univariate.py 文件源码

def _sample(self, n_samples):
        logits, temperature = self.logits, self.temperature
        if not self.is_reparameterized:
            logits = tf.stop_gradient(logits)
            temperature = tf.stop_gradient(temperature)
        shape = tf.concat([[n_samples], self.batch_shape], 0)

        uniform = open_interval_standard_uniform(shape, self.dtype)
        # TODO: add Logistic distribution
        logistic = tf.log(uniform) - tf.log(1 - uniform)
        samples = tf.sigmoid((logits + logistic) / temperature)

        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