clc
clear all
outputFolder=fullfile('recycle101');
rootFolder=fullfile(outputFolder,'recycle');
categories={'Aluminium Can','PET Bottles','Drink Carton Box'};
imds=imageDatastore(fullfile(rootFolder,categories),'LabelSource','foldernames');
tbl=countEachLabel(imds)
minSetCount=min(tbl{:,2});
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
countEachLabel(imds);
%randomly choose file for aluminium can, PET bottles, and drink carton box
AluminiumCan=find(imds.Labels=='Aluminium Can',1);
PETBottles=find(imds.Labels=='PET Bottles',1);
DrinkCartonBox=find(imds.Labels=='Drink Carton Box',1);
%plot image that was pick randomly
figure
subplot(2,2,1);
imshow(readimage(imds,AluminiumCan));
subplot(2,2,2);
imshow(readimage(imds,PETBottles));
subplot(2,2,3);
imshow(readimage(imds,DrinkCartonBox));
%Load pre-trained network
net = resnet50;
analyzeNetwork(net)
net = resnet50('Weights','none');
lys = net.Layers;
lys(end-3:end)
numClasses = numel(categories(imdsTrain.Labels));
lgraph = layerGraph(net);
%Replace the classification layers for new task
newFCLayer = fullyConnectedLayer(3,'Name','new_fc','WeightLearnRateFactor',10,'BiasLearnRateFactor',10);
lgraph = replaceLayer(lgraph,'fc1000',newFCLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,'ClassificationLayer_fc1000',newClassLayer);
