Number of layers: 71

Number of connections: 78

Training setup file: “params.mat”

This script is run to create the network layers, import training and validation data, and train the network.

➀ Load Training Setup Data:

trainingSetup = load(“params.mat”)

➁ Import Data:

imdsTrain = trainingSetup.imdsTrain;

imdsValidation = trainingSetup.imdsValidation;

% Resize the images to match the network input layer.

augimdsTrain = augmentedImageDatastore([224 224 3],imdsTrain);

augimdsValidation = augmentedImageDatastore([224 224 3],imdsValidation);

➂ Set Training Options:

opts = trainingOptions(“sgdm”,…

“ExecutionEnvironment”,“auto”,…

“InitialLearnRate”,0.001,…

“MaxEpochs”,5,…

“Shuffle”,“every-epoch”,…

“Plots”,“training-progress”,…

“ValidationData”,augimdsValidation);

➃ Create Layer Graph:

lgraph = layerGraph();

Add Layer Branches:

tempLayers = [

imageInputLayer([224 224 3],“Name”,“data”,“Normalization”,“zscore”,“Mean”,trainingSetup.data.Mean,“StandardDeviation”,trainingSetup.data.StandardDeviation)

convolution2dLayer([7 7],64,“Name”,“conv1”,“BiasLearnRateFactor”,0,“Padding”,[3 3 3 3],“Stride”,[2 2],“Bias”,trainingSetup.conv1.Bias,“Weights”,trainingSetup.conv1.Weights)

batchNormalizationLayer(“Name”,“bn_conv1”,“Offset”,trainingSetup.bn_conv1.Offset,“Scale”,trainingSetup.bn_conv1.Scale,“TrainedMean”,trainingSetup.bn_conv1.TrainedMean,“TrainedVariance”,trainingSetup.bn_conv1.TrainedVariance)

reluLayer(“Name”,“conv1_relu”)

maxPooling2dLayer([3 3],“Name”,“pool1”,“Padding”,[1 1 1 1],“Stride”,[2 2])];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],64,“Name”,“res2a_branch2a”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res2a_branch2a.Bias,“Weights”,trainingSetup.res2a_branch2a.Weights)

batchNormalizationLayer(“Name”,“bn2a_branch2a”,“Offset”,trainingSetup.bn2a_branch2a.Offset,“Scale”,trainingSetup.bn2a_branch2a.Scale,“TrainedMean”,trainingSetup.bn2a_branch2a.TrainedMean,“TrainedVariance”,trainingSetup.bn2a_branch2a.TrainedVariance)

reluLayer(“Name”,“res2a_branch2a_relu”)

convolution2dLayer([3 3],64,“Name”,“res2a_branch2b”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res2a_branch2b.Bias,“Weights”,trainingSetup.res2a_branch2b.Weights)

batchNormalizationLayer(“Name”,“bn2a_branch2b”,“Offset”,trainingSetup.bn2a_branch2b.Offset,“Scale”,trainingSetup.bn2a_branch2b.Scale,“TrainedMean”,trainingSetup.bn2a_branch2b.TrainedMean,“TrainedVariance”,trainingSetup.bn2a_branch2b.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

additionLayer(2,“Name”,“res2a”)

reluLayer(“Name”,“res2a_relu”)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],64,“Name”,“res2b_branch2a”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res2b_branch2a.Bias,“Weights”,trainingSetup.res2b_branch2a.Weights)

batchNormalizationLayer(“Name”,“bn2b_branch2a”,“Offset”,trainingSetup.bn2b_branch2a.Offset,“Scale”,trainingSetup.bn2b_branch2a.Scale,“TrainedMean”,trainingSetup.bn2b_branch2a.TrainedMean,“TrainedVariance”,trainingSetup.bn2b_branch2a.TrainedVariance)

reluLayer(“Name”,“res2b_branch2a_relu”)

convolution2dLayer([3 3],64,“Name”,“res2b_branch2b”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res2b_branch2b.Bias,“Weights”,trainingSetup.res2b_branch2b.Weights)

batchNormalizationLayer(“Name”,“bn2b_branch2b”,“Offset”,trainingSetup.bn2b_branch2b.Offset,“Scale”,trainingSetup.bn2b_branch2b.Scale,“TrainedMean”,trainingSetup.bn2b_branch2b.TrainedMean,“TrainedVariance”,trainingSetup.bn2b_branch2b.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

additionLayer(2,“Name”,“res2b”)

reluLayer(“Name”,“res2b_relu”)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],128,“Name”,“res3a_branch2a”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Stride”,[2 2],“Bias”,trainingSetup.res3a_branch2a.Bias,“Weights”,trainingSetup.res3a_branch2a.Weights)

batchNormalizationLayer(“Name”,“bn3a_branch2a”,“Offset”,trainingSetup.bn3a_branch2a.Offset,“Scale”,trainingSetup.bn3a_branch2a.Scale,“TrainedMean”,trainingSetup.bn3a_branch2a.TrainedMean,“TrainedVariance”,trainingSetup.bn3a_branch2a.TrainedVariance)

reluLayer(“Name”,“res3a_branch2a_relu”)

convolution2dLayer([3 3],128,“Name”,“res3a_branch2b”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res3a_branch2b.Bias,“Weights”,trainingSetup.res3a_branch2b.Weights)

batchNormalizationLayer(“Name”,“bn3a_branch2b”,“Offset”,trainingSetup.bn3a_branch2b.Offset,“Scale”,trainingSetup.bn3a_branch2b.Scale,“TrainedMean”,trainingSetup.bn3a_branch2b.TrainedMean,“TrainedVariance”,trainingSetup.bn3a_branch2b.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],128,“Name”,“res3a_branch1”,“BiasLearnRateFactor”,0,“Stride”,[2 2],“Bias”,trainingSetup.res3a_branch1.Bias,“Weights”,trainingSetup.res3a_branch1.Weights)

batchNormalizationLayer(“Name”,“bn3a_branch1”,“Offset”,trainingSetup.bn3a_branch1.Offset,“Scale”,trainingSetup.bn3a_branch1.Scale,“TrainedMean”,trainingSetup.bn3a_branch1.TrainedMean,“TrainedVariance”,trainingSetup.bn3a_branch1.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

additionLayer(2,“Name”,“res3a”)

reluLayer(“Name”,“res3a_relu”)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],128,“Name”,“res3b_branch2a”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res3b_branch2a.Bias,“Weights”,trainingSetup.res3b_branch2a.Weights)

batchNormalizationLayer(“Name”,“bn3b_branch2a”,“Offset”,trainingSetup.bn3b_branch2a.Offset,“Scale”,trainingSetup.bn3b_branch2a.Scale,“TrainedMean”,trainingSetup.bn3b_branch2a.TrainedMean,“TrainedVariance”,trainingSetup.bn3b_branch2a.TrainedVariance)

reluLayer(“Name”,“res3b_branch2a_relu”)

convolution2dLayer([3 3],128,“Name”,“res3b_branch2b”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res3b_branch2b.Bias,“Weights”,trainingSetup.res3b_branch2b.Weights)

batchNormalizationLayer(“Name”,“bn3b_branch2b”,“Offset”,trainingSetup.bn3b_branch2b.Offset,“Scale”,trainingSetup.bn3b_branch2b.Scale,“TrainedMean”,trainingSetup.bn3b_branch2b.TrainedMean,“TrainedVariance”,trainingSetup.bn3b_branch2b.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

additionLayer(2,“Name”,“res3b”)

reluLayer(“Name”,“res3b_relu”)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],256,“Name”,“res4a_branch2a”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Stride”,[2 2],“Bias”,trainingSetup.res4a_branch2a.Bias,“Weights”,trainingSetup.res4a_branch2a.Weights)

batchNormalizationLayer(“Name”,“bn4a_branch2a”,“Offset”,trainingSetup.bn4a_branch2a.Offset,“Scale”,trainingSetup.bn4a_branch2a.Scale,“TrainedMean”,trainingSetup.bn4a_branch2a.TrainedMean,“TrainedVariance”,trainingSetup.bn4a_branch2a.TrainedVariance)

reluLayer(“Name”,“res4a_branch2a_relu”)

convolution2dLayer([3 3],256,“Name”,“res4a_branch2b”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res4a_branch2b.Bias,“Weights”,trainingSetup.res4a_branch2b.Weights)

batchNormalizationLayer(“Name”,“bn4a_branch2b”,“Offset”,trainingSetup.bn4a_branch2b.Offset,“Scale”,trainingSetup.bn4a_branch2b.Scale,“TrainedMean”,trainingSetup.bn4a_branch2b.TrainedMean,“TrainedVariance”,trainingSetup.bn4a_branch2b.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],256,“Name”,“res4a_branch1”,“BiasLearnRateFactor”,0,“Stride”,[2 2],“Bias”,trainingSetup.res4a_branch1.Bias,“Weights”,trainingSetup.res4a_branch1.Weights)

batchNormalizationLayer(“Name”,“bn4a_branch1”,“Offset”,trainingSetup.bn4a_branch1.Offset,“Scale”,trainingSetup.bn4a_branch1.Scale,“TrainedMean”,trainingSetup.bn4a_branch1.TrainedMean,“TrainedVariance”,trainingSetup.bn4a_branch1.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

additionLayer(2,“Name”,“res4a”)

reluLayer(“Name”,“res4a_relu”)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],256,“Name”,“res4b_branch2a”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res4b_branch2a.Bias,“Weights”,trainingSetup.res4b_branch2a.Weights)

batchNormalizationLayer(“Name”,“bn4b_branch2a”,“Offset”,trainingSetup.bn4b_branch2a.Offset,“Scale”,trainingSetup.bn4b_branch2a.Scale,“TrainedMean”,trainingSetup.bn4b_branch2a.TrainedMean,“TrainedVariance”,trainingSetup.bn4b_branch2a.TrainedVariance)

reluLayer(“Name”,“res4b_branch2a_relu”)

convolution2dLayer([3 3],256,“Name”,“res4b_branch2b”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res4b_branch2b.Bias,“Weights”,trainingSetup.res4b_branch2b.Weights)

batchNormalizationLayer(“Name”,“bn4b_branch2b”,“Offset”,trainingSetup.bn4b_branch2b.Offset,“Scale”,trainingSetup.bn4b_branch2b.Scale,“TrainedMean”,trainingSetup.bn4b_branch2b.TrainedMean,“TrainedVariance”,trainingSetup.bn4b_branch2b.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

additionLayer(2,“Name”,“res4b”)

reluLayer(“Name”,“res4b_relu”)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],512,“Name”,“res5a_branch2a”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Stride”,[2 2],“Bias”,trainingSetup.res5a_branch2a.Bias,“Weights”,trainingSetup.res5a_branch2a.Weights)

batchNormalizationLayer(“Name”,“bn5a_branch2a”,“Offset”,trainingSetup.bn5a_branch2a.Offset,“Scale”,trainingSetup.bn5a_branch2a.Scale,“TrainedMean”,trainingSetup.bn5a_branch2a.TrainedMean,“TrainedVariance”,trainingSetup.bn5a_branch2a.TrainedVariance)

reluLayer(“Name”,“res5a_branch2a_relu”)

convolution2dLayer([3 3],512,“Name”,“res5a_branch2b”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res5a_branch2b.Bias,“Weights”,trainingSetup.res5a_branch2b.Weights)

batchNormalizationLayer(“Name”,“bn5a_branch2b”,“Offset”,trainingSetup.bn5a_branch2b.Offset,“Scale”,trainingSetup.bn5a_branch2b.Scale,“TrainedMean”,trainingSetup.bn5a_branch2b.TrainedMean,“TrainedVariance”,trainingSetup.bn5a_branch2b.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([1 1],512,“Name”,“res5a_branch1”,“BiasLearnRateFactor”,0,“Stride”,[2 2],“Bias”,trainingSetup.res5a_branch1.Bias,“Weights”,trainingSetup.res5a_branch1.Weights)

batchNormalizationLayer(“Name”,“bn5a_branch1”,“Offset”,trainingSetup.bn5a_branch1.Offset,“Scale”,trainingSetup.bn5a_branch1.Scale,“TrainedMean”,trainingSetup.bn5a_branch1.TrainedMean,“TrainedVariance”,trainingSetup.bn5a_branch1.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

additionLayer(2,“Name”,“res5a”)

reluLayer(“Name”,“res5a_relu”)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

convolution2dLayer([3 3],512,“Name”,“res5b_branch2a”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res5b_branch2a.Bias,“Weights”,trainingSetup.res5b_branch2a.Weights)

batchNormalizationLayer(“Name”,“bn5b_branch2a”,“Offset”,trainingSetup.bn5b_branch2a.Offset,“Scale”,trainingSetup.bn5b_branch2a.Scale,“TrainedMean”,trainingSetup.bn5b_branch2a.TrainedMean,“TrainedVariance”,trainingSetup.bn5b_branch2a.TrainedVariance)

reluLayer(“Name”,“res5b_branch2a_relu”)

convolution2dLayer([3 3],512,“Name”,“res5b_branch2b”,“BiasLearnRateFactor”,0,“Padding”,[1 1 1 1],“Bias”,trainingSetup.res5b_branch2b.Bias,“Weights”,trainingSetup.res5b_branch2b.Weights)

batchNormalizationLayer(“Name”,“bn5b_branch2b”,“Offset”,trainingSetup.bn5b_branch2b.Offset,“Scale”,trainingSetup.bn5b_branch2b.Scale,“TrainedMean”,trainingSetup.bn5b_branch2b.TrainedMean,“TrainedVariance”,trainingSetup.bn5b_branch2b.TrainedVariance)];

lgraph = addLayers(lgraph,tempLayers);

tempLayers = [

additionLayer(2,“Name”,“res5b”)

reluLayer(“Name”,“res5b_relu”)

globalAveragePooling2dLayer(“Name”,“pool5”)

fullyConnectedLayer(2,“Name”,“fc”)

softmaxLayer(“Name”,“prob”)

classificationLayer(“Name”,“classoutput”)];

lgraph = addLayers(lgraph,tempLayers);

% clean up helper variable

clear tempLayers;

➄ Connect Layer Branches:

lgraph = connectLayers(lgraph,“pool1”,“res2a_branch2a”);

lgraph = connectLayers(lgraph,“pool1”,“res2a/in2”);

lgraph = connectLayers(lgraph,“bn2a_branch2b”,“res2a/in1”);

lgraph = connectLayers(lgraph,“res2a_relu”,“res2b_branch2a”);

lgraph = connectLayers(lgraph,“res2a_relu”,“res2b/in2”);

lgraph = connectLayers(lgraph,“bn2b_branch2b”,“res2b/in1”);

lgraph = connectLayers(lgraph,“res2b_relu”,“res3a_branch2a”);

lgraph = connectLayers(lgraph,“res2b_relu”,“res3a_branch1”);

lgraph = connectLayers(lgraph,“bn3a_branch2b”,“res3a/in1”);

lgraph = connectLayers(lgraph,“bn3a_branch1”,“res3a/in2”);

lgraph = connectLayers(lgraph,“res3a_relu”,“res3b_branch2a”);

lgraph = connectLayers(lgraph,“res3a_relu”,“res3b/in2”);

lgraph = connectLayers(lgraph,“bn3b_branch2b”,“res3b/in1”);

lgraph = connectLayers(lgraph,“res3b_relu”,“res4a_branch2a”);

lgraph = connectLayers(lgraph,“res3b_relu”,“res4a_branch1”);

lgraph = connectLayers(lgraph,“bn4a_branch2b”,“res4a/in1”);

lgraph = connectLayers(lgraph,“bn4a_branch1”,“res4a/in2”);

lgraph = connectLayers(lgraph,“res4a_relu”,“res4b_branch2a”);

lgraph = connectLayers(lgraph,“res4a_relu”,“res4b/in2”);

lgraph = connectLayers(lgraph,“bn4b_branch2b”,“res4b/in1”);

lgraph = connectLayers(lgraph,“res4b_relu”,“res5a_branch2a”);

lgraph = connectLayers(lgraph,“res4b_relu”,“res5a_branch1”);

lgraph = connectLayers(lgraph,“bn5a_branch2b”,“res5a/in1”);

lgraph = connectLayers(lgraph,“bn5a_branch1”,“res5a/in2”);

lgraph = connectLayers(lgraph,“res5a_relu”,“res5b_branch2a”);

lgraph = connectLayers(lgraph,“res5a_relu”,“res5b/in2”);

lgraph = connectLayers(lgraph,“bn5b_branch2b”,“res5b/in1”);

➅ Train Network:

[net, traininfo] = trainNetwork(augimdsTrain,lgraph,opts);

Additional code

➆ Grad-CAM:

label = classify(trainedNetwork_1,Image)

scoreMap = gradCAM(trainedNetwork_1,Image,label);

figure

imshow(Image)

hold on

imagesc(scoreMap,‘AlphaData’,0.5)

colormap jet

➇ ImageLIME:

label = classify(trainedNetwork_1,Image)

scoreMap = imageLIME(trainedNetwork_1,Image,label);

figure

imshow(Image)

hold on

imagesc(scoreMap,‘AlphaData’,0.5)

colormap jet

➈ Occlusion sensitivity:

label = classify(trainedNetwork_1,Image)

scoreMap = occlusionSensitivity(trainedNetwork_1,Image,label);

figure

imshow(Image)

hold on

imagesc(scoreMap,‘AlphaData’,0.5)

colormap jet