代码搜索：Gradient

找到约 2,951 项符合「Gradient」的源代码

代码结果 2,951
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m mlpgrad.m

function [g, gdata, gprior] = mlpgrad(net, x, t) %MLPGRAD Evaluate gradient of error function for 2-layer network. % % Description % G = MLPGRAD(NET, X, T) takes a network data structure NET together
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www.eeworm.com/read/176823/9483115

m netgrad.m

function g = netgrad(w, net, x, t) %NETGRAD Evaluate network error gradient for generic optimizers % % Description % % G = NETGRAD(W, NET, X, T) takes a weight vector W and a network data % structure
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www.eeworm.com/read/176823/9483165

m mlpbkp.m

function g = mlpbkp(net, x, z, deltas) %MLPBKP Backpropagate gradient of error function for 2-layer network. % % Description % G = MLPBKP(NET, X, Z, DELTAS) takes a network data structure NET % togeth
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www.eeworm.com/read/176823/9483192

m scg.m

function [x, options, flog, pointlog, scalelog] = scg(f, x, options, gradf, varargin) %SCG Scaled conjugate gradient optimization. % % Description % [X, OPTIONS] = SCG(F, X, OPTIONS, GRADF) uses a sca
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www.eeworm.com/read/176823/9483293

m glmgrad.m

function [g, gdata, gprior] = glmgrad(net, x, t) %GLMGRAD Evaluate gradient of error function for generalized linear model. % % Description % G = GLMGRAD(NET, X, T) takes a generalized linear model da
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www.eeworm.com/read/176823/9483369

m mlpgrad.m

function [g, gdata, gprior] = mlpgrad(net, x, t) %MLPGRAD Evaluate gradient of error function for 2-layer network. % % Description % G = MLPGRAD(NET, X, T) takes a network data structure NET together
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www.eeworm.com/read/167700/9955233

m waveletnetwork.m

clear all %initiate of data P=3 %numberof sample m=1%number of input node n=10%number of hidden node N=1%number of ouptut node % %a(n) b(n) scale and shifting parameter matrix %x(P,m) i
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www.eeworm.com/read/362100/10019188

m wnns.m

clear all %initiate of data P=48; %numberof sample样本数 m=3;%number of input node输入节点数 n=10;%number of hidden node隐层节点数 N=3;%number of ouptut node输出节点数 % %a(n) b(n) scale and shifting param
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www.eeworm.com/read/360770/10079226

m gradfmu.m

function g = gradfmu (f,p,q,x,mu) %----------------------------------------------------------------------- % Usage: g = gradfmu (f,p,q,x,mu) % % Description: Numerically approximate the grad
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www.eeworm.com/read/360770/10079251

m conjgrad.m

function [x,ev,j] = conjgrad (x0,tol,v,m,f) %----------------------------------------------------------------------- % Usage: [x,ev,j] = conjgrad (x0,tol,v,m,f) % % Description: Use the Flet
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