Error using trainNetwork . Number of observations in X and Y disagree.

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Good Evening,
I'm trying to train a neural network but "Error using trainNetwork . Number of observations in X and Y disagree." error came out. I am not able to understand where is the problem.
XTrain is a 607x39x367x1 double and loadYTrain is 607x1 double (before training i changed to categorical also.).
can you please help me to resolve this?. its urgent.
Thank you all in advance.
Here is my code:
layers = [
imageInputLayer([39 367 1])
convolution2dLayer(3, 32, 'Padding', 'same')
reluLayer
convolution2dLayer(3, 64, 'Padding', 'same')
reluLayer
maxPooling2dLayer(2, 'Stride', 2)
dropoutLayer(0.2)
fullyConnectedLayer(128)
reluLayer
dropoutLayer(0.2)
fullyConnectedLayer(64)
reluLayer
fullyConnectedLayer(15)
softmaxLayer
classificationLayer];
% Set training options
options = trainingOptions('adam', ...
'MaxEpochs', 20, ...
'MiniBatchSize', 16, ...
'InitialLearnRate', 0.0001, ...
'ValidationData', {test_data, test_labels_categorical}, ...
'Plots', 'training-progress');
train_labels_categorical = categorical(train_labels);
% Train the model
trained_model = trainNetwork(train_data, train_labels_categorical, layers, options);

Accepted Answer

Katja Mogalle
Katja Mogalle on 17 Jul 2023
Hello,
The Deep Learning Toolbox in MATLAB expects in-memory image data to be presented as a h-by-w-by-c-by-N numeric array, where h, w, and c are the height, width, and number of channels of the images, respectively, and N is the number of images.
If I understand your data correctly, your array is currently of size N-by-h-by-w-by-c. You can permute the training and test data to bring them in the correct format as follows:
train_data = permute(train_data,[2,3,4,1]);
I hope this helps.
Katja
  6 Comments
Katja Mogalle
Katja Mogalle on 18 Jul 2023
You mentioned initially, that "XTrain is a 607x39x367x1". Now you say that "x_train=607x14313". So I assume the data was somehow flattened to save it to file, but I don't know how exactly.
You'd need to reshape the data ack into a 4-D array which the convolutional neural network can interpret as height-by-width-by-channels-by-numObservations. But to to this, you need to figure out how the data was flattened. I suspect you'd need one of the following commands to unflatten the data:
x_train = reshape(x_train,[607,39,367,1])
or
x_train = reshape(x_train,[607,367,39,1])
Then, don't forget to permute the observation dimension into the fourth dimension as I showed earlier.
As you are using a 2D convolutional neural network, you have to decide which dimensions of your data represent the two "spatial" dimensions and which ones represents the "channel/feature" dimension. I suspect your data only has one dimension that could be interpreted as "space" (or time). So another option you could have a look at, are 1D CNNs or even recurrent networks. Here is an example showing speech emotion recognition based on gtcc coefficients and which uses a recurrent neural network: https://www.mathworks.com/help/audio/ug/speech-emotion-recognition.html
Gowri Prasood
Gowri Prasood on 18 Jul 2023
thankyou for the help. now i am able to train the network but. validation accuracy is very low and validation loss is very high.

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