Error using trainNetwork Invalid training data. For cell array input, responses must be an N-by-1 cell array of sequences, where N is the number of sequences. The spatial and
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Error using trainNetwork
Invalid training data. For cell array input, responses must be an N-by-1 cell array of sequences, where N is the number of sequences. The spatial and channel dimensions of the sequences must be the same as the output size of the last layer (1).
% Edit - running code here
load CycleAgeingData.mat
numHiddenUnits = 50;
inputSize1 = size(Data{1},1)
% layers = [
% sequenceInputLayer(numChannels)
% lstmLayer(128)
% fullyConnectedLayer(numChannels)
% regressionLayer];
%
layers = [ ...
sequenceInputLayer(inputSize1)
lstmLayer(50, 'OutputMode', 'sequence')
fullyConnectedLayer(7)
dropoutLayer(0.011547480894612765)
fullyConnectedLayer(1)
regressionLayer];
% layersLSTM = [ ...
% sequenceInputLayer(inputSize1)
% lstmLayer(numHiddenUnits)
% fullyConnectedLayer(1)
% regressionLayer
% ];
% cell1x = num2cell(features', 1)';
% targets=cap6/cap6(1)
% cell1yB = num2cell(targets);
numChannels = size(Data{1},1)
numObservations = numel(Data);
idxTrain = 1:floor(0.7*numObservations);
idxval = floor(0.7*numObservations)+1:numObservations-2
idxTest = floor(0.7*numObservations)+4:numObservations;
dataTrain = Data(idxTrain);
dataVal = Data(idxval)
dataTest = Data(idxTest);
%trainindx=(1:24)
%validindx=(25:29)
%testindx=(30:34)
traincell2yB = target(idxTrain, :);
valcell2yB = target(idxval, :);
testcell2yB = target(idxTest, :);
options = trainingOptions('rmsprop', ...
'MaxEpochs', 1500, ...
'MiniBatchSize', 50, ...
'InitialLearnRate', 0.00036008553147273947, ...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropPeriod', 125, ...
'LearnRateDropFactor', 0.02, ...
'Shuffle', 'every-epoch', ...
'ValidationData', {dataVal, valcell2yB}, ...
'ValidationFrequency', 50, ...
'Verbose', 1, ...
'Plots', 'training-progress');
% options = trainingOptions('rmsprop', ...
% 'InitialLearnRate', 0.001, ...
% 'MaxEpochs',500, ...
% 'MiniBatchSize',50, ...
% 'Plots','training-progress', 'ValidationData', {valcell1x, valcell1yB});
% options = trainingOptions('adam', ...
% 'InitialLearnRate', 0.001, ...
% 'MaxEpochs',500, ...
% 'MiniBatchSize',50, ...
% 'Plots','training-progress', 'ValidationData', {valcell1x, valcell1yB});
netLSTM1 = trainNetwork(dataTrain, traincell2yB, layers, options);
Here is my data
9 Comments
Cris LaPierre
on 1 May 2024
Sorry, I now understand you are trying to perform sequence-to-sequence regression. That changes some things. You might find this example useful. Sequence to Sequence Regression using Deep Learning
Accepted Answer
Cris LaPierre
on 1 May 2024
I believe the issue is because the sequence length is not the same in each sequence.
There are 2 reasons for this. First, your response vectors are Nx1, but need to be transponsed to 1xN so that the training and response sequences are the same length. Second, one of your sequences has a different response length.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1684246/image.png)
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