Getting error while running alexnet

Question

Tan 2023-5-14

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1962789-getting-error-while-running-alexnet

评论： Walter Roberson 2024-5-2

net =alexnet('Weights','none');
lys = net.Layers();
lys(end-3:end)

getting error:

Unrecognized method, property, or field 'Layers' for class 'nnet.cnn.layer.Layer'.

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Walter Roberson 2023-5-14

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1962789-getting-error-while-running-alexnet#answer_1234469

编辑：Walter Roberson 2023-5-14

When you call alexnet() with 'Weights', 'none' then the result you get back is a Layer array, not a DagNetwork or LayerGraph. The list of layers that you are trying to get is just the same as net itself in this case.

You need the .Layers() if you load in the regular pre-trained alexnet

11 个评论
显示 9更早的评论隐藏 9更早的评论

Tan 2023-5-14

编辑：Walter Roberson 2023-5-14

在 MATLAB Online 中打开

here is my full coding:

clc
clear all; 
close all;
outputFolder=fullfile('recycle101');
rootFolder=fullfile(outputFolder,'project');
categories={'aluminiumcan','petbottle','drinkcartonbox'};
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=='aluminiumcan',1);
PETBottles=find(imds.Labels=='petbottle',1);
DrinkCartonBox=find(imds.Labels=='drinkcartonbox',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=alexnet;
net =alexnet('Weights','none');
analyzeNetwork(net)
lys = net;
lys(end-3:end)
numClasses = numel(categories(imdsTrain.Labels));
lgraph = layerGraph(lys);
%Replace the classification layers for new task
newFCLayer = fullyConnectedLayer(3,'Name','new_fc','WeightLearnRateFactor',10,'BiasLearnRateFactor',10);
lgraph = replaceLayer(lgraph,'fc8',newFCLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,'output',newClassLayer);
%Resize the image
imageSize=lys(1).InputSize;
augmentedTrainingSet=augmentedImageDatastore(imageSize,...
    imdsTrain,'ColorPreprocessing','gray2rgb');
augmentedValidateSet=augmentedImageDatastore(imageSize,...
    imdsValidation,'ColorPreprocessing','gray2rgb');
options = trainingOptions('sgdm', ...
    'MiniBatchSize',4, ...
    'MaxEpochs',8, ...
    'InitialLearnRate',1e-4, ...
    'Shuffle','every-epoch', ...
    'ValidationData',augmentedValidateSet, ...
    'ValidationFrequency',3, ...
    'Verbose',false, ...
    'ExecutionEnvironment','cpu', ...
    'Plots','training-progress');
trainedNet = trainNetwork(augmentedTrainingSet,lgraph,options);
[YPred,probs] = classify(trainedNet,augmentedValidateSet,'ExecutionEnvironment', 'cpu');
YValidation = imdsValidation.Labels;
%Calculate accuracy,error,pecision and recall
accuracy = sum(YPred == YValidation)/numel(YValidation)
error=1-accuracy
confMat=confusionmat(YValidation ,YPred);
confMat=bsxfun(@rdivide,confMat,sum(confMat,2));
z=mean(diag(confMat))
cmt=confMat
sum_of_row= sum(cmt,2)
z=(diag(cmt));
precision = z./sum_of_row ;
overallprecision=mean(precision);
sum_of_column= sum(cmt,1)
recall=z./sum_of_column'
totalrecall=mean(recall);
f1=2*(overallprecision*totalrecall)/(overallprecision+totalrecall)
categories = {'aluminium can','PET bottle','drink carton box'}
label = categorical(categories)
cm  = confusionchart(cmt,label)
cm.RowSummary = 'row-normalized';
cm.ColumnSummary = 'column-normalized';
%%save Network
save simpleDL.mat trainedNet lgraph
%% Testing process
I = imread('plastic73.jpg');
ds=augmentedImageDatastore(imageSize,...
    I,'ColorPreprocessing','gray2rgb');
predictedLabel = trainedNet.classify(ds);
sprintf('The loaded image belongs to %s class', predictedLabel)

mukthi 2024-5-2

Even though showing error as unrecognised function or variable 'alexnet'

Walter Roberson 2024-5-2

@mukthi

"This function requires Deep Learning Toolbox™ Model for AlexNet Network support package. If this support package is not installed, the function provides a download link. Alternatively, see Deep Learning Toolbox Model for AlexNet Network."

请先登录，再进行评论。