MATLAB error: The output size (4) of the last layer doesn't match the number of classes (2). How to match the size for neural network?

categories={'Dog','Cat'};
rootfolder='C:\Users\njindal\Documents\CNN\CNn\DeepLearningDemos\DeepLearningDemos\train dataset';
imds = imageDatastore(fullfile(rootfolder,categories),...
         'LabelSource','foldernames');
imds.countEachLabel
varSize=32;
 conv1=convolution2dLayer(5,varSize,'Padding',2);
conv1.Weights=gpuArray(single(randn([5 5 3 varSize]) *0.0001));
fc1=fullyConnectedLayer(64);
fc1.Weights=gpuArray(single(randn([64 576]) *0.1));
fc2=fullyConnectedLayer(4);
fc2.Weights=gpuArray(single(randn([4 64]) *0.1));
layers = [imageInputLayer([varSize varSize 3]);
          conv1;
          maxPooling2dLayer(3,'Stride',2);
           reluLayer();
            convolution2dLayer(5,32,'Padding',2);
            reluLayer();
            averagePooling2dLayer(3,'Stride',2);
            convolution2dLayer(5,64,'Padding',2);
           reluLayer();
           averagePooling2dLayer(3,'Stride',2);
           fc1;
           reluLayer();
           fc2;
           softmaxLayer();
          classificationLayer()];
opts = trainingOptions('sgdm',...
       'InitialLearnRate',0.001,...
       'LearnRateSchedule', 'piecewise',...
       'LearnRateDropFactor',0.1,...
       'LearnRateDropPeriod',8,...
       'L2Regularization',0.004,...
       'MaxEpochs',10,...
       'MiniBatchSize',100,...
       'Verbose',true);
[net, info] = trainNetwork(imds,layers,opts);
net.Layers

[Chandani Madnani]

This error usually occurs when a wrong number of classes is specified in the fully connected layer.

Try changing the following line: fc2=fullyConnectedLayer(4); with fc2=fullyConnectedLayer(2);

Answer 1

[progga ilma]

replace fullyConnectedLayer (4) with

fullyConnectedLayer (numClasses)

numClasses = numel (categories (imdsTrain.Labels));

imds = imageDatastore ( 'emotics2another' , ...
    'IncludeSubfolders' , true, ...
    'LabelSource' , 'foldernames' );

% imdsValidation = imageDatastore ('flower_photostest2', ...
% 'IncludeSubfolders', true, ...
% 'LabelSource', 'foldernames'); 
[imdsTrain, imdsValidation] = splitEachLabel (imds, 0.3, 'randomize' );
imdsTrain.ReadFcn = @customreader;
imdsValidation.ReadFcn = @customreader;

% augmentedTrainingSet = augmentedImageDatastore (imdsTrain, 'ColorPreprocessing', 'rgb2gray');% {resized according to image size%}
% 
% augmentedTestSet = augmentedImageDatastore (imdsValidation, 'ColorPreprocessing', 'rgb2gray');
numClasses = numel (categories (imdsTrain.Labels));

layers = [
   imageInputLayer ([200 200 3])
    
    convolution2dLayer (3.8)
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer (2)
    
    convolution2dLayer (3.16)
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer (2)
    
    convolution2dLayer (3.32)
    batchNormalizationLayer
    reluLayer
    
    fullyConnectedLayer (numClasses)
    softmaxLayer
    classificationLayer];

options = trainingOptions ( 'sgdm' , ...
    'InitialLearnRate' , 0.01, ...
    'MaxEpochs' , 4, ...
    'Shuffle' , 'every-epoch' , ...
    'ValidationData' , imdsValidation, ...
    'ValidationFrequency' , 30, ...
    'Verbose' , false, ...
    'Plots' , 'training-progress' );