How to Train 1d CNN on Custom dataset in matrix form in MATLAB
12 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
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 commentaires
Réponses (1)
yanqi liu
le 17 Fév 2022
Modifié(e) : yanqi liu
le 17 Fév 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 commentaires
yanqi liu
le 18 Fév 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
Voir également
Catégories
En savoir plus sur Image Data Workflows dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!