univariate.py 文件源码

def __init__(self,
                 mean=0.,
                 logstd=None,
                 std=None,
                 group_ndims=0,
                 is_reparameterized=True,
                 use_path_derivative=False,
                 check_numerics=False,
                 **kwargs):
        self._mean = tf.convert_to_tensor(mean)
        warnings.warn("FoldNormal: The order of arguments logstd/std will change "
                      "to std/logstd in the coming version.", FutureWarning)
        if (logstd is None) == (std is None):
            raise ValueError("Either std or logstd should be passed but not "
                             "both of them.")
        elif logstd is None:
            self._std = tf.convert_to_tensor(std)
            dtype = assert_same_float_dtype([(self._mean, 'FoldNormal.mean'),
                                             (self._std, 'FoldNormal.std')])
            logstd = tf.log(self._std)
            if check_numerics:
                logstd = tf.check_numerics(logstd, "log(std)")
            self._logstd = logstd
        else:
            # std is None
            self._logstd = tf.convert_to_tensor(logstd)
            dtype = assert_same_float_dtype([(self._mean, 'FoldNormal.mean'),
                                             (self._logstd, 'FoldNormal.logstd')])
            std = tf.exp(self._logstd)
            if check_numerics:
                std = tf.check_numerics(std, "exp(logstd)")
            self._std = std

        try:
            tf.broadcast_static_shape(self._mean.get_shape(),
                                      self._std.get_shape())
        except ValueError:
            raise ValueError(
                "mean and std/logstd should be broadcastable to match each "
                "other. ({} vs. {})".format(
                    self._mean.get_shape(), self._std.get_shape()))
        self._check_numerics = check_numerics
        super(FoldNormal, self).__init__(
            dtype=dtype,
            param_dtype=dtype,
            is_continuous=True,
            is_reparameterized=is_reparameterized,
            use_path_derivative=use_path_derivative,
            group_ndims=group_ndims,
            **kwargs)