Dataset = imageDatastore('Dataset', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');
[Training_Dataset, Validation_Dataset, Testing_Dataset] = splitEachLabel(Dataset, 0.7, 0.15, 0.15);
net = googlenet;
analyzeNetwork(net)
Input_Layer_Size = net.Layers(1).InputSize(1:2);
Resized_Training_Dataset = augmentedImageDatastore(Input_Layer_Size ,Training_Dataset);
Resized_Validation_Dataset = augmentedImageDatastore(Input_Layer_Size ,Validation_Dataset);
Resized_Testing_Dataset = augmentedImageDatastore(Input_Layer_Size, Testing_Dataset);
Feature_Learner = net.Layers(142).Name;
Output_Classifier = net.Layers(144).Name;
Number_of_Classes = numel(categories(Training_Dataset.Labels));
New_Feature_Learner = fullyConnectedLayer(Number_of_Classes, ...
'Name', 'PV Feature Learner', ...
'WeightLearnRateFactor', 10, ...
'BiasLearnRateFactor', 10);
New_Classifier_Layer = classificationLayer('Name', 'PV Classifier');
Network_Architecture = layerGraph(net);
New_Network = replaceLayer(Network_Architecture, Feature_Learner, New_Feature_Learner);
New_Network = replaceLayer(New_Network, Output_Classifier, New_Classifier_Layer);
analyzeNetwork(New_Network)
Minibatch_Size = 4;
Validation_Frequency = floor(numel(Resized_Training_Dataset.Files)/Minibatch_Size);
Training_Options = trainingOptions('sgdm', ...
'MiniBatchSize', Minibatch_Size, ...
'MaxEpochs', 15, ...
'InitialLearnRate', 3e-4, ...
'Shuffle', 'every-epoch', ...
'ValidationData', Resized_Validation_Dataset, ...
'ValidationFrequency', Validation_Frequency, ...
'Verbose', false, ...
'Plots', 'training-progress');
net = trainNetwork(Resized_Training_Dataset, New_Network, Training_Options);
