Error using nnet.cnn.L​ayerGraph>​iThrowErro​rIfLayerHa​sMultipleI​nputs Layer 'inception_3a-output' has multiple inputs. Specify which input of layer 'inception_3a-output' to use.

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clc
clear all;
close all;
%Open file in MATLAB
outputFolder=fullfile('recycle101');
rootFolder=fullfile(outputFolder,'project');
categories={'aluminiumcan','petbottle','drinkcartonbox'};
%Separate dataset into training and validation in the ratio of 70:30
imds=imageDatastore(fullfile(rootFolder,categories),'LabelSource','foldernames');
tbl=countEachLabel(imds)
minSetCount=min(tbl{:,2});
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
%Count the total datasets in each file
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=googlenet;
analyzeNetwork(net)
lys = net.Layers;
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,'loss3-classifier',newFCLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,'output',newClassLayer);
analyzeNetwork(lgraph)
lgraph = connectLayers(lgraph,'inception_3a-relu_5x5','inception_3a-output');
lgraph = connectLayers(lgraph,'inception_3b-output','pool3-3x3_s2');
%Resize the image and train the network
imageSize=net.Layers(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);

回答(1 个)

Ranjeet
Ranjeet 2023-6-2
Hi Tan,
You are extracting layers from ‘GoogleNet’ in the following code and then using the extracted layer to form a layerGraph’ and then further add/replace other layers like fully connection layer and classification layer.
%Load pre-trained network
net=googlenet;
analyzeNetwork(net)
lys = net.Layers;
lys(end-3:end)
numClasses = numel(categories(imdsTrain.Labels));
lgraph = layerGraph(lys);
However, ‘Googlenet’ has multiple parallel connections from a single layer throughout the network. When you are create layerGraph from net.Layers, connection information in ‘net.Connections’ is not being used. ‘layerGraph’ can directly be created from network by ‘lgraph = layerGraph(net)’ so that connection information is also there. You may use the following code:
net=googlenet;
analyzeNetwork(net)
numClasses = 5;
lgraph = layerGraph(net);
% check the layer connected to fully connected layer]
inputSizeFc = lgraph.Layers(142).InputSize;
%Replace the classification layers for new task
newFCLayer = fullyConnectedLayer(numClasses,'Name','new_fc','WeightLearnRateFactor',10,'BiasLearnRateFactor',10, ...
'Bias',ones(numClasses, 1), 'Weights',ones(numClasses, inputSizeFc));
lgraph = replaceLayer(lgraph,'loss3-classifier',newFCLayer);
newClassLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,'output',newClassLayer);
analyzeNetwork(lgraph);

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