Multi stream CNN implementation

i have designed CNN and it is trained with dataset stored in XTrain image datastore.

layers = [

imageInputLayer([227 227 3],"Name","imageinput")

.

.

.

averagePooling2dLayer([5 5],"Name","avgpool2d_5","Padding","same","Stride",[2 2])

fullyConnectedLayer(10,"Name","fc")

softmaxLayer("Name","softmax")

classificationLayer("Name","classoutput")];

options = trainingOptions('sgdm', ...
    'LearnRateSchedule','piecewise', ...
    'InitialLearnRate',0.01, ...
    'ValidationFrequency',10,...
    'MaxEpochs',10, ...
    'MiniBatchSize',10, ...
  'ValidationData',{input,testinput},...
    'Plots','training-progress')

[net, traininfo] = trainNetwork(input,layers,options);

YPred = classify(net,testinput);

accuracy = mean(YPred==YTest)

confusion = plotconfusion(YTest,YPred)

I got a good result.

I have to train my CNN with three different datasets namely input_in, input_mid, input_rin. Reference block is attached below.

After last pooling layer i have to concatenate the features. Then fully connect, softmax and classification layer should appear.

i have try like this:

deepnet1=Layer(input_in);

deepnet2=Layer(input_mid);

deepnet3=Layer(input_rin);

deepnet4=createLayerFullyConnect(numHiddenDimension);

function deepnet=createLayerFullyConnect(numHiddenDimension)

layers = [

imageInputLayer([1 numHiddenDimension*3 1],"Name","imageinput","Normalization","none")

fullyConnectedLayer(30,"Name","fc_1")

fullyConnectedLayer(20,"Name","fc_2")

fullyConnectedLayer(10,"Name","fc_3")];

lgraph = layerGraph(layers);

deepnet = dlnetwork(lgraph);

end

I don't know how to train same network with different images and concatenate them after training. Any help is appriciated. Thanks in advnace.

Regards,

Ramasenthil.

[Ankita Jain]

Hey.. did you solve this problem? Any help would be appreciated.