univariate.py 文件源码

def _sample(self, n_samples):
        # samples must be sampled from (-1, 1) rather than [-1, 1)
        loc, scale = self.loc, self.scale
        if not self.is_reparameterized:
            loc = tf.stop_gradient(loc)
            scale = tf.stop_gradient(scale)
        shape = tf.concat([[n_samples], self.batch_shape], 0)
        uniform_samples = tf.random_uniform(
            shape=shape,
            minval=np.nextafter(self.dtype.as_numpy_dtype(-1.),
                                self.dtype.as_numpy_dtype(0.)),
            maxval=1.,
            dtype=self.dtype)
        samples = loc - scale * tf.sign(uniform_samples) * \
            tf.log1p(-tf.abs(uniform_samples))
        static_n_samples = n_samples if isinstance(n_samples, int) else None
        samples.set_shape(
            tf.TensorShape([static_n_samples]).concatenate(
                self.get_batch_shape()))
        return samples