代码搜索：Gradient

找到约 2,951 项符合「Gradient」的源代码

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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/485544/6552712

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/485544/6552765

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/485544/6552803

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/261194/11660424

java swfgradienti.java

// // Description: // SWFGradient Interface // // Authors: // Jonathan Shore // Based on php wrapper developed by // // Copyright: // Copyright 200
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www.eeworm.com/read/259881/11760211

m calcgxmodref.m

function [gX,normgX] = calcgxmodref(net,X,PD,BZ,IWZ,LWZ,N,Ac,El,perf,Q,TS); %CALCGXMODREF Calculate weight and bias performance gradient as a single vector for a % fixed Neural Network
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www.eeworm.com/read/138706/11882432

m xiaobosjwl.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/153969/11997423

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/153969/11997440

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/255379/12084485

c header.c

/************************************************************* 3-D Reconstruction of Medical Images Three Dimensional Reconstruction Of Medical Images from Serial Slices - CT, MRI, Ultraso
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