, np.newaxis, :]
        diff = (X - mus) ** 2
        diffsigma = diff / sigmas2
        diffsigmanorm = T.sum(diffsigma, axis=-1)
        expterm = T.exp(-0.5 * diffsigmanorm)
        probs = (0.5 / np.pi) * sigmainvprods * expterm
        return probs