DL之DNN优化技术：DNN优化器的参数优化—更新参数的四种最优化方法(SGD/Momentum/AdaGrad/Adam)的案例理解、图表可视化比较

DL之DNN优化技术：DNN优化器的参数优化—更新参数的四种最优化方法(SGD/Momentum/AdaGrad/Adam)的案例理解、图表可视化比较

四种最优化方法简介

DL之DNN优化技术：神经网络算法简介之GD/SGD算法(BP算法)的简介、理解、代码实现、SGD缺点及改进(Momentum/NAG/Ada系列/RMSProp)之详细攻略

优化器案例理解

输出结果

设计思路

核心代码

#T1、SGD算法
class SGD:
'……'
    def update(self, params, grads):
        for key in params.keys():
            params[key] -= self.lr * grads[key] 

#T2、Momentum算法
import numpy as np
class Momentum:
'……' 

    def update(self, params, grads):
        if self.v is None:
            self.v = {}
            for key, val in params.items():
                self.v[key] = np.zeros_like(val)
        for key in params.keys():
            self.v[key] = self.momentum*self.v[key] - self.lr*grads[key]
            params[key] += self.v[key]

#T3、AdaGrad算法
'……'

    def update(self, params, grads):
        if self.h is None:
            self.h = {}
            for key, val in params.items():
                self.h[key] = np.zeros_like(val)
        for key in params.keys():
            self.h[key] += grads[key] * grads[key]
            params[key] -= self.lr * grads[key] / (np.sqrt(self.h[key]) + 1e-7)  

#T4、Adam算法
'……'

    def update(self, params, grads):
        if self.m is None:
            self.m, self.v = {}, {}
            for key, val in params.items():
                self.m[key] = np.zeros_like(val)
                self.v[key] = np.zeros_like(val)
        self.iter += 1
        lr_t  = self.lr * np.sqrt(1.0 - self.beta2**self.iter) / (1.0 - self.beta1**self.iter)        

        for key in params.keys():
            self.m[key] += (1 - self.beta1) * (grads[key] - self.m[key])
            self.v[key] += (1 - self.beta2) * (grads[key]**2 - self.v[key])

            params[key] -= lr_t * self.m[key] / (np.sqrt(self.v[key]) + 1e-7)

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