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/150905/12250405
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/150905/12250439
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/150905/12250545
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/150905/12250661
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/150290/12300452
html syng.html
Synergistic Image Segmenter
Edge Detection and Image Segmentation (EDISON) System
www.eeworm.com/read/338243/12316518
man slaveforward.3.man
SLAVEFORWARD(derived)FORWARD OPERATORS SLAVEFORWARD(derived)
Nov 20 10:03
NAME
SlaveForward
SYNOPSIS
#include
cla
www.eeworm.com/read/338243/12316637
hh cg.hh
//============================================================
// COOOL version 1.1 --- Nov, 1995
// Center for Wave Phenomena, Colorado School of Mines
//==================
www.eeworm.com/read/230872/14271289
html readme.html
PgsLookAndFeel - An introduction
h1, h2, h3 {
font-size: 16px;
background: #0098FF;
border-bottom: 1px solid #006BB3;
color
www.eeworm.com/read/220289/14843748
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/220289/14843784
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