monte_carlo.py 文件源码

def sgvb(self):
        """
        Implements the stochastic gradient variational bayes (SGVB) gradient
        estimator for the objective, also known as "reparameterization trick"
        or "path derivative estimator". It was first used for importance
        weighted objectives in (Burda, 2015), where it's named "IWAE".

        It only works for latent `StochasticTensor` s that can be
        reparameterized (Kingma, 2013). For example,
        :class:`~zhusuan.model.stochastic.Normal`
        and :class:`~zhusuan.model.stochastic.Concrete`.

        .. note::

            To use the :meth:`sgvb` estimator, the ``is_reparameterized``
            property of each latent `StochasticTensor` must be True (which is
            the default setting when they are constructed).

        :return: A Tensor. The surrogate cost for Tensorflow optimizers to
            minimize.
        """
        return -self.tensor