How to Multiple output regression

I want to know how to custom regression training loop (multiple output).

I want to get simple code example about custom multiple output regression.

Every ex is about cnn but i just desire DNN Thank u for reading my question :)

Answer 1

[Raynier Suresh]

The below code will give you an example on how to create and train a custom network with multiple regression output.

%% Create the network with multiple output
layers = [imageInputLayer([28 28 1],'Normalization','none','Name','in')
          fullyConnectedLayer(1,'Name','fc1')];
lgraph = layerGraph(layers);
lgraph = addLayers(lgraph,fullyConnectedLayer(1,'Name','fc2'));
lgraph = connectLayers(lgraph,'in','fc2');

figure
plot(lgraph)
dlnet = dlnetwork(lgraph);

%% Training Data
XTrain = rand(28,28,1,50);% Input data (50 images of size 28x28x1)
YTrain1 = randi(10,50,1); % Regression Output data for Output 1
YTrain2 = randi(10,50,1); % Regression Output data for Output 2
dsXTrain = arrayDatastore(XTrain,'IterationDimension',4);
dsYTrain1 = arrayDatastore(YTrain1);
dsYTrain2 = arrayDatastore(YTrain2);

dsTrain = combine(dsXTrain,dsYTrain1,dsYTrain2);

%% Train the Network
numEpochs = 3;
miniBatchSize = 128;
plots = "training-progress";

mbq = minibatchqueue(dsTrain,'MiniBatchSize',miniBatchSize,'MiniBatchFcn', @preprocessData,'MiniBatchFormat',{'SSCB','',''});

if plots == "training-progress"
    figure
    lineLossTrain = animatedline('Color',[0.85 0.325 0.098]);
    ylim([0 inf])
    xlabel("Iteration");ylabel("Loss");grid on
end

trailingAvg = [];
trailingAvgSq = [];

iteration = 0;
start = tic;

% Loop over epochs.
for epoch = 1:numEpochs
    
    % Shuffle data.
    shuffle(mbq)
    
    % Loop over mini-batches
    while hasdata(mbq)
        
        iteration = iteration + 1;
        
        [dlX,dlY1,dlY2] = next(mbq);
                       
        % Evaluate the model gradients, state, and loss using dlfeval and the
        % modelGradients function.
        [gradients,state,loss] = dlfeval(@modelGradients, dlnet, dlX, dlY1, dlY2);
        dlnet.State = state;
        
        % Update the network parameters using the Adam optimizer.
        [dlnet,trailingAvg,trailingAvgSq] = adamupdate(dlnet,gradients,trailingAvg,trailingAvgSq,iteration);
        
        % Display the training progress.
        if plots == "training-progress"
            D = duration(0,0,toc(start),'Format','hh:mm:ss');
            addpoints(lineLossTrain,iteration,double(gather(extractdata(loss))))
            title("Epoch: " + epoch + ", Elapsed: " + string(D))
            drawnow
        end
    end
end

%% Necessary function to train the network

function [gradients,state,loss] = modelGradients(dlnet,dlX,T1,T2)

[dlY1,dlY2,state] = forward(dlnet,dlX,'Outputs',["fc1" "fc2"]);
lossT1 = mse(dlY1,T1);
lossT2 = mse(dlY2,T2);
loss = 0.1*lossT1 + 0.1*lossT2;
gradients = dlgradient(loss,dlnet.Learnables);

end

function [X,Y1,Y2] = preprocessData(XCell,Y1Cell,Y2Cell)
    X = cat(4,XCell{:});
    Y1 = cat(2,Y1Cell{:});
    Y2 = cat(2,Y2Cell{:}); 
    
end

For more information you can refer the below links

https://www.mathworks.com/help/deeplearning/ug/multiple-input-and-multiple-output-networks.html

https://www.mathworks.com/help/deeplearning/ug/train-network-with-multiple-outputs.html

[Cheng Qiu]

I always got error indicating that the the value of the derivative must be a scalar dlarray with trace. How to fix it? Below is my code.

[Cheng Qiu]

layers = [
    imageInputLayer([32 1],'Name','input','Normalization','none')
    fullyConnectedLayer(Nhide,"Name","FC1")
    reluLayer("Name","Relu1")
    fullyConnectedLayer(Nhide,"Name","FC2")
    dropoutLayer(0.5,"Name","DO")
    fullyConnectedLayer(outputSize,"Name","FC3")
    reluLayer('Name','Relu2')];
lgraph = layerGraph(layers);
dlnet = dlnetwork(lgraph);
% Training Option
numEpochs = 1e3;
miniBatchSize = 32;
initialLearnRate = 0.001;
decay = 0.01;
momentum = 0.9;
plots = "training-progress";
executionEnvironment = "auto";
if plots == "training-progress"
    figure
    lineLossTrain = animatedline('Color',[0.85 0.325 0.098]);
    ylim([0 inf])
    xlabel("Iteration")
    ylabel("Loss")
    grid on
end
%% Training The Network
numObservations = numel(output);
numIterationsPerEpoch = floor(numObservations./miniBatchSize);
iteration = 0;
start = tic;
% Loop over epochs.
for epoch = 1:numEpochs
    % Shuffle data.
    idx = randperm(numel(OutPower(:,1)));
    input = input(:,:,:,idx);
    output = output(:,idx);
    % Loop over mini-batches.
    for i = 1:numIterationsPerEpoch
        iteration = iteration + 1;
        % Read mini-batch of data and convert the labels to dummy
        % variables.
        idx = (i-1)*miniBatchSize+1:i*miniBatchSize;
        X = input(:,:,:,idx);
        Y1 = output(1,idx);
        Y2 = output(2,idx);
        Y3 = output(3,idx);
        Y4 = output(4,idx);
        % Convert mini-batch of data to dlarray.
        dlX = dlarray(X,'SSCB');
        dlY1= dlarray(Y1,'SB');
        dlY2= dlarray(Y2,'SB');
        dlY3= dlarray(Y3,'SB');
        dlY4= dlarray(Y4,'SB');
        %         dlY = dlarray(Y,'SSCB');
        % If training on a GPU, then convert data to gpuArray.
        if (executionEnvironment == "auto" && canUseGPU) || executionEnvironment == "gpu"
            dlX = gpuArray(dlX);
        end
        % Evaluate the model gradients, state, and loss using dlfeval and the
        % modelGradients function and update the network state.
        [gradients,state,loss] = dlfeval(@modelGradients,dlnet,dlX,dlY1,dlY2,dlY3,dlY4);
        dlnet.State = state;
        function [gradients,state,loss] = modelGradients(dlnet,dlX,Y1,Y2,Y3,Y4)
        [dlYPred,state] = forward(dlnet,dlX);
        loss = sqrt((dlYPred(1)-Y1).^2+(dlYPred(2)-Y2).^2+(dlYPred(3)-Y3).^2+(dlYPred(4)-Y4).^2)/2;
        gradients = dlgradient(loss,dlnet.Learnables);
        end