代码搜索：NetWork

找到约 10,000 项符合「NetWork」的源代码

代码结果 10,000
热门代码： main.c GPIO I2C SPI UART timer interrupt ADC PWM LED
www.eeworm.com/read/485494/6551227

htm 21187.htm

 
·
www.eeworm.com/read/485544/6552647

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
·
www.eeworm.com/read/485544/6552649

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
·
www.eeworm.com/read/485544/6552701

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
·
www.eeworm.com/read/485544/6552742

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
·
www.eeworm.com/read/485544/6552756

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
·
www.eeworm.com/read/485544/6552757

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
·
www.eeworm.com/read/485544/6552799

m mlpjacob.m

function g = mlpjacob(net, x) %MLPJACOB Backpropagate gradient of error function for 2-layer network. % % Description % G = MLPJACOB(NET, X) takes a network data structure NET and an % input vec
·
www.eeworm.com/read/485544/6552810

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
·
www.eeworm.com/read/485544/6552825

m mlpunpack.m

function [W1, B1, W2, B2, W3, B3, W4, B4] = mlpunpack(nodes, wh) % MLPUNPACK ReBEL MLP neural network weight matrices de-vectorizer. % % This function unpacks the parameters (weights and biases) of
·