代码搜索：gradient
找到约 2,951 项符合「gradient」的源代码
代码结果 2,951
www.eeworm.com/read/415313/11076474
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
www.eeworm.com/read/415313/11076508
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
www.eeworm.com/read/415313/11076626
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
www.eeworm.com/read/415313/11076711
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
www.eeworm.com/read/414548/11108824
m wavenetrecogniton.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) input ma
www.eeworm.com/read/413912/11137109
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
www.eeworm.com/read/413912/11137182
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
www.eeworm.com/read/413912/11137212
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
www.eeworm.com/read/413912/11137311
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
www.eeworm.com/read/413912/11137415
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