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

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}; % Use parentheses () for table indexing
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]); % Reshape for CNN

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’);
% 5-Fold Cross-Validation
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});
endHello, 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}; % Use parentheses () for table indexing
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]); % Reshape for CNN

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 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}; % Use parentheses () for table indexing
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]); % Reshape for CNN

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 cnn, machine learning, deep learning MATLAB Answers — New Questions

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