Matlab中常见的神经网络训练函数和学习函数
一、训练函数
1、traingd
Name:Gradient descent backpropagation (梯度下降反向传播算法 )
Description:triangd is a network training function that updates weight and bias values according to gradient descent.
2、traingda
Name:Gradient descent with adaptive learning rate backpropagation(自适应学习率的t梯度下降反向传播算法)
Description:triangd is a network training function that updates weight and bias values according to gradient descent with adaptive learning rate. it will return a trained net (net) and the trianing record (tr).
3、traingdx (newelm函数默认的训练函数)
name:Gradient descent with momentum and adaptive learning rate backpropagation(带动量的梯度下降的自适应学习率的反向传播算法)
Description:triangdx is a network training function that updates weight and bias values according to gradient descent momentum and an adaptive learning rate.it will return a trained net (net) and the trianing record (tr).
4、trainlm
Name:Levenberg-Marquardt backpropagation (L-M反向传播算法)
Description:triangd is a network training function that updates weight and bias values according toLevenberg-Marquardt optimization. it will return a trained net (net) and the trianing record (tr).
注:更多的训练算法请用matlab的help命令查看。
二、学习函数
1、learngd
Name:Gradient descent weight and bias learning function (梯度下降的权值和阈值学习函数)
Description:learngd is the gradient descent weight and bias learning function, it will return the weight change dW and a new learning state.
2、learngdm
Name:Gradient descent with momentum weight and bias learning function (带动量的梯度下降的权值和阈值学习函数)
Description:learngd is the gradient descent with momentum weight and bias learning function, it will return the weight change dW and a new learning state.
注:更多的学习函数用matlab的help命令查看。
三、训练函数与学习函数的区别
学习函数的输出是权值和阈值的增量,训练函数的输出是训练好的网络和训练记录,在训练过程中训练函数不断调用学习函数修正权值和阈值,通过检测设定的训练步数或性能函数计算出的误差小于设定误差,来结束训练。
或者这么说:训练函数是全局调整权值和阈值,考虑的是整体误差的最小。学习函数是局部调整权值和阈值,考虑的是单个神经元误差的最小[1]。 参考链接:【1】 https://zhidao.baidu.com/question/1883990061249711708.html?fr=iks&word=matlab%D6%D0traingdx%BA%CDlearngdm%B5%C4%C7%F8%B1%F0&ie=gbk
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