added script for training alexnet

Mokey_go_brrrr.m

@@ -0,0 +1,54 @@
+imds=imageDatastore( ...
+    'SignsCutted', ...
+    'IncludeSubfolders',true, ...
+    'LabelSource','foldernames');
+
+fprintf("Anzahl Bilder: %d\n", length(imds.Labels));
+
+rng(7);
+[imdsTrain, idmsValidation] = splitEachLabel(imds,0.7,'randomized');
+
+fprintf("Trainingsmenge Anzahl Elemente: %d  Test(Validierungs)menge: %d\n", ...
+    length(imdsTrain.Labels), length(idmsValidation.Labels));
+
+net=alexnet;
+
+layersTransfer = net.Layers(1:end-3);
+numClasses = numel(categories(imdsTrain.Labels));
+
+layers = [
+    layersTransfer
+    fullyConnectedLayer(numClasses, ...
+        'WeightLearnRateFactor',20, ...
+        'BiasLearnRateFactor',20)
+    softmaxLayer
+    classificationLayer
+];
+
+options = trainingOptions('sgdm', ...
+    MiniBatchSize=10, ...                       % 10
+    MaxEpochs=6, ...                            % 6
+    InitialLearnRate=1e-4, ...                  % 1e-4
+    ValidationData=idmsValidation, ...
+    ValidationFrequency=3, ...                  % 3
+    ValidationPatience=10, ...                  % 5
+    Verbose=false, ...                          % false
+    Plots='training-progress');
+
+netTransfer = trainNetwork(imdsTrain, layers, options);
+
+[yPred, scores] = classify(netTransfer, idmsValidation);
+
+idx=randperm(numel(idmsValidation.Files), 64);
+
+figure
+for i=1 : 64
+    subplot(8,8,i)
+    I = readimage(idmsValidation, idx(i));
+    imshow(I)
+    label = yPred(idx(i));
+    title(string(label));
+end
+
+accuracy = mean(yPred == idmsValidation.Labels);
+fprintf("Genauigkeit: %3.2f\n", accuracy*100);