我的Gradient Descent算法有什么问题

Question

嗨,我正在尝试为函数实现梯度下降算法:

我的算法起点是w =(u,v)=(2,2).学习率为eta = 0.01且bound = 10 ^ -14.这是我的MATLAB代码:

function [resultTable, boundIter] = gradientDescent(w, iters, bound, eta)
% FUNCTION [resultTable, boundIter] = gradientDescent(w, its, bound, eta)
% 
% DESCRIPTION: 
% - This function will do gradient descent error minimization for the
% function E(u,v) = (u*exp(v) - 2*v*exp(-u))^2.
%
% INPUTS: 
% 'w' a 1-by-2 vector indicating initial weights w = [u,v]
% 'its' a positive integer indicating the number of gradient descent
% iterations
% 'bound' a real number indicating an error lower bound
% 'eta' a positive real number indicating the learning rate of GD algorithm
%
% OUTPUTS: 
% 'resultTable' a iters+1-by-6 table indicating the error, partial
% derivatives and weights for each GD iteration
% 'boundIter' a positive integer specifying the GD iteration when the error
% function got below the given error bound 'bound'
% 


% The error function 
E = @(u,v) (u*exp(v) - 2*v*exp(-u))^2;

% Partial derivative of E with respect to u 
pEpu = @(u,v) 2*(u*exp(v) - 2*v*exp(-u))*(exp(v) + 2*v*exp(-u));
% Partial derivative of E with respect to v 
pEpv = @(u,v) 2*(u*exp(v) - 2*v*exp(-u))*(u*exp(v) - 2*exp(-u));

% Initialize boundIter
boundIter = 0;
% Create a table for holding the results
resultTable = zeros(iters+1, 6);
% Iteration number
resultTable(1, 1) = 0;
% Error at iteration i
resultTable(1, 2) = E(w(1), w(2));
% The value of pEpu at initial w = (u,v)
resultTable(1, 3) = pEpu(w(1), w(2));
% The value of pEpv at initial w = (u,v)
resultTable(1, 4) = pEpv(w(1), w(2));
% Initial u
resultTable(1, 5) = w(1);
% Initial v
resultTable(1, 6) = w(2);

% Loop all the iterations
for i = 2:iters+1

    % Save the iteration number
    resultTable(i, 1) = i-1; 
    % Update the weights
    temp1 = w(1) - eta*(pEpu(w(1), w(2)));
    temp2 = w(2) - eta*(pEpv(w(1), w(2)));
    w(1) = temp1;
    w(2) = temp2;
    % Evaluate the error function at new weights
    resultTable(i, 2) = E(w(1), w(2));
    % Evaluate pEpu at the new point 
    resultTable(i, 3) = pEpu(w(1), w(2));
    % Evaluate pEpv at the new point
    resultTable(i, 4) = pEpv(w(1), w(2));
    % Save the new weights
    resultTable(i, 5) = w(1);
    resultTable(i, 6) = w(2);
    % If the error function is below a specified bound save this iteration
    % index
    if E(w(1), w(2)) < bound
        boundIter = i-1;
    end

end

这是我的机器学习课程中的练习,但由于某种原因,我的结果都是错误的.代码中肯定有问题.我已经尝试过调试和调试它并且没有发现任何错误...有人可以确定我的问题在这里吗？...换句话说,你可以检查代码是否是给定函数的有效梯度下降算法？

如果我的问题太清楚或者您需要更多信息,请告诉我:)

感谢您的努力和帮助!=)

这是我五次迭代的结果以及其他人得到的结果:

参数:w = [2,2],eta = 0.01,束缚= 10 ^ -14,iters = 5

Answer 1

如下问题所讨论：我想说其他人是错误的......你的最小化导致 的值更小E(u,v)，检查：

E(1.4,1.6) = 37.8 >> 3.6 = E(0.63, -1.67)