Gradient descent with a simple function팔로우 조회 수: 1 (최근 30일) Hai Luu 2020년 3월 13일 추천 0 링크 번역 MATLAB Online에서 열기 Hi everyone, I am currently practicing this method on a simple function, however I keep getting this error ...
function[g_result,u_result] = GD(N_Z,y,alpha,u0) %GD 梯度下降法 % Detailed explanation goes here [n,~] =size(N_Z); u = u0; k = 0; t = y-N_Z*u; disp("g(u):"); while(合理的终止条件) k = k + 1; u = u -alpha* (-2/n)*N_Z'*t; t = y-N_Z*u; if(mod...
function y = gfun(x) x1 = x(1); x2 = x(2); y = [2*x1 + x2 + 2; x1 + 2*x2 - 3]; end 最速下降法算法函数 function [x,val,k]=grad(fun,gfun,xO) %功能:用最速下降法求解无约束问题: min f(x)%输入:x0是初始点, fun, gfun分别是目标函数和梯度 %输出: x, val分别是...
一、梯度下降(Gradient Descent) 神经网络的参数用 表示,选一个初始的参数 ,然后计算 对loss function的gradient,也就是计算神经网络中每一个参数对loss function的偏微分,计算出来所有参数的偏微分之后,得到一个向量,然后就可以更新网络的参数。更新一次参数就可以得到 ,更新两次就得到 ,然后不断更新直到找到那组让l...
梯度下降(Gradient Descent)是一种常用的优化算法,常用于机器学习中的参数优化。 梯度下降的基本思想是,沿着函数的梯度(或者说导数)的反方向,以步长为步进量逐渐逼近函数的最小值点。在机器学习中,梯度下降被用来求解最小化损失函数的参数。 具体来说,对于一个损失函数 ...
def gradient_function(theta, X, y): diff = np.dot(X, theta) - y return (1./m) * np.dot(np.transpose(X), diff) 接下来就是最重要的梯度下降算法,我们取 \theta_{0} 和\theta_{1} 的初始值都为1,再进行梯度下降过程。 def gradient_descent(X, y, alpha): theta = np.array([1,...
然后，实现最速下降法算法。其更新公式为：x_new = x_old - α * g(x_old)。在MATLAB中，可以编写如下函数来实现：matlab function [x, f] = gradientDescent(f, g, x0, alpha, maxIter)x = x0;for iter = 1:maxIter grad = g(x);x = x - alpha * grad;f(iter) = f(x);...
Returns 1 if this function usesgWorgA Examples Here you define a random gradientGfor a weight going to a layer with three neurons from an input with two elements. Also define a learning rate of 0.5 and momentum constant of 0.8: gW = rand(3,2); lp.lr = 0.5; lp.mc = 0.8; ...
% redefine objective function syntax for use with optimization: f2 = @(x) f(x(1),x(2)); % gradient descent algorithm: whileand(gnorm>=tol, and(niter <= maxiter, dx >= dxmin)) % calculate gradient: g = grad(x); gnorm = norm(g); ...
('Gradient Descent for Curve Fitting'); legend('Data', 'Fitted Curve'); grid on; % 可视化损失函数随迭代次数的变化 figure; plot(1:max_iter, arrayfun(@(i) loss_function(theta_history(i, :), x, y), 1:max_iter)); xlabel('Iteration'); ylabel('Loss'); title('Loss Function vs. ...