model.py 文件源码

def _gaussian(self, enc_output):
        def latent_loss(mu, sigma):
            pow_mu = mu * mu
            pow_sigma = sigma * sigma
            return 0.5 * torch.mean(pow_mu + pow_sigma - torch.log(pow_sigma) - 1)

        mu = self._enc_mu(enc_output)
        sigma = torch.exp(.5 * self._enc_log_sigma(enc_output))
        self.latent_loss = latent_loss(mu, sigma)

        weight = next(self.parameters()).data
        std_z = Variable(weight.new(*sigma.size()), requires_grad=False)
        std_z.data.copy_(torch.from_numpy(
                np.random.normal(size=sigma.size())))

        return mu + sigma * std_z