How to Multiple output regression

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jaehong kim
jaehong kim on 12 Feb 2021
Edited: KSSV on 21 Sep 2022 at 4:16
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 :)

Answers (1)

Raynier Suresh
Raynier Suresh on 17 Feb 2021
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
  2 Comments
Cheng Qiu
Cheng Qiu on 28 Sep 2021
Edited: KSSV on 21 Sep 2022 at 4:16
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

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