IEF-the idea of iterative error feedback

IEF (Iterative Error Feedback): Iterative feedback
Objective: To make more accurate prediction of parameters
research ideas:
(1) in an iterative manner to update the parameters you want to forecast
(2) the output of this error is the error ----- The shape variable of the parameter
(3) after multiple iterations is the parameter that needs to be predicted

        for i in np.arange(self.num_stage):
            print('Iteration %d' % i)
           # theta_prev 表示的是需要预测的参数
            state = tf.concat([self.img_feat, theta_prev], 1)

            if i == 0:
                # delta_theta 表示的是预测参数的形变量
                delta_theta, threeD_var = threed_enc_fn(
                    state,
                    num_output=self.total_params,
                    reuse=False)
            else:
                delta_theta, _ = threed_enc_fn(
                    state, num_output=self.total_params, reuse=True)
			"""
			这里面忽略了你想要对于预测参数的操作
			"""
            # (形成迭代误差反馈了) 通过误差来更新需要预测的参数
            theta_here = theta_prev + delta_theta
           
            # Finally update to end iteration.---> theta_here update the theta
            # 迭代误差反馈的入口
            theta_prev = theta_here

The overall idea is to update the desired parameters through errors-through iterative methods-
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