trainlm predict value & switch
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output of trained network is y from
load data
Tb = readtable('train1.csv','PreserveVariableNames',true)
x = Tb(:,(1:562))%data.simplefitInputs';
t = Tb(:,(563:563));%data.simplefitTargets';
[net,tr] = train(net,x,t);
y = net(x);
as target were classes 1,2,3,4,5,6
why output is 1.123 , 4.454, 5.6575 etc not same classes as input?
and how to convert output y to predicted value ?
YPredicted = classify(net.myNet,y)
returns error:
SWITCH expression must be a scalar or a character vector.
Error in network/subsref (line 173)
switch (subs)
my final output would be confusionmatrix
plotconfusion(YTest,YPredicted)
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Risposte (1)
Mahesh Taparia
il 18 Mar 2020
Modificato: Mahesh Taparia
il 18 Mar 2020
Hi
In my understanding, you want classification output and you are getting regression output. Convert the variable 't' into a categorical array before training the model and check if it is working or not, i.e
t = categorical(Tb(:,(563:563)));
3 Commenti
Mahesh Taparia
il 19 Mar 2020
Hi
The below is the working code for classification of your application. Hope it will helps.
Train= load('TrainSetArray.mat')
Target = load('TargetSetArray.mat')
% avaliable under
%https://drive.google.com/open?id=1CzfnIY5DZqcu8Vt-zxM3I6sVLnzLhwK5
x = Train.TrainSetArray;
t = Target.TargetSet;
t1=ind2vec(t');
trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation.
% Create a Fitting Network
hiddenLayerSize = 1;
net = patternnet(hiddenLayerSize,trainFcn);
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% Train the Network
[net,tr] = train(net,x',t1);
y = net(x');%%%%% include the test vectors here
perf = perform(net,t,y);
classes = vec2ind(y);
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Categorie
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