81 lines
2.1 KiB
Mathematica
81 lines
2.1 KiB
Mathematica
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clear all;clc
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%% Only modify the codes in the Define Network Architecture section.
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%% Load and Explore Image Data
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% training data
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digitDatasetPath = 'optdigits_train';
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imds_train = imageDatastore(digitDatasetPath, ...
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'IncludeSubfolders',true,'LabelSource','foldernames');
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figure;
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perm = randperm(1873,20);
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for i = 1:20
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subplot(4,5,i);
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imshow(imds_train.Files{perm(i)});
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end
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labelCount = countEachLabel(imds_train)
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img = readimage(imds_train,1);
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size(img)
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% validation data
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digitDatasetPath = 'optdigits_valid';
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imds_valid = imageDatastore(digitDatasetPath, ...
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'IncludeSubfolders',true,'LabelSource','foldernames');
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figure;
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perm = randperm(1873,20);
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for i = 1:20
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subplot(4,5,i);
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imshow(imds_valid.Files{perm(i)});
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end
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labelCount = countEachLabel(imds_valid)
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img = readimage(imds_valid,1);
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size(img)
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% testing data
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digitDatasetPath = 'optdigits_test';
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imds_test = imageDatastore(digitDatasetPath, ...
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'IncludeSubfolders',true,'LabelSource','foldernames');
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figure;
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perm = randperm(1873,20);
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for i = 1:20
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subplot(4,5,i);
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imshow(imds_test.Files{perm(i)});
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end
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labelCount = countEachLabel(imds_test)
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img = readimage(imds_test,1);
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size(img)
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%%%%%%%%%%%%%%%%%%%%%%%% Modify the codes here %%%%%%%%%%%%%%%%%%%%%%
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%% Define Network Architecture
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%%%%%%%%%%%%%%%%%%%%%%%% Modify the codes here %%%%%%%%%%%%%%%%%%%%%%
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% network 1
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layers = [ ...
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imageInputLayer([8 8 1])
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convolution2dLayer(4,10,'Padding','same')
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%batchNormalizationLayer
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reluLayer
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fullyConnectedLayer(10)
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softmaxLayer
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classificationLayer];
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%% Specify training/validation options
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options = trainingOptions('adam','MaxEpochs',10, ...
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'ValidationData',imds_valid, ...
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'ValidationFrequency',30, ...
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'Verbose',false, ...
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'Plots','training-progress');
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%% Train the network
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net = trainNetwork(imds_train,layers,options);
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%% Predict the labels of new data and calculate the classification accuracy.
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YPred = classify(net,imds_test);
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Ytest = imds_test.Labels;
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accuracy = sum(YPred == Ytest)/numel(Ytest)
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