updated matlab code

source/matlab/resnet50_custom.m

@@ -0,0 +1,188 @@
+function resnet50_custom()
+    %%Load pre-trained network model
+    net = resnet50;
+    lgraph = layerGraph(net);
+
+    %%Load DataSet
+    imds = imageDatastore('/Users/somdipdey/Downloads/Motorway-Traffic-Categorised', ...
+        'IncludeSubfolders',true, ...
+        'LabelSource','foldernames');
+
+    %Split DataSet into two sets: Training & Validation. Here Split is done on
+    %9:1 ratio.
+    [imdsTrain,imdsValidation] = splitEachLabel(imds,0.75,'randomized');
+
+    %Select 9 random image numbers to show
+    numTrainImages = numel(imdsTrain.Labels);
+    idx = randperm(numTrainImages,9);
+    figure
+
+    %Show images of 9 randoms
+    for i = 1:9
+        subplot(3,3,i)
+        I = readimage(imdsTrain,idx(i));
+        imshow(I);
+        title(char(imdsTrain.Labels(idx(i))));
+    end
+
+    %Get inputSize of images
+    inputSize = net.Layers(1).InputSize;
+
+    %Remove last layer (3 layers from array)
+    lgraph = removeLayers(lgraph, {'fc1000','fc1000_softmax','ClassificationLayer_fc1000'});
+
+    %Fetch num of classes
+    numClasses = numel(categories(imdsTrain.Labels));
+
+    %Add a fully connected layer, a dropout layer, a softmax layer
+    newlayers = [
+        fullyConnectedLayer(numClasses,'WeightLearnRateFactor',20,'BiasLearnRateFactor',20,'Name','lastFCCustom')
+        dropoutLayer(0.5,'Name','DROPOUT')
+        softmaxLayer('Name','softmax')
+        classificationLayer('Name','classoutput')];
+
+    lgraph = addLayers(lgraph,newlayers);
+    lgraph = connectLayers(lgraph,'avg_pool','lastFCCustom');
+
+    layers = lgraph.Layers;
+
+    connections = lgraph.Connections;
+
+    layers(1:111) = freezeWeights(layers(1:111));
+    lgraph = createLgraphUsingConnections(layers,connections);
+
+    %Display extracted feature of the last fully connected layer, which is
+    %custom built on 4 categories -->
+    %last_layer = 39;
+    %last_layer_name = net.Layers(last_layer).Name;
+    %visual_channels = 1:6;
+    %thisI = deepDreamImage(net,last_layer,visual_channels, ...
+    %    'PyramidLevels',1, ...
+    %    'NumIterations',28);
+    %figure;
+    %montage(thisI);
+    %title(['Layer: ',last_layer_name,' Features'])
+
+    %%Train network
+    % Augment dataset to prevent network from overfitting and memorizing
+    % training images
+    pixelRange = [-30 30];
+    imageAugmenter = imageDataAugmenter( ...
+        'RandXReflection',true, ...
+        'RandXTranslation',pixelRange, ...
+        'RandYTranslation',pixelRange);
+
+    %Augment training dataset images along with resizing
+    augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain, ...
+        'DataAugmentation',imageAugmenter);
+
+    %Just resize the validation dataset
+    augimdsValidation = augmentedImageDatastore(inputSize(1:2),imdsValidation);
+
+    %Specify training options
+    options = trainingOptions('sgdm', ...
+        'MiniBatchSize',10, ...
+        'MaxEpochs',10, ...
+        'InitialLearnRate',1e-4, ...
+        'ValidationData',augimdsValidation, ...
+        'ValidationFrequency',3, ...
+        'ValidationPatience',Inf, ...
+        'Verbose',false, ...
+        'ExecutionEnvironment','cpu', ...
+        'Plots','training-progress');
+
+    %Finally start training
+    netTransfer = trainNetwork(augimdsTrain,lgraph,options);
+
+    %%Classify Validation Images
+    [YPred,scores] = classify(netTransfer,augimdsValidation);
+    %Display Predication and it's score
+    fprintf('Prediction: %s' , YPred , ', Scores: ');
+    disp(scores);
+    fprintf('\n');
+
+    %%Display nine sample validation images with their predicted labels
+    idx = randperm(numel(imdsValidation.Files),9);
+    figure
+    for i = 1:9
+        subplot(3,3,i)
+        I = readimage(imdsValidation,idx(i));
+        imshow(I)
+        label = YPred(idx(i));
+        title(string(label));
+    end
+
+    %%Calculate the classification accuracy on the validation set. Accuracy is the fraction of labels that the network predicts correctly
+    YValidation = imdsValidation.Labels;
+    accuracy = mean(YPred == YValidation);
+    %Display Accuracy
+    fprintf('Accuracy: %s' , accuracy);
+
+    %Test classification on a completely different set
+    %Just resize the validation dataset
+        %%Load DataSet
+    test_imds = imageDatastore('/Users/somdipdey/Documents/MATLAB/Add-Ons/Collections/Deep Learning Tutorial Series/code/AHS/test_dataset', ...
+        'IncludeSubfolders',true, ...
+        'LabelSource','foldernames');
+    test_augimds = augmentedImageDatastore(inputSize(1:2),test_imds);
+    %testing dataset if not needed
+    %%Classify Test Images
+    %[YPred2,scores2] = classify(netTransfer,test_augimdsValidation);% use if augmented
+    %test dataset is used for testing
+    [YPred2,scores2] = classify(netTransfer,test_augimds);
+    %Display Predication and it's score
+    fprintf('\n Testing on a completely new dataset \n');
+    fprintf('Prediction: %s' , YPred2 , ', Scores: ');
+    disp(scores2);
+    fprintf('\n');
+    %%Calculate the classification accuracy on the validation set. Accuracy is the fraction of labels that the network predicts correctly
+    test_YValidation = test_imds.Labels;
+    accuracy2 = mean(YPred2 == test_YValidation);
+    %Display Accuracy
+    fprintf('\n Accuracy: %s \n' , accuracy2);
+
+    %%Display nine sample validation images with their predicted labels
+    idx2 = randperm(numel(test_imds.Files),9);
+    figure
+    for i = 1:9
+        subplot(3,3,i)
+        I = readimage(test_imds,idx2(i));
+        imshow(I)
+        label = YPred2(idx2(i));
+        title(string(label));
+    end
+end
+
+% layers = freezeWeights(layers) sets the learning rates of all the
+% parameters of the layers in the layer array |layers| to zero.
+
+function layers = freezeWeights(layers)
+
+for ii = 1:size(layers,1)
+    props = properties(layers(ii));
+    for p = 1:numel(props)
+        propName = props{p};
+        if ~isempty(regexp(propName, 'LearnRateFactor$', 'once'))
+            layers(ii).(propName) = 0;
+        end
+    end
+end
+
+end
+
+% lgraph = createLgraphUsingConnections(layers,connections) creates a layer
+% graph with the layers in the layer array |layers| connected by the
+% connections in |connections|.
+
+function lgraph = createLgraphUsingConnections(layers,connections)
+
+lgraph = layerGraph();
+for i = 1:numel(layers)
+    lgraph = addLayers(lgraph,layers(i));
+end
+
+for c = 1:size(connections,1)
+    lgraph = connectLayers(lgraph,connections.Source{c},connections.Destination{c});
+end
+
+end