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/177674/9442478

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/177674/9442562

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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www.eeworm.com/read/177674/9442589

m mdn.m

function net = mdn(nin, nhidden, ncentres, dim_target, mix_type, ... prior, beta) %MDN Creates a Mixture Density Network with specified architecture. % % Description % NET = MDN(NIN, NHIDDEN, NCENTRE
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www.eeworm.com/read/177674/9442591

m rbfpak.m

function w = rbfpak(net) %RBFPAK Combines all the parameters in an RBF network into one weights vector. % % Description % W = RBFPAK(NET) takes a network data structure NET and combines the % componen
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www.eeworm.com/read/177674/9442693

m netevfwd.m

function [y, extra, invhess] = netevfwd(w, net, x, t, x_test, invhess) %NETEVFWD Generic forward propagation with evidence for network % % Description % [Y, EXTRA] = NETEVFWD(W, NET, X, T, X_TEST) tak
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www.eeworm.com/read/177261/9463125

c etheroutputdemo.c

/* etherOutputDemo.c - Demo for using low-level output ethernet routines */ /* Copyright 1984-1997 Wind River Systems, Inc. */ /* modification history -------------------- 01d,06nov97,mm added cop
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www.eeworm.com/read/176823/9483096

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/176823/9483098

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/176823/9483177

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/176823/9483269

m mdn.m

function net = mdn(nin, nhidden, ncentres, dim_target, mix_type, ... prior, beta) %MDN Creates a Mixture Density Network with specified architecture. % % Description % NET = MDN(NIN, NHIDDEN, NCENTRE
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