Invalid training data for LSTM network.

Hi all,

I am creating a LSTM network and I am getting an error 'Invalid training data. Predictors and responses must have the same number of observations.' I converted the input training data to cell arrays and the output to a categorical one, yet I am getting an error.

Here is a sample of the code:

DataParts = zeros(size(Train1_inputX1,1), size(Train1_inputX1,2),1,2); %(4500,400,1,2)
DataParts(:,:,:,1) = real(cell2mat(Train1_inputX1));
DataParts(:,:,:,2) = imag(cell2mat(Train1_inputX1)) ;
XTrain=num2cell(reshape(DataParts, [400,1,2,4050])); %Train data



DataParts1 = zeros(size(testX1_input,1), size(testX1_input,2),1, 2);
DataParts1(:,:,:,1) = real(cell2mat(testX1_input));
DataParts1(:,:,:,2) = imag(cell2mat(testX1_input)) ;
Ttrain=num2cell(reshape(DataParts1,[400,1,2,500])); %Test data

DataParts2 = zeros(size(ValX1_input,1), size(ValX1_input,2),1, 2);
DataParts2(:,:,:,1) = real(cell2mat(ValX1_input));
DataParts2(:,:,:,2) = imag(cell2mat(ValX1_input));
Vtrain =num2cell(reshape(DataParts2,[400,1,2,450])); %450 is the number of segments  %400 is the number of samples 


 Valoutfinal= categorical(ValX1_output); %450 values
 testoutfinal = categorical(testX1_output); %500 values
 Trainoutfinal= categorical(Train1_outputX1);%4050 values




%% NETWORK ARCHITECTURE

inputSize = [400 1 2];
numHiddenUnits = 800;
numClasses = 4;

layers = [ ...
    sequenceInputLayer(inputSize,'Name','input')
    flattenLayer('Name','flatten')
    bilstmLayer(numHiddenUnits ,'OutputMode','last','Name','lstm')
    fullyConnectedLayer(numClasses , 'Name','fc')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classification')];

% Specify training options.  
maxEpochs = 100;
miniBatchSize = 27;

options = trainingOptions('sgdm', ...
    'ExecutionEnvironment','cpu', ...
    'GradientThreshold',1, ...
    'MaxEpochs',maxEpochs, ...
    'MiniBatchSize',miniBatchSize, ...
    'SequenceLength','longest', ...
    'Shuffle','never', ...
    'Verbose',0, ...
    'Plots','training-progress');

%% Train network
net = trainNetwork(Ttrain,Trainoutfinal,layers,options);

Any help is greatly appreciated.

Thanks a mil.

Answer 1

[Avadhoot]

Hi @tyler seudath,

I understand that you are facing the "Invalid training data" error for your LSTM model. This error suggests that there is a mismatch between the number of observations in the predictors and the responses. After going through your code, I have found the following:

1) The reshaping of "DataParts" into "XTrain", "Ttrain" and "VTrain" looks incorrect. You have reshaped the entire dataset into a single cell. But each sequence or sample must be in a separate cell according to the correct format. You'll need to apply similar corrections to your validation and test sets.

You can take a look at the below code to understand how it must be done:

% Assuming Train1_inputX1 is a cell array where each cell contains a sequence
XTrain = cell(size(Train1_inputX1));
for i = 1:length(Train1_inputX1)
    tempData = Train1_inputX1{i}; % Extract the sequence
    % Assuming each sequence is a 2D matrix where rows are time steps
    tempData = [real(tempData), imag(tempData)]; % Combine real and imaginary parts
    XTrain{i} = tempData.'; % Transpose to make it [Features, TimeSteps]
end

2) Ensure that the number of sequences in your predictors matches the number of labels in your responses. The error suggests there's a mismatch.

After making the above corrections the error must go away, provided the corrected data preparation aligns the number of observations in predictors and responses.

I hope it helps.