How can I train multi-input deep network without DataStore

Prashant Kumar answered . 2024-12-21 15:42:47

data = read(ds)
data =

  4×3 cell array

    {28×28 double}    {128×128 double}    {[7]}
    {28×28 double}    {128×128 double}    {[7]}
    {28×28 double}    {128×128 double}    {[9]}
    {28×28 double}    {128×128 double}    {[9]}

%% Generate random training data for muliple input network
imgDat = randn([28 28 3 200]);
vectorData = randn([10 1 200]);
trainLabels = categorical(randi([0 1], 200,1));

%% Convert trianing data into cell arrays
imgCells = mat2cell(imgDat,28,28,3,ones(200,1));
vectorCells = mat2cell(vectorData,10,1,ones(200,1));
imgCells = reshape(imgCells,[200 1 1]);
vectorCells = reshape(vectorCells,[200 1 1]);
labelCells = arrayfun(@(x)x,trainLabels,'UniformOutput',false);
combinedCells = [imgCells vectorCells labelCells];

%% Save the converted data so that it can be loaded by filedatastore
save('traingData.mat','combinedCells');

filedatastore = fileDatastore('traingdata.mat','ReadFcn',@load);
trainingDatastore = transform(filedatastore,@rearrangeData);

%% Define muliple input network
layers1 = [
    imageInputLayer([28 28 3],'Name','input')  
    convolution2dLayer(3,16,'Padding','same','Name','conv_1')
    batchNormalizationLayer('Name','BN_1')
    reluLayer('Name','relu_1')
    fullyConnectedLayer(10,'Name','fc11')
    additionLayer(2,'Name','add')
    fullyConnectedLayer(2,'Name','fc12')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classOutput')];

lgraph = layerGraph(layers1);

layers2 = [imageInputLayer([10 1],'Name','vinput')
    fullyConnectedLayer(10,'Name','fc21')];

lgraph = addLayers(lgraph,layers2);
lgraph = connectLayers(lgraph,'fc21','add/in2');
plot(lgraph)

%% Define trainingOptions and also set 'Shuffle' to 'never' for this workaround to work
options = trainingOptions('adam', ...
    'InitialLearnRate',0.005, ...
    'LearnRateSchedule','piecewise',...
    'MaxEpochs',300, ...
    'MiniBatchSize',1024, ...
    'Verbose',1, ...
    'Plots','training-progress',...
    'Shuffle','never');

net = trainNetwork(trainingDatastore,lgraph,options);

%% function to be used to transform the filedatastore 
%to ensure the read(datastore) returns M-by-3 cell array ie., (numInputs+1) columns
function out = rearrangeData(ds)
out = ds.combinedCells;
end

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