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 uvw.m

function f = uvw(xy); % % uvw % Objective function to minimize in Conjugate Gradient Homework. % Vectorized to handle a 2 x n input matrix xy, % whose columns give the evaluation points in the
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www.eeworm.com/read/394381/8227702

m fminusub.m

function [x,FVAL,GRADIENT,HESSIAN,EXITFLAG,OUTPUT] = fminusub(funfcn,x,verbosity,options,Fval,Gval,Hval,varargin) %FMINUSUB Finds the minimum of a function of several variables. % Copyright (c)
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www.eeworm.com/read/415311/11077208

m backpropagation_cgd.m

function [D, Wh, Wo] = Backpropagation_CGD(train_features, train_targets, params, region) % Classify using a backpropagation network with a batch learning algorithm and conjugate gradient descent
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www.eeworm.com/read/334860/12568150

m fminusub.m

function [x,FVAL,GRADIENT,HESSIAN,EXITFLAG,OUTPUT] = fminusub(funfcn,x,verbosity,options,Fval,Gval,Hval,varargin) %FMINUSUB Finds the minimum of a function of several variables. % Copyright (c)
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www.eeworm.com/read/216626/4889417

bas_fun triangle.dg.1.bas_fun

3 2 0 0.5 0.5 1 0 0 0 ./triangle.DG.1.bas_fun.so lambda_1 gradient_lambda_1 2 0 0.0 0.5 1 0 0 0 ./triangle.DG.1.bas_fun.so lambda_2 gradient_lambda_2 2 0 0.5 0.0 1 0 0 0 ./triangle.DG.1.bas_fun.s
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www.eeworm.com/read/216626/4889431

bas_fun triangle.rt.1.bas_fun

3 1 0 0.5 0.5 1 0 0 0 ./triangle.RT.1.bas_fun.so lambda_1 gradient_lambda_1 1 1 0.0 0.5 1 0 0 0 ./triangle.RT.1.bas_fun.so lambda_2 gradient_lambda_2 1 2 0.5 0.0 1 0 0 0 ./triangle.RT.1.bas_fun.s
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www.eeworm.com/read/216626/4889531

bas_fun twin_triangle.2.bas_fun

9 0 0 0.0 0.0 2 0 0 0 ./twin_triangle.2.bas_fun.so phi_1 gradient_phi_1 0 1 1.0 0.0 2 0 0 0 ./twin_triangle.2.bas_fun.so phi_2 gradient_phi_2 0 2 0.5 0.5 2 0 0 0 ./twin_triangle.2.bas_fun.so phi
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www.eeworm.com/read/216626/4889554

bas_fun tetrahedron.1.d.bas_fun

4 3 0 0.0 0.0 0.0 1 0 0 0 0 ./tetrahedron.1.D.bas_fun.so lambda_1 gradient_lambda_1 3 0 1.0 0.0 0.0 1 0 0 0 0 ./tetrahedron.1.D.bas_fun.so lambda_2 gradient_lambda_2 3 0 0.0 1.0 0.0 1 0 0 0 0 ./t
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www.eeworm.com/read/177674/9442393

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/176823/9483098

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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