Fixing Mismatched Output & Response Size in Last Layer? G

The output size of the last layer does not match the response size?

willison_ad asked: 2021-04-07 simulink , matlab , simulation , nural network

Troubleshooting MATLABs Output size of the last layer does not match the response size error? Our resource provides solutions and debugging tips for deep lea

Expert Answer

Prashant Kumar answered . 2025-07-04 02:55:54

The issue is probably due to the format of the responses of the training data and not w.r.t the convolution filter, padding etc. Also you don't have to make the image dimensions equal and it can be as it is, provided all the input Observations of the training data are having the same shape.

Refer to the documentation of the Input Arguments of the trainNetwork function for the syntax: net = trainNetwork(images,responses,layers,options).

The following code might help you:

inputSize = [153 365 3];
numSamples = 128;
numClasses = 4;
responseSize = 32;
%% Generate random data for training the network.
trainData = randn([inputSize numSamples]);
trainLabels = randn([numSamples responseSize]);

%% Create a network.
layers = [
    imageInputLayer(inputSize) 
    convolution2dLayer(3,8,'Padding','same')
    reluLayer 
    averagePooling2dLayer(2,'Stride',1)
    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer
    averagePooling2dLayer(2,'Stride',1)
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(3,32 ,'Padding','same')
    batchNormalizationLayer
    reluLayer
    dropoutLayer(0.2)
    fullyConnectedLayer(responseSize)
    regressionLayer];

analyzeNetwork(layers);
%% Define training options.
options = trainingOptions('adam', ...
    'InitialLearnRate',0.005, ...
    'LearnRateSchedule','piecewise',...
    'MaxEpochs',1, ...
    'MiniBatchSize',4, ...
    'Verbose',1, ...
    'Plots','training-progress',....
    'ExecutionEnvironment','cpu');

%% Train the network.
net = trainNetwork(trainData,trainLabels,layers,options);

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