initial quiz 5

quiz/5/main.m

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

quiz/5/myLReLULayer.m

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+classdef myLReLULayer < nnet.layer.Layer
+    
+    properties
+            
+        % Scaling coefficient
+        Alpha
+    end
+
+    methods
+        function layer = myLReLULayer(scale, name) 
+            % Create an examplePreluLayer with numChannels channels
+
+            % Set layer name
+            if nargin == 2
+                layer.Name = name;
+            end
+
+            % Set layer description
+            layer.Description = ...
+                ['myLReLULayer with ', num2str(scale), ' channels']; 
+
+            layer.Alpha = scale;
+        end
+        
+        function Z = predict(layer, X)
+            % Forward input data through the layer and output the result
+            
+            Z = max(X, layer.Alpha*X);
+        end
+        
+        function dLdX= backward(layer, X, Z, dLdZ, memory)
+            % Backward propagate the derivative of the loss function through 
+            % the layer 
+            
+            dLdX = layer.Alpha*dLdZ;
+            dLdX(X>0) = dLdZ(X>0);
+        end
+    end
+end