Invalid training data. Predictors and responses must have the same number of observations.
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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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Risposta accettata
  Matt J
      
      
 il 28 Ago 2025
        
      Modificato: Matt J
      
      
 il 28 Ago 2025
  
      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
XTrain,
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)
3 Commenti
  Matt J
      
      
 il 28 Ago 2025
				
      Modificato: Matt J
      
      
 il 28 Ago 2025
  
			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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