univariate.py 文件源码

def __init__(self,
                 alpha,
                 beta,
                 group_ndims=0,
                 check_numerics=False,
                 **kwargs):
        self._alpha = tf.convert_to_tensor(alpha)
        self._beta = tf.convert_to_tensor(beta)
        dtype = assert_same_float_dtype(
            [(self._alpha, 'InverseGamma.alpha'),
             (self._beta, 'InverseGamma.beta')])

        try:
            tf.broadcast_static_shape(self._alpha.get_shape(),
                                      self._beta.get_shape())
        except ValueError:
            raise ValueError(
                "alpha and beta should be broadcastable to match each "
                "other. ({} vs. {})".format(
                    self._alpha.get_shape(), self._beta.get_shape()))
        self._check_numerics = check_numerics
        super(InverseGamma, self).__init__(
            dtype=dtype,
            param_dtype=dtype,
            is_continuous=True,
            is_reparameterized=False,
            group_ndims=group_ndims,
            **kwargs)