Want to add regularization (L2) in Resnet50 code

Hi guys,

I am training my data using Resnet50 in CNN but data is overfit. I want to reduce overfitting. So I want to add Regularizatin L2. Can anybody tell me how to add L2 in my code. You can see my code below.

clear all

close all

imds = imageDatastore("E:\test\data", ...

'IncludeSubfolders',true,'LabelSource','foldernames');

[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomize'); %70% for train 30% for test

net=resnet50; % for the first time,you have to download the package from Add-on explorer

%Replace Final Layers

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

lgraph = layerGraph(net);

newFCLayer = fullyConnectedLayer(numClasses,'Name','new_fc','WeightLearnRateFactor',10,'BiasLearnRateFactor',10);

lgraph = replaceLayer(lgraph,'fc1000' ,newFCLayer);

newClassLayer = classificationLayer('Name','new_classoutput');

lgraph = replaceLayer(lgraph,'ClassificationLayer_predictions',newClassLayer);

%Train Network

inputSize = net.Layers(1).InputSize;

augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain);

augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);

options = trainingOptions('sgdm', ...

'MiniBatchSize',10, ...

'MaxEpochs',20, ...

'InitialLearnRate',1e-3, ...

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

'ValidationData',augimdsValidation, ...

'ValidationFrequency',5, ...

'Verbose',false, ...

'Plots','training-progress');

trainedNet = trainNetwork(augimdsTrain,lgraph,options);

YPred = classify(trainedNet,augimdsValidation);

accuracy = mean(YPred == imdsValidation.Labels)

C = confusionmat(imdsValidation.Labels,YPred)

cm = confusionchart(imdsValidation.Labels,YPred);

cm.Title = 'Confusion Matrix for Validation Data';

cm.ColumnSummary = 'column-normalized';

cm.RowSummary = 'row-normalized';

Answer 1

[Srivardhan Gadila]

You can specify the L2 regularization factors for the weights and biases in Convolutional and Fully Connected Layers by specifying the BiasL2Factor and WeightL2Factor properties, respectively. trainNetwork then multiplies the L2 regularization factors that you specify by using trainingOptions with these factors.

For more informtion refer to setL2Factor, Solver Options, L2 Regularization, Set Up Parameters in Convolutional and Fully Connected Layers.

[Muhammad Zubair]

Thank You so much