'Distance to optimum.
Returns:
D_t ops'

def _dist_to_opt(self):

    dist_to_opt_ops = []
    self._grad_norm = tf.sqrt(self._grad_norm_squared)
    avg_op = self._moving_averager.apply([self._grad_norm])
    dist_to_opt_ops.append(avg_op)
    with tf.control_dependencies([avg_op]):
        self._grad_norm_avg = self._moving_averager.average(self._grad_norm)
        self._d_t = (self._grad_norm_avg / self._grad_norm_squared_avg)
    avg_op = self._moving_averager.apply([self._d_t])
    dist_to_opt_ops.append(avg_op)
    with tf.control_dependencies([avg_op]):
        self._dist_to_opt_avg = tf.identity(self._moving_averager.average(self._d_t))
    return dist_to_opt_ops
