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
