helmholtz.py 文件源码

def log_marginal(self, y, h, py, q):
        '''Computes the approximate log marginal.

        Uses \log \sum p / q - \log N

        Args:
            y: T.tensor, target values.
            h: T.tensor, latent samples.
            py: T.tesnor, conditional density p(y | h)
            q: approximate posterior q(h | y)
        Returns:
            approximate log marginal.
        '''
        log_py_h = -self.conditional.neg_log_prob(y, py)
        log_ph   = -self.prior.neg_log_prob(h)
        log_qh   = -self.posterior.neg_log_prob(h, q)
        assert log_py_h.ndim == log_ph.ndim == log_qh.ndim

        log_p     = log_py_h + log_ph - log_qh
        log_p_max = T.max(log_p, axis=0, keepdims=True)
        w         = T.exp(log_p - log_p_max)

        return (T.log(w.mean(axis=0, keepdims=True)) + log_p_max).mean()