Question

我参加机器学习课程并尝试在matlab中实现梯度下降算法。函数computeCost工作正常，因为我已单独测试它。我用它来查看每次迭代的成本，而且它似乎并没有减少。它只是随机波动。 alpha的值被赋予0.01，所以我知道它不是学习率太大的问题。我得到的theta的答案与预期的输出非常不同。我哪里错了？提前谢谢！

function theta = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta

% Initialize some useful values
m = length(y); % number of training examples

temp1=0;
temp2=0;
for iter = 1:num_iters
for k = 1:m
    temp1 = temp1 + (theta(1) + theta(2)*X(k, 2) - y(k));
    temp2 = temp2 + ((theta(1) + theta(2)*X(k, 2) - y(k))*X(k, 2));

end 

theta(1) = theta(1)-(1/m)*alpha*temp1;
theta(2) = theta(2)-(1/m)*alpha*temp2;



computeCost(X, y, theta)

end

end

编辑：这里也是computeCost

function J = computeCost(X, y, theta)
m = length(y); % number of training examples


J = 0;
temp = 0;
for index = 1:m
    temp = temp + (theta(1) + theta(2)*X(index, 2)-y(index))^2;

end 
J = temp/(2*m); 
end

Answer 1

尝试更改：

temp1=0;
temp2=0;
for iter = 1:num_iters

到

for iter = 1:num_iters
  temp1=0;
  temp2=0;

需要为每次迭代计算新的渐变（或者您在动量项中有效构建）。

梯度下降算法在matlab中给出错误的答案

1 个答案: