代码搜索：Gradient
找到约 2,951 项符合「Gradient」的源代码
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www.eeworm.com/read/220289/14843801
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/220289/14843854
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/220289/14843904
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/117977/14892322
bas grad.bas
Attribute VB_Name = "Grad"
'Gradient Background Source code - Released into the public domain by John Rogers, June 19, 1996
'
'Requires VB40032.DLL.
'Gradient Background Demonstration program re
www.eeworm.com/read/117961/14892579
cc shape.cc
/////////////////////////////////////////////////////////////
// Flash Plugin and Player
// Copyright (C) 1998,1999 Olivier Debon
//
// This program is free software; you can redistribute it and/or
/
www.eeworm.com/read/213880/15123448
cpp edge_explorer.cpp
/*------------------------------------------------------------------------------
File : edge_explorer.cpp
Description : Real time edge detection while moving a ROI
(rectan
www.eeworm.com/read/212307/15160055
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/212307/15160091
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/212307/15160108
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/212307/15160163
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