I keep getting this error
"Incompatible input and output sequence lengths. The network must return sequences with the same length as the
input data or a sequence with length one."
when I try to run this 1D CNN:
(For reference I am trying to analyse 320 signals with 2000 time points each)
inputSize = size(xTrain());
numClasses = 2; % Cracked or not cracked
layers = [
sequenceInputLayer(time_points,'Name', 'input','MinLength', 2000)
convolution1dLayer(32,20,'Padding', 'same')
batchNormalizationLayer
reluLayer
maxPooling1dLayer(2,'Stride',2)
convolution1dLayer(64,10,'Padding', 'same')
batchNormalizationLayer
reluLayer
maxPooling1dLayer(2,'Stride',2)
fullyConnectedLayer(256)
reluLayer
dropoutLayer(0.5)
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
%----------------------Define training options-----------------------------
options = trainingOptions('adam', ...
'InitialLearnRate',0.01, ...
'MaxEpochs',50, ...
'MiniBatchSize',128, ...
'Shuffle','every-epoch', ...
'ValidationData',{xTest,yTest}, ...
'ValidationFrequency',5, ...
'Verbose',false, ...
'Plots','training-progress');
% Train the network
net = trainNetwork(xTrain, yTrain, layers, options);
This is my first time using these neural networks so I have left all the layers and options in case there is an error in those. Any help would be useful.
Thank you

 Accepted Answer

Shouldn't your input layer be,
sequenceInputLayer(inputSize,'Name', 'input','MinLength', time_points)

5 Comments

Thank you that seems to have fixed it. Can you explain why that worked? 'time_points' is set equal to 2000 and inputSize is equal to [320, 1] now. I thought inputSize is supposed to be equal to the number of features in the sequence?
Yes, you have 320 features and 2000 time points.
Okay, I am now getting an error saying that the input data to my fullyConnectedLayer is invalid as it cannot have both spatial and temporal dimensions. How can this be possible if each sequence is only 1D (2000,1)? I have tried taking the real component of each value but this doesn't change anything
Seems like a good question. You should post it! However, if you do, you should attach an example of the training input that triggers the error, so that people can reproduce and study it.
Will do - thank you for your help!

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