I have a problem in the trainNetwork for Xtrain and Ytrain it gives me X and Y must have the same number of observations.

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mahmoud Bassiouni
回答： mahmoud Bassiouni 2019-3-27
XTrain = importdata('C:\Users\m7mod\Documents\MATLAB\TrainM.mat'); % size of XTrain = 9 * 44 YTrain = categorical([1 1 1 1 0 0 0 0 0]'); % size of YTrain = 9 * 1 layers = [ ... imageInputLayer([44 1]) convolution2dLayer(5,20) reluLayer fullyConnectedLayer(10) softmaxLayer classificationLayer()] options = trainingOptions('sgdm'); XNew = zeros(size(XTrain,1),1,1,size(XTrain,2)); XNew(:,1,1,:) = XTrain(:,:); net = trainNetwork(XNew,YTrain',layers,options);

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Ameer Hamza
Ameer Hamza 2018-4-24
编辑：Ameer Hamza 2018-4-24
You are facing the problem because you are trying to use imageInputLayer and convolution2dLayer which will only work if your input sample have at least 2 non-singleton dimensions (i.e. m*n and m*n*k will work but 1*m or m*1 will not work). For a single dimension data (as in your case 1*44), you can use sequenceInputLayers. For your case, if you can change the layers combination as shown in following script snippet the code will work
XTrain = importdata('C:\Users\m7mod\Documents\MATLAB\TrainM.mat'); % size of XTrain = 9 * 44
YTrain = categorical([1 1 1 1 0 0 0 0 0]'); % size of YTrain = 9 * 1
layers = [ ...
sequenceInputLayer(44)
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm');
net = trainNetwork(XTrain',YTrain',layers,options);
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Ameer Hamza
Ameer Hamza 2018-4-24

The documentation page state that it was introduced in 2017b.

If you are using earlier version then you can use fitnet and train as

XTrain = importdata('C:\Users\m7mod\Documents\MATLAB\TrainM.mat'); % size of XTrain = 9 * 44 
YTrain = [1 1 1 1 0 0 0 0 0]'; % size of YTrain = 9 * 1 
net = fitnet([10 10]);
net = train(net, XTrain',YTrain');
mahmoud Bassiouni
mahmoud Bassiouni 2019-3-26
XTrain = AllTrainCel(1:200000,:)'; 4 * 200000
YTrain = categorical([1 0 -1 -2]'); % 4 * 1;
layers = [ ...
sequenceInputLayer(200000)
%reluLayer
LSTMLayer
fullyConnectedLayer(4)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm');
net = trainNetwork(XTrain',YTrain',layers,options);
net = fitnet([10 10]);
XTest = AllTestCel(1:200000,:)';
YTest = categorical([1 0 -1 -2]');
[YPred] = classify(net,XTest);
The problem is in the classification stage the classify doesnt work it. It needs more parameters although it work in the examples with two parameters only

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mahmoud Bassiouni
mahmoud Bassiouni 2019-3-27
The problem is in the classification stage the classify doesnt work it. It needs more parameters although it work in the examples of deep learning with two parameters only
XTrain = AllTrainCel(1:200000,:)'; 4 * 200000
YTrain = categorical([1 0 -1 -2]'); % 4 * 1;
layers = [ ...
sequenceInputLayer(200000)
%reluLayer
LSTMLayer
fullyConnectedLayer(4)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm');
net = trainNetwork(XTrain',YTrain',layers,options);
net = fitnet([10 10]);
XTest = AllTestCel(1:200000,:)';
YTest = categorical([1 0 -1 -2]');
[YPred] = classify(net,XTest);

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