代码搜索：Gradient

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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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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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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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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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htm glmgrad.htm

Netlab Reference Manual glmgrad glmgrad Purpose Evaluate gradient of error function for generalized linear model. Synopsis
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htm rbfbkp.htm

Netlab Reference Manual rbfbkp rbfbkp Purpose Backpropagate gradient of error function for RBF network. Synopsis
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htm mlpgrad.htm

Netlab Reference Manual mlpgrad mlpgrad Purpose Evaluate gradient of error function for 2-layer network. Synopsis
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htm mlpbkp.htm

Netlab Reference Manual mlpbkp mlpbkp Purpose Backpropagate gradient of error function for 2-layer network. Synopsis
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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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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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