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mAtlAB梯度下降法

function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters) %GRADIENTDESCENT Performs gradient descent to learn theta % theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by % taking num_iters ...

你for循环里怎么没有m出现? 应该是 p= theta(1) - (alpha / m) * sum((X * theta - y).* X(:,1)); q= theta(2) - (alpha / m) * sum((X * theta - y).* X(:,2));

Rosenbrock函数 实现代码: clc,clear all format long g x0=[0;0]; fun=@func; gfun=@gfunc; [x,val,k]=grad(fun,gfun,x0) %最速下降法(梯度法) 目标函数 function f=func(x) f=100*(x(1)^2-x(2))^2+(1-x(1))^2; end 梯度函数 function g=gfu...

function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters) %GRADIENTDESCENT Performs gradient descent to learn theta % theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by % taking num_iters ...

修改如下: o=[10;10]; x=[1,1;1,2;1,3;1,4] y=[2.5;3.5;3;4] t=[1;1]; while max(abs(t))>1e-10; for j=1:2; t(j)=x(:,j)'*0.001*(x*o-y); o(j,1)=o(j,1)-t(j); end end

function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters) %GRADIENTDESCENT Performs gradient descent to learn theta % theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by % taking num_iters ...

你for循环里怎么没有m出现? 应该是 p= theta(1) - (alpha / m) * sum((X * theta - y).* X(:,1)); q= theta(2) - (alpha / m) * sum((X * theta - y).* X(:,2));

额。。。 一种启发式的改进就是,为学习速率选用自适应值,它依赖于连续迭代步骤中的误差函数值。 自适应调整学习速率的梯度下降算法,在训练的过程中,力图使算法稳定,同时又使学习的步长尽量地大,学习速率则是根据局部误差曲面作出相应的调整。...

因为Matlab是以1作为起始单元,且以列为主,在Matlab中使用四维blob为[width, height, channels, num],且width是最快的维度,而且要在BGR通道。而且Caffe使用单精度浮点型数据。如果你的数据不是浮点型的,set_data将会自动转换为single。

最简单的是。见官方帮助 Backpropagation Algorithm There are many variations of the backpropagation algorithm, several of which are described in this chapter. The simplest implementation of backpropagation learning updates the ne...

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