trainlm predict value & switch

2020 3 15
1 个回答
更新时间：2020 3 23
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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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回答（1 个）

Mahesh Taparia
Mahesh Taparia 2020-3-18
编辑：Mahesh Taparia 2020-3-18

0 个投票

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)));

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Tomasz Kaczmarski
Tomasz Kaczmarski 2020-3-18
apologise it will not work
code
%Train= load('TrainSetArray.mat')
%Target = load('TargetSet.mat')
% avaliable under
%https://drive.google.com/open?id=1CzfnIY5DZqcu8Vt-zxM3I6sVLnzLhwK5
x = Train';
t = categorical(table2array(Tb(:,(563:563))));
trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation.
% Create a Fitting Network
hiddenLayerSize = 1;
net = fitnet(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,t);
%plot confusion chart across categories
YPredicted = classify(net.myNet,y)
plotconfusion(y,YPredicted)
Error in nntraining.setup (line 77)
[net,data,tr,err] = setupPerWorker(net,trainFcn,X,Xi,Ai,T,EW,enableConfigure);
train and target mat files avaliable under below link
https://drive.google.com/open?id=1CzfnIY5DZqcu8Vt-zxM3I6sVLnzLhwK5
Mahesh Taparia
Mahesh Taparia 2020-3-19
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);
Tomasz Kaczmarski
Tomasz Kaczmarski 2020-3-23
Hi
apologise but its not working im getting
In test (line 14)
Error using bsxfun
Non-singleton dimensions of the two input arrays must match each other.
Error in gsubtract>calc_general (line 40)
c = bsxfun(@minus,a,b);
additionally im trying to achive is confusion matrix across classes mine code is below
%PREPROCESSING
%load data
Tb = readtable('train1.csv','PreserveVariableNames',true);
Tbv = readtable('test1.csv','PreserveVariableNames',true);
%merge both datasets they can be splitted to train target later via dynamic
%parameter
Tbt = union(Tb, Tbv)
%split into train target
TrainSet = Tbt(:,(1:562))%data.simplefitInputs';
TargetSet = Tbt(:,(563:563));%data.simplefitTargets';
TargetSet= table2array(TargetSet)
%make some noise
TrainSetArray = table2array(TrainSet)
noiseSignal = cos(5 * pi * 100 * TrainSetArray)+sqrt(5) * randn(size(TrainSetArray));
%TRAINING
x = TrainSetArray';
%x = noiseSignal';
t = TargetSet';
%t = categorical(Tb(:,(563:563)));FAILING DURING TRAINING
trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation.
% Create a Fitting Network
hiddenLayerSize = 10;
net = fitnet(hiddenLayerSize,trainFcn);
% Create a Fitting Network
hiddenLayerSize = 1;
net = fitnet(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,t);
%get output
y = net(x);
%get test set
testX = x(:,tr.testInd);
testT = t(:,tr.testInd);
testY = net(testX);
%PLOTTING CONFUSION MATRIXacorss classes as
% https://uk.mathworks.com/help/deeplearning/ref/plotconfusion.html
YPredicted = classify(net.myNet,testX)%Line Causing error SWITCH expression must be a scalar or a character vector.
plotconfusion(testy,YPredicted)

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