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/212307/15160201
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/210259/15202726
m mutual_mimo_deri_symm.m
function [fgrad_mean, fhess_mean, fmutual_mean] = ...
mutual_mimo_deri_symm(M, N, H0, Rt_sqrt, gamma, Q, NSAMPLE, ...
ind1, ind2, I, Nx, Nr);
%----------------------
www.eeworm.com/read/172754/5382389
java gradientpaintreadhandler.java
/* ========================================================================
* JCommon : a free general purpose class library for the Java(tm) platform
* ===========================================
www.eeworm.com/read/154890/5631980
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/170936/9779168
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/170936/9779224
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/170936/9779251
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/170936/9779334
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/170936/9779392
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/415313/11076393
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