hmc.py 文件源码

def tune(self, acceptance_rate, fresh_start):
        def adapt_stepsize():
            new_step = tf.assign(self.step, (1 - fresh_start) * self.step + 1)
            rate1 = tf.div(1.0, new_step + self.t0)
            new_h_bar = tf.assign(
                self.h_bar, (1 - fresh_start) * (1 - rate1) * self.h_bar +
                rate1 * (self.delta - acceptance_rate))
            log_epsilon = self.mu - tf.sqrt(new_step) / self.gamma * new_h_bar
            rate = tf.pow(new_step, -self.kappa)
            new_log_epsilon_bar = tf.assign(
                self.log_epsilon_bar,
                rate * log_epsilon + (1 - fresh_start) * (1 - rate) *
                self.log_epsilon_bar)
            with tf.control_dependencies([new_log_epsilon_bar]):
                new_log_epsilon = tf.identity(log_epsilon)

            return tf.exp(new_log_epsilon)

        c = tf.cond(self.adapt_step_size,
                    adapt_stepsize,
                    lambda: tf.exp(self.log_epsilon_bar))

        return c