How to Train 1d CNN on Custom dataset in matrix form in MATLAB
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Hi everyone, i hope you are doing well.
In my previous question : https://www.mathworks.com/matlabcentral/answers/1649260-how-to-train-cnn-on-custom-dataset-in-matrix-form
yanqi liu answer the question with 2D CNN, But i wanted to train 1D CNN
i have the following dataset myFile.txt includes 102x5,in which first 4 coloums are the Number of Observation and the last column are the Discrete labels/Classes for the dataset. I want to train 1D-CNN on this dataset
sz = size(dataset);
dataset = dataset(randperm(sz(1)),:);
traindata=dataset(:,1:4);
trainlabel=categorical(dataset(:,5));
classes = unique(trainlabel)
numClasses = numel(unique(trainlabel))
PD = 0.80 ;
Ptrain = []; Ttrain = [];
Ptest = []; Ttest = [];
for i = 1 : length(classes)
indi = find(trainlabel==classes(i));
indi = indi(randperm(length(indi)));
indj = round(length(indi)*PD);
Ptrain = [Ptrain; traindata(indi(1:indj),:)]; Ttrain = [Ttrain; trainlabel(indi(1:indj),:)];
Ptest = [Ptest; traindata(indi(1+indj:end),:)]; Ttest = [Ttest; trainlabel(indi(1+indj:end),:)];
end
Ptrain=(reshape(Ptrain', [4,1,1,size(Ptrain,1)]));
Ptest=(reshape(Ptest', [4,1,1,size(Ptest,1)]));
layers = [imageInputLayer([4 1 1])
convolution2dLayer([3 1],3,'Stride',1)
batchNormalizationLayer
reluLayer
maxPooling2dLayer(2,'Stride',2,'Padding',[0 0 0 1])
dropoutLayer
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
options = trainingOptions('adam', ...
'MaxEpochs',3000, ...
'Shuffle','every-epoch', ...
'Plots','training-progress', ...
'Verbose',false, ...
'ValidationData',{Ptest,Ttest},...
'ExecutionEnvironment', 'cpu', ...
'ValidationPatience',Inf);
net = trainNetwork(Ptrain,Ttrain,layers,options);
3 Kommentare
Antworten (1)
yanqi liu
am 17 Feb. 2022
Bearbeitet: yanqi liu
am 17 Feb. 2022
yes,sir,if 2021b has convolution1dLayer,so we can make the cnn as follows,then we can try train it
layers = [sequenceInputLayer(4)
convolution1dLayer(3,32,Padding="causal")
reluLayer
globalMaxPooling1dLayer
dropoutLayer
fullyConnectedLayer(5)
softmaxLayer
classificationLayer];
layers
8 Kommentare
yanqi liu
am 18 Feb. 2022
yes,sir,here on web,we can not see the plot curve,so we get the train status info and plot it
this picture is train acc curve by stats info structure
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