代码搜索：gradient

找到约 2,951 项符合「gradient」的源代码

代码结果 2,951
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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
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www.eeworm.com/read/344585/3207747

m gbayes.m

function [g, gdata, gprior] = gbayes(net, gdata) %GBAYES Evaluate gradient of Bayesian error function for network. % % Description % G = GBAYES(NET, GDATA) takes a network data structure NET together
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main tetramod.tetra.main

TETRAMOD - TETRAhedron MODel builder. In each layer, velocity gradient is constant or a 2-D grid; horizons could be a uniform grid and/or added by a 2-D grid specified. tetramo
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c e_and_g.c

/* # proc: e_and_g - Computes the error value resulting from sending the # proc: patterns through the MLP. Also can compute the gradient # proc: of the error w.r.t. the weights, a
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www.eeworm.com/read/396844/2406593

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
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www.eeworm.com/read/396844/2406680

m gbayes.m

function [g, gdata, gprior] = gbayes(net, gdata) %GBAYES Evaluate gradient of Bayesian error function for network. % % Description % G = GBAYES(NET, GDATA) takes a network data structure NET together
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www.eeworm.com/read/359369/2978479

m gbayes.m

function [g, gdata, gprior] = gbayes(net, gdata) %GBAYES Evaluate gradient of Bayesian error function for network. % % Description % G = GBAYES(NET, GDATA) takes a network data structure NET toget
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www.eeworm.com/read/357319/3017375

dfm jvgradientformdemo.dfm

object frgradient: Tfrgradient Left = 192 Top = 110 Width = 696 Height = 480 Caption = 'Gradient Backround' Color = clBtnFace Font.Charset = DEFAULT_CHARSET Font.Color = clWind
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clw gradienttxt.clw

; CLW file contains information for the MFC ClassWizard [General Info] Version=1 LastClass=CAboutDlg LastTemplate=CDialog NewFileInclude1=#include "stdafx.h" NewFileInclude2=#include "Gradient
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www.eeworm.com/read/395537/8168746

m uvwd.m

function gradf = uvwd(xy); % % uvwd % Gradient of objective function uvw. % Vectorized to handle a 2 x n input matrix xy, % whose columns give the evaluation points in the plane. % Returns an
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