function?[model?L]?=?mlpReg（XYklambda）
%?Train?a?multilayer?perceptron?neural?network
%?Input:
%???X:?d?x?n?data?matrix
%???Y:?p?x?n?response?matrix
%???k:?T?x?1?vector?to?specify?number?of?hidden?nodes?in?each?layer
%???lambda:?regularization?parameter
%?Ouput:
%???model:?model?structure
%???L:?loss
%?Written?by?Mo?Chen?（sth4nth@gmail.com）.
if?nargin?????lambda?=?1e-2;
end
eta?=?1e-3;
maxiter?=?50000;
L?=?inf（1maxiter）;

k?=?[size（X1）;k（:）;size（Y1）];
T?=?numel（k）-1;
W?=?cell（T1）;
b?=?cell（T1）;
for?t?=?1:T
????W{t}?=?randn（k（t）k（t+1））;
????b{t}?=?randn（k（t+1）1）;
end
R?=?cell（T1）;
Z?=?cell（T+11）;
Z{1}?=?X;
for?iter?=?2:maxiter
%?????forward
????for?t?=?1:T-1
????????Z{t+1}?=?tanh（W{t}‘*Z{t}+b{t}）;
????end
????Z{T+1}?=?W{T}‘*Z{T}+b{T};

%?????loss
????E?=?Z{T+1}-Y;?????
????Wn?=?cellfun（@（x）?dot（x（:）x（:））W）;????????????%?|W|^2
????L（iter）?=?dot（E（:）E（:））+lambda*sum（Wn）;

%?????backward
????R{T}?=?E;????????????????%?delta
????for?t?=?T-1:-1:1
????????df?=?1-Z{t+1}.^2;????%?h‘（a）
????????R{t}?=?df.*（W{t+1}*R{t+1}）;????%?delta
????end
????
%?????gradient?descent
????for?t=1:T
????????dW?=?Z{t}*R{t}‘+lambda*W{t};
????????db?=?sum（R{t}2）;
????????W{t}?=?W{t}-eta*dW;
????????b{t}?=?b{t}-eta*db;
????end
end
L?=?L（12:iter）;
model.W?=?W;
model.b?=?b;

?屬性????????????大小?????日期????時間???名稱
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?????文件??????12945??2019-11-11?20:56??MLP回歸的反向傳播的實現\A-Ein?F-Hout.xlsx

?????文件???????1342??2019-11-19?19:59??MLP回歸的反向傳播的實現\mlpReg.m

?????文件????????336??2019-11-19?22:07??MLP回歸的反向傳播的實現\mlpRegPred.m

?????文件????????888??2019-11-21?16:58??MLP回歸的反向傳播的實現\mlp_demo.m

?????目錄??????????0??2019-11-21?16:59??MLP回歸的反向傳播的實現

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????????????????15511????????????????????5