willison_ad asked . 2021-04-07

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

[XTrain,~,YTrain] = digitTrain4DArrayData;
[XValidation,~,YValidation] = digitTest4DArrayData;
%Loading in training and validation images 
layers = [
    imageInputLayer([153 365 3]) 
    convolution2dLayer(3,8,'Padding','same') %8 filters of size 3x3 with 'same' padding (also 3x3), no size 
    batchNormalizationLayer %Normalising each input between [0,1]
    reluLayer %Rectified Linear Unit (ReLu), threshold operation to each element of input, where any value less than zero is set to zero
    averagePooling2dLayer(2,'Stride',1) %Pool size 2x2 with Stride/step of 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(32) %Regression problem, FC layer must precede the regression layer at the end. Here at size 1
    regressionLayer];
%Creating layer networks: 4 pairs of convoluting and pooling operations. 
miniBatchSize  = 128;
validationFrequency = floor(numel(YTrain)/miniBatchSize);
options = trainingOptions('sgdm', ...
    'MiniBatchSize',miniBatchSize, ...
    'MaxEpochs',30, ... %Trained for 30 epochs
    'InitialLearnRate',1e-3, ... %Setting inital learn rate to 0.001 
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropFactor',0.1, ...
    'LearnRateDropPeriod',20, ... %Lowering the learn rate by 0.1 after 20 epochs
    'Shuffle','every-epoch', ...
    'ValidationData',{XValidation,YValidation}, ...
    'ValidationFrequency',validationFrequency, ...
    'Plots','training-progress', ...
    'Verbose',false);
%Training the network 
net = trainNetwork(XTrain,YTrain,layers,options);
%Creating the network; this command uses a compatible GPU if possible. 
YPredicted = predict(net,XValidation);
%Test the performance of the network by evaluating the accuracy on the validation data 
predictionError = YValidation - YPredicted;
%Evaluating performance of the model by calculating:
%1. Ther percentage of predictions within an acceptatble error margin
%2. The RMSE of the predicted and actual 
thr = 10; %setting threshold to 10
numCorrect = sum(abs(predictionError) < thr);
numValidationImages = numel(YValidation); 
%calculating no. predictions within acceptable error margin 
accuracy = numCorrect/numValidationImages; %accuracy as a %
squares = predictionError.^2;
rmse = sqrt(mean(squares));
%Calculating RSME 

simulink , matlab , simulation , nural network