代码搜索：NetWork

找到约 10,000 项符合「NetWork」的源代码

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www.eeworm.com/read/245176/12813189

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a max-win multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
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www.eeworm.com/read/245176/12813321

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a max-win multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
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www.eeworm.com/read/245176/12813330

m train.m

function net = train(net, tutor, varargin) % TRAIN % % Train a dag-svm multi-class support vector classifier network using the % specified tutor to train each component two-class network. %
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www.eeworm.com/read/144110/12815360

ini bonline.ini

[Admin] InstallType=Network [Default] BookTitle=Programming Windows Fifth Edition LaunchFile=petzoldi.CHM RPC=00000 BookIcon=1995x.ICO
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www.eeworm.com/read/144089/12817930

txt n.txt

N 【 缩 】 Nano 纳 N 【 缩 】 Negative 负 的 N 【 缩 】 Neper 奈培 N 【 缩 】 Neutral 中性的
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www.eeworm.com/read/144089/12818027

txt w.txt

W 【 缩 】 Wait 等待 W 【 缩 】 Watt 瓦 W 【 缩 】 Wave guide 波 导 ; 波 导 管 W 【 缩 】 Width 宽
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www.eeworm.com/read/143706/12849505

m mdnpak.m

function w = mdnpak(net) %MDNPAK Combines weights and biases into one weights vector. % % Description % W = MDNPAK(NET) takes a mixture density network data structure NET % and combines the network w
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www.eeworm.com/read/143706/12849510

m demolgd1.m

%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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www.eeworm.com/read/143706/12849701

m nethess.m

function [h, varargout] = nethess(w, net, x, t, varargin) %NETHESS Evaluate network Hessian % % Description % % H = NETHESS(W, NET, X, T) takes a weight vector W and a network data % structure NET, to
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www.eeworm.com/read/143706/12849828

m mlpfwd.m

function [y, z, a] = mlpfwd(net, x) %MLPFWD Forward propagation through 2-layer network. % % Description % Y = MLPFWD(NET, X) takes a network data structure NET together with a % matrix X of input vec
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