You are getting this error as you are using "extractData" with a traced argument. This would lead to breaking of tracing. You may use the following documentation to know what norms need to be followed within your loss function:
I have tried your code with a different loss function, and this worked:
% Parameter settings
M = 10; % Dimension of input features
N = 50; % Number of samples per class
numEpochs = 100;
learnRate = 0.01;
% Generate example data
X = rand(2*N, M);
X(1:N, :) = X(1:N, :) + 1; % Data for class A
X(N+1:end, :) = X(N+1:end, :) - 1; % Data for class B
% Define the neural network
layers = [
featureInputLayer(M, 'Normalization', 'none')
fullyConnectedLayer(10)
reluLayer
fullyConnectedLayer(3)
];
dlnet = dlnetwork(layerGraph(layers));
% Custom training loop
for epoch = 1:numEpochs
dlX = dlarray(X', 'CB'); % Transpose input data to match network's expected format
Y = forward(dlnet, dlX);
Yact = rand(size(Y));
[gradients, loss] = dlfeval(@modelGradients, dlnet, Y, Yact);
dlnet = dlupdate(@sgdmupdate, dlnet, gradients, learnRate);
disp(['Epoch ' num2str(epoch) ', Loss: ' num2str(extractdata(loss))]);
end
% Testing phase
X_test = rand(N, M); % Assume test data is randomly generated
dlX_test = dlarray(X_test', 'CB'); % Transpose input data to match network's expected format
Y_test = predict(dlnet, dlX_test);
disp('Dimensionality reduction results during testing:');
disp(extractdata(Y_test)');
% Model gradient function
function [gradients, loss] = modelGradients(dlnet, Y, Yact)
loss = mse(Y, Yact);
gradients = dlgradient(loss, dlnet.Learnables);
end
% Update function
function param = sgdmupdate(param, grad, learnRate)
param = param - learnRate * grad;
end
I understand that as your loss function is unsupervised, you are running into issues. You will have to refactor the code accordingly.
Hope this helps!
