Cant get concatenationLayer to connect to other layers in my CNN

When I used connectLayers and plot(lgraph) everything is connected properly, but when I use analyzeNetwork, none of my connections seem to have made it through.

The error states "Unconnected input. Each layer input must be connected to the output of another layer. Detected unconnected inputs: input 'in2'.

Codes = imageDatastore('Desktop/Data_Old/Train_Data/Codes','IncludeSubfolders',true);

Eyes = imageDatastore('/Desktop/Data_Old/Train_Data/Eyes','IncludeSubfolders',true);

imds = combine(Eyes,Codes);

miniBatchSize = 64;

Layers = [

%trunk

imageInputLayer([280 320 1],'Normalization','none','Name','Input');

convolution2dLayer(5,64,'Name','conv1_1')%input size: 280x320 output size: 276x316

batchNormalizationLayer('Name','BN1')

reluLayer('Name','conv1')

maxPooling2dLayer(5,'Name','pool1')

%main branch

concatenationLayer(2,2,'Name','cat1')%not sure if 2 is correct expecting 272x624

convolution2dLayer(5,64,'Stride',[2 2],'Name','conv21_1');

batchNormalizationLayer('Name','BN2')

reluLayer('Name','conv21','Name','RL1')

convolution2dLayer(4,64,'Padding',[2 2],'Name','CONV22_1');

batchNormalizationLayer('Name','BN3')

reluLayer('Name','conv22')

convolution2dLayer(4,64,'Padding',[2 2],'Name','CONV23_1');

batchNormalizationLayer('Name','BN4')

reluLayer('Name','conv23')

convolution2dLayer(5,64,'Stride',[2 2],'Name','CONV24_1');

batchNormalizationLayer('Name','BN5')

reluLayer('Name','conv24')

convolution2dLayer(2,64,'Stride',[2 1],'Name','CONV25_1');

batchNormalizationLayer('Name','BN6')

reluLayer('Name','conv25')

transposedConv2dLayer(2,64,'Stride',[1 2],'Name','CONV26_1');

batchNormalizationLayer('Name','BN7')

reluLayer('Name','conv26')

transposedConv2dLayer(2,64,'Stride',[1 2],'Name','CONV27_1');

batchNormalizationLayer('Name','BN8')

reluLayer('Name','conv27')

transposedConv2dLayer([2 50],64,'Name','CONV28_1');

batchNormalizationLayer('Name','BN9')

reluLayer('Name','conv28')

transposedConv2dLayer([2 50],64,'Name','CONMV29_1');

batchNormalizationLayer('Name','BN10')

reluLayer('Name','conv29')

%Branch1

convolution2dLayer(5,64,'Name','convB11');

reluLayer('Name','rl2');

batchNormalizationLayer('Name','BN11')

maxPooling2dLayer(2,'Padding',[1 1],'Stride',[2 2],'Name','poolB1')

concatenationLayer(2,2,'Name','cat2')%not sure if 2 is correct expecting 134x308

convolution2dLayer([3 3],64,'Padding',[2 3],'Stride',[1 2],'Name','CONVB12_1');

batchNormalizationLayer('Name','BN12')

reluLayer('Name','convB12')

transposedConv2dLayer(2,64,'Stride',[2 2],'Name','DECONVB11_1')

batchNormalizationLayer('Name','BN13')

reluLayer('Name','deconvB11')

%Branch2

convolution2dLayer(5, 64,'Name','convB21');

batchNormalizationLayer('Name','BN14')

reluLayer('Name','RL3');

maxPooling2dLayer(4,'Padding',[1 1],'Stride',[2 2],'Name','poolB2')

concatenationLayer(2,2,'Name','cat3') %not sure if 2 is correct expecting 64x148

convolution2dLayer([4 3],64,'Padding',[3 4],'Stride',[1 2],'Name','CONVB22_1');

batchNormalizationLayer('Name','BN15')

reluLayer('Name','convB22')

transposedConv2dLayer(2,64,'Stride',[2 2],'Name','DECONVB21_1');

batchNormalizationLayer('Name','BN16')

reluLayer('Name','deconvB21');

%Branch3

convolution2dLayer(5,64,'Name','convB31');

batchNormalizationLayer('Name','BN17')

reluLayer('Name','RL4');

maxPooling2dLayer(4,'Padding',[1 1],'Stride',[2 2],'Name','poolB3')

convolution2dLayer([5 4],64,'Padding',[4 3],'Name','CONVB32_1');

batchNormalizationLayer('Name','BN18')

reluLayer('Name','convB32')

transposedConv2dLayer(2,64,'Stride',[2 2],'Name','DECONVB31_1')

batchNormalizationLayer('Name','BN19')

reluLayer('Name','deconvB31')

%Output

transposedConv2dLayer([2 19],64,'Name','DECONV_1');

batchNormalizationLayer('Name','BN20')

reluLayer('Name','deconv1')

regressionLayer('Name','Output')

];

lgraph = layerGraph(Layers);

lgraph = disconnectLayers(lgraph,'conv29','convB11');

lgraph = disconnectLayers(lgraph,'deconvB31','DECONV_1');

lgraph = disconnectLayers(lgraph,'deconv1','convB11');

lgraph = disconnectLayers(lgraph,'deconvB11','convB21');

lgraph = disconnectLayers(lgraph,'deconvB21','convB31');

lgraph = connectLayers(lgraph,'pool1','convB11');

lgraph = connectLayers(lgraph,'poolB1', 'convB21');

lgraph = connectLayers(lgraph,'poolB2', 'convB31');

lgraph = connectLayers(lgraph,'conv29','DECONV_1');

lgraph = connectLayers(lgraph,'deconvB11','cat1/in2');

lgraph = connectLayers(lgraph,'deconvB21','cat2/in2');

lgraph = connectLayers(lgraph,'deconvB31','cat3/in2');

plot(lgraph)

analyzeNetwork(Layers)

% options = trainingOptions('adam', ...

% 'InitialLearnRate',3e-4, ...

% 'MaxEpochs',5, ...

% 'Shuffle','every-epoch', ...

% 'Verbose',false, ...

% 'ExecutionEnvironment','parallel',...

% 'Plots','training-progress');

%

% net_RBDN = trainNetwork(imds , Layers, options);

% save(net_RBDN);

output from plot(lgraph)

output from analyzeNetwork

Answer 1

[Frank Mosely]

Turns out that when you modify your network the arguments to trainNetwork and analyzeNetwork change. whomp.

So above, I should have said analyzeNetwork(lgraph)