Error using trainNetwork (line 191) Too many input arguments.

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Ayat
Ayat 2024-7-31
评论： Ayat 2024-8-16
Hello, i am trying to code an automatic detection of alzheimer from EEG signals but my code has an error when using trainNetwork. It worked perfectely with a SVM but doesn't with a CNN. I tried looking online but nothing seems too work. I got this error :
Error using trainNetwork (line 191)
Too many input arguments.
Error in CNN (line 178)
net = trainNetwork(X_train, y_train, layers, options);
Caused by:
Error using gather
Too many input arguments.
Does anyone have an idea. Here is the part of my code that produce the CNN :
X = all_features{:, 1:end-1};
y = all_features.Label;
y = categorical(y);
disp(['Feature matrix dimensions: ', num2str(size(X))]);
disp(['Labels vector dimensions: ', num2str(size(y))]);
X = zscore(X);
numFeatures = size(X, 2);
numObservations = size(X, 1);
X = reshape(X, [numObservations, numFeatures, 1, 1]);
layers = [
imageInputLayer([numFeatures 1 1])
convolution2dLayer([3 1], 8, 'Padding', 'same')
batchNormalizationLayer
reluLayer
maxPooling2dLayer([2 1], 'Stride', 2)
convolution2dLayer([3 1], 16, 'Padding', 'same')
batchNormalizationLayer
reluLayer
fullyConnectedLayer(2)
softmaxLayer
classificationLayer];
options = trainingOptions('adam', ...
'MaxEpochs', 30, ...
'MiniBatchSize', 16, ...
'InitialLearnRate', 0.001, ...
'ValidationFrequency', 10, ...
'Verbose', false, ...
'Plots', 'training-progress');
cv = cvpartition(y, 'KFold', 5, 'Stratify', true);
accuracies = zeros(cv.NumTestSets, 1);
confusion_matrices = cell(cv.NumTestSets, 1);
for k = 1:cv.NumTestSets
train_idx = training(cv, k);
test_idx = test(cv, k);
X_train = X(train_idx, :, :, :);
y_train = y(train_idx);
X_test = X(test_idx, :, :, :);
y_test = y(test_idx);
net = trainNetwork(X_train, y_train, layers, options);
y_pred = classify(net, X_test);
confusion_matrices{k} = confusionmat(y_test, y_pred);
cm = confusion_matrices{k};
accuracies(k) = sum(diag(cm)) / sum(cm(:));
end
mean_accuracy = mean(accuracies);
fprintf('Mean Accuracy across 5 folds: %.2f%%\n', mean_accuracy * 100);
save('eeg_cnn_classifier_cv.mat', 'net');
disp('Confusion Matrices for each fold:');
for k = 1:cv.NumTestSets
disp(['Fold ', num2str(k), ':']);
disp(confusion_matrices{k});
end

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采纳的回答

Shantanu Dixit
Shantanu Dixit 2024-8-6
编辑：Shantanu Dixit 2024-8-6
Hi Ayat, it seems that you are facing issue while calling the trainNetwork using CNN. The issue lies in how the data transformation is done using reshape.
If you perform an operation (e.g., reshape) that creates trailing singleton (1) dimensions beyond the second dimension, MATLAB will automatically remove those dimensions from the resulting variable.
% Does not change the dimensions of X.
X = reshape(X,[numObservations, numFeatures,1, 1]);
Here reshaping can be done as follows:
X = reshape(X,[numFeatures, 1, 1, numObservations])
Subsequently the way X_train, X_test are accessed needs to be changed.
%% access X_train, X_test as follows
for k = 1:cv.NumTestSets
train_idx = training(cv, k);
test_idx = test(cv, k);
X_train = X(:, :, :, train_idx);
y_train = y(train_idx);
X_test = X(:, :, :, test_idx);
y_test = y(test_idx);
net = trainNetwork(X_train, y_train, layers, options);
y_pred = classify(net, X_test);
confusion_matrices{k} = confusionmat(y_test, y_pred);
cm = confusion_matrices{k};
accuracies(k) = sum(diag(cm)) / sum(cm(:));
end
Refer to the below links from the forum and MathWorks documentation for more information
  1 个评论
Ayat
Ayat 2024-8-16
Hello,
Sorry to answer this late, indeed the code now works with this method. I actually managed to find this alone by experiencing with my code but now i also know the reason why thanks to you. Thanks a lot.

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