Matlab code for Classification of IRIS data using MLP (Multi Layer Perceptron)

3 次查看（过去 30 天）
Bunny
Bunny 2016-11-23
评论： Bunny 2016-12-2
I'm trying to execute the following matlab code but I'm getting error about Time steps (TS) which is presented in network/sim.m (predefined matlab code). I couldn't edit this sim.m.
close all; clear; clc
%%load divided input data set
load fisheriris
% coding (+1/-1) of 3 classes
a = [-1 -1 +1]';
b = [-1 +1 -1]';
c = [+1 -1 -1]';
% define training inputs
rand_ind = randperm(50);
trainSeto = meas(rand_ind(1:35),:);
trainSeto=trainSeto';
trainVers = meas(50 + rand_ind(1:35),:);
trainVers=trainVers';
trainVirg = meas(100 + rand_ind(1:35),:);
trainVirg=trainVirg';
trainInp = [trainSeto trainVers trainVirg];
% define targets
tmp1 = repmat(a,1,length(trainSeto));
tmp2 = repmat(b,1,length(trainVers));
tmp3 = repmat(c,1,length(trainVirg));
T = [tmp1 tmp2 tmp3];
%%network training
trainCor = zeros(10,10);
valCor = zeros(10,10);
Xn = zeros(1,10);
Yn = zeros(1,10) ;
for k = 1:10 ,
Yn(1,k) = k;
for n = 1:10,
Xn(1,n) = n;
net = newff(trainInp,T,[k n],{},'trainbfg');
net = init(net);
net.divideParam.trainRatio = 1;
net.divideParam.valRatio = 0;
net.divideParam.testRatio = 0;
net.trainParam.show = NaN;
net.trainParam.max_fail = 2;
rand_ind = randperm(50);
valSeto = meas(rand_ind(1:20),:);
valSeto= valSeto';
valVers = meas(50 + rand_ind(1:20),:);
valVers=valVers';
valVirg = meas(100 + rand_ind(1:20),:);
valVirg=valVirg';
valInp = [valSeto valVers valVirg];
VV.P = valInp;
tmp1 = repmat(a,1,length(valSeto));
tmp2 = repmat(b,1,length(valVers));
tmp3 = repmat(c,1,length(valVirg));
valT = [tmp1 tmp2 tmp3];
net = train(net,trainInp,T,[],[],VV);%,TV);
Y = sim(net,trainInp);
[Yval,Pfval,Afval,Eval,perfval] = sim(net,valInp,[],[],valT);
Error of my matlab code:

请先登录，再回答此问题。

采纳的回答

Walter Roberson
Walter Roberson 2016-11-23
At the moment it appears to me to be a bug in the sim code. It looks to me as if you could get around the bug by not requesting the 5th output of sim()
  11 个评论
显示 9更早的评论
Walter Roberson
Walter Roberson 2016-12-1
I mean that I would need to dig into the Mathworks neural network code. I would rather not do that for the old code. You should re-write using feedforwardnet() instead of newff() and make other such appropriate changes.
Time spent investigating the inner working of code that was replaced six years ago would be a waste for me; I would have no further use for any information gained. Time spent investigating the current Mathworks code has the potential to be of use in future.

请先登录，再进行评论。

更多回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Deep Learning Toolbox 的更多信息

标签

产品

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by