How to Train CNN on Custom dataset in matrix form

Question

Med Future 2022-2-14

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1649260-how-to-train-cnn-on-custom-dataset-in-matrix-form

评论： yanqi liu 2022-2-17

采纳的回答： yanqi liu

myFile.txt

在 MATLAB Online 中打开

Hi everyone, i hope you are doing well

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 simple CNN on this dataset

How i can randomly split the data into train/validation/ test and reshape the data to train simple CNN

when i run numel(trainlabel) it shows 102 Classes but i have only 5 Classes

I have got this error when i run the below code

Caused by:

Layer 2: Input size mismatch. Size of input to this layer is different from the expected input size.

Inputs to this layer:

from layer 1 (size 102(S) × 4(S) × 1(C) × 1(B))

%myFile.txt includes 102x5,in which first 4 coloums are the
% Number of Observation and the last 5 column are the Discrete labels/Classes for
% the dataset
dataset=readmatrix('myFile.txt');
traindata=dataset(:,1:4);
trainlabel=categorical(dataset(:,5));
% The following show 102 Classes but i have only 5 Classes
numClasses = numel(trainlabel)
%How to Split Data Randomly into Train,Validation and Test
% The below code Split the data 80/20 in Train and Test but not randomly
PD = 0.80 ;  % percentage 80%
Ptrain = traindata(1:round(PD*length(traindata)),:) ; Ttrain = trainlabel(1:round(PD*length(traindata))) ;
Ptest = traindata(round(PD*length(traindata)):end,:) ;Ttest = trainlabel(round(PD*length(traindata)):end) ;
% I have defined the following Network for training,
layers = [ ...
    imageInputLayer([102 4 1])
    convolution2dLayer(5,20)
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    fullyConnectedLayer(5)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'MaxEpochs',20,...
    'InitialLearnRate',1e-4, ...
    'Verbose',false, ...
    'Plots','training-progress');
net = trainNetwork(traindata,trainlabel,layers,options);

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

yanqi liu 2022-2-14

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1649260-how-to-train-cnn-on-custom-dataset-in-matrix-form#answer_895440

在 MATLAB Online 中打开

yes，sir，may be reshape your data，such as

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);

10 个评论
显示 8更早的评论隐藏 8更早的评论

Med Future 2022-2-17

@yanqi liu Can you please share 1D CNN network for this?

yanqi liu 2022-2-17

在 MATLAB Online 中打开

yes，sir，may be make model structure like this

layers = [ ...
    sequenceInputLayer(4)
    lstmLayer(100,'OutputMode','sequence')
    dropoutLayer(0.3)
    lstmLayer(50,'OutputMode','sequence')
    dropoutLayer(0.2)
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];