the output size of the last layer doesn't match the number of classes

I want to build seven inputs, one output network. (11 classes; 1, 2, ... , 11)

I know I should use combined datastore or transformed datastore type for input.

Also, I made a mat file is already combined all of inputs and label in a cell.

I created 3D image arrays for each input and labels array for training.

The mat file consists of {1x8}; {1x (7 input arrays, 1 categorical label)} in the CombinedCell variable

CombinedCell =

1×8 cell array

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[1]}

Then I load all files using datastore function by following the code.

function [trainingDatastore]=read_datastore(folder)
tempdatastore = datastore(folder,'ReadFcn',@load,'IncludeSubfolders',1,'Type','file','FileExtensions','.mat');
trainingDatastore = transform(tempdatastore,@rearrangeData);
    function out = rearrangeData(ds)
        out = ds.CombinedCell;
    end
end

My network's structure code:

nClasses = 11;
inputSize = [10 9 640 1];
lgraph = layerGraph();
for ind=1:length(Multi_input)
    Input =  [image3dInputLayer(inputSize,"Name",Multi_input{ind})
        convolution3dLayer([1 1 16],32,"Name",[Multi_input{ind} '_conv3d_1'],"Padding","same")
        leakyReluLayer(0.01,"Name",[Multi_input{ind}  '_leaky1'])
        maxPooling3dLayer([1 1 12],"Name",[Multi_input{ind} '_pool1'],"Padding","same")
        averagePooling3dLayer([1 1 64],"Name",[Multi_input{ind}  '_avrpool1'],"Stride",[1 1 32])
        convolution3dLayer([10 9 1],16,"Name",[Multi_input{ind}  '_spa1'],"Padding","same")
        leakyReluLayer(0.01,"Name",[Multi_input{ind}  '_leaky2'])
        maxPooling3dLayer([5 5 1],"Name",[Multi_input{ind}  '_pool2'],"Padding","same")];
    lgraph = addLayers(lgraph,Input);
    clear Input
end
bottom = [
    additionLayer(length(Multi_input),"Name","addition")
    convolution3dLayer([1 1 5],16,"Name",'Combined_conv3d_1',"Stride",1)
    fullyConnectedLayer(inputSize(1)*inputSize(2)*16,"Name","fc1")
    fullyConnectedLayer(nClasses,"Name","fc2")
    softmaxLayer("Name","softmax")
    classificationLayer("Name","classoutput")];
lgraph = addLayers(lgraph,bottom);
for ind=1:length(Multi_input)
    lgraph = connectLayers(lgraph,[Multi_input{ind} '_pool2'],['addition/in' num2str(ind)]);
end

[trainedNet, traininfo]= trainNetwork(trainingDatastore,lgraph,options);

Matlab show an error "the output size (11) of the last layer doesn't match the number of classes (1)

How should I fix the problem?

[Walter Roberson]

You do not show us how you are moving from the datastore returning a cell array, into the form needed by the layers ?

[Youngmin Na]

I used this code to train the network: [trainedNet, traininfo]= trainNetwork(trainingDatastore,lgraph,options);

Answer 1

[Walter Roberson]

A 1x8 cell array at that point codes a single sample with a single data class. Your input must have at least one representative each from all 11 classes.

[Youngmin Na]

I modified my data. A 11x8 cell array at a single sample now. Even the data have all classes samples, the error message was appeared.

What should I do?

11×8 cell array

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[1 ]}

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[2 ]}

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[3 ]}

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[4 ]}

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[5 ]}

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[6 ]}

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[7 ]}

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[8 ]}

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[9 ]}

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[10]}

{10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {10×9×640 double} {[11]}