Invalid training data. Predictors and responses must have the same number of observations.

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Bahadir 2025-8-28，18:52

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https://ww2.mathworks.cn/matlabcentral/answers/2179667-invalid-training-data-predictors-and-responses-must-have-the-same-number-of-observations

编辑： Matt J 2025-8-28，21:34

采纳的回答： Matt J

在 MATLAB Online 中打开

I wan to train a LSTM.

But I get Error:

Error using trainNetwork (line 191)

Invalid training data. Predictors and responses must have the same number of observations.

layers = [ ...
    sequenceInputLayer(6)
    lstmLayer(120,'OutputMode','last')
    fullyConnectedLayer(2)
    softmaxLayer
    classificationLayer];
options = trainingOptions('adam', ...
    'MaxEpochs',20, ...
    'MiniBatchSize',32, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.005, ...
    'Shuffle','every-epoch', ...
    'Verbose',0, ...
    'Plots','training-progress');
net = trainNetwork(XTrain, YTrain, layers, options);

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Answer 1

Matt J 2025-8-28，19:41

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2179667-invalid-training-data-predictors-and-responses-must-have-the-same-number-of-observations#answer_1569821

编辑：Matt J 2025-8-28，19:55

在 MATLAB Online 中打开

Your XTrain shouldn't be a 100x6 cell. It should be a 100x1 cell where each XTrain{i} is a matrix with 6 rows. Example,

layers = [ ...
    sequenceInputLayer(6)
    lstmLayer(120,'OutputMode','last')
    fullyConnectedLayer(2)
    softmaxLayer
    classificationLayer];
for i=1:100
 XTrain{i,1} = rand(6,randi(20));
end
YTrain = categorical(randi([0,1],100,1));
whos YTrain
  Name          Size            Bytes  Class          Attributes

  YTrain      100x1               346  categorical              
XTrain,
XTrain = 100×1 cell array
    {6×20 double}
    {6×16 double}
    {6×11 double}
    {6×2  double}
    {6×18 double}
    {6×8  double}
    {6×13 double}
    {6×17 double}
    {6×19 double}
    {6×6  double}
    {6×1  double}
    {6×1  double}
    {6×17 double}
    {6×10 double}
    {6×5  double}
    {6×4  double}
options = trainingOptions('adam', ...
    'MaxEpochs',20, ...
    'MiniBatchSize',32, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.005, ...
    'Shuffle','every-epoch', ...
    'Verbose',1, ...
    'Plots','none');
net = trainNetwork(XTrain, YTrain, layers, options)
Training on single CPU.
|========================================================================================|
|  Epoch  |  Iteration  |  Time Elapsed  |  Mini-batch  |  Mini-batch  |  Base Learning  |
|         |             |   (hh:mm:ss)   |   Accuracy   |     Loss     |      Rate       |
|========================================================================================|
|       1 |           1 |       00:00:00 |       46.88% |       0.7000 |          0.0050 |
|      17 |          50 |       00:00:01 |       71.88% |       0.6504 |          0.0050 |
|      20 |          60 |       00:00:01 |       53.12% |       0.6374 |          0.0050 |
|========================================================================================|
Training finished: Max epochs completed.
net = 
  SeriesNetwork with properties:

         Layers: [5×1 nnet.cnn.layer.Layer]
     InputNames: {'sequenceinput'}
    OutputNames: {'classoutput'}

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Bahadir 2025-8-28，20:58

When ı try trainnet, I get the error.

Caused by:

Layer 'classoutput': Detected output layer. The network must not have output layers.

Matt J 2025-8-28，21:05

编辑：Matt J 2025-8-28，21:34

The error is complaining that you have not removed the output layer (classificationLayer) from your layers array. Output layers do not belong in the network when training with trainnet, because the loss function is separately specified to trainnet using the lossFcn input parameter.

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