Why do I get the error"Training using trainNetwork failed. Duplicate table variable name: 'input'"

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源 2023-3-29
回答： Cris LaPierre 2023-5-11
When I use ’combine‘ to connet 'TrainA_Train' and 'TrainB_Train' , then I get the error"Training using trainNetwork failed. Duplicate table variable name: 'input'"
Regarding ’TranA‘, it is a file read using the 'imageDatastore'.
Regarding ’TranB‘, it is also a file read using the 'imageDatastore'.
this is my code:
TrainA = imageDatastore("C:\Users\Administrator\Desktop\data1","IncludeSubfolders",true,"LabelSource","foldernames");
[TrainA_Train, TrainA_Valid, TrainA_test] = splitEachLabel(imdsTraingray,0.6,0.2,"randomized");
TrainB = imageDatastore("C:\Users\Administrator\Desktop\data2","IncludeSubfolders",true,"LabelSource","foldernames");
[TrainB_Train, TrainB_Valid, TrainB_test] = splitEachLabel(imdsTraingray,0.6,0.2,"randomized");
Train = combine(TrainA_Train,TrainB_Train);
Valid = combine(TrainA_Valid,TrainB_Valid);
opts = trainingOptions("adam",...
"ExecutionEnvironment","auto",...
"InitialLearnRate",0.0001,...
"LearnRateDropFactor",0.01,...
"LearnRateDropPeriod",10,...
"LearnRateSchedule","piecewise",...
"MaxEpochs",20,...
"MiniBatchSize",40,...
"Shuffle","every-epoch",...
"Plots","training-progress",...
"ValidationData",Valid);
[net, traininfo] = trainNetwork(Train,lgraph,opts);
Note:I omitted the network model
I want to train a deep neural network with 2 inputs and 1 output,but I don't konw how to revise this problem.

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回答（1 个）

Cris LaPierre
Cris LaPierre 2023-5-11
I think this is because you are incorrectly using the same datastore (imdsTraingray?) for your splitEachLabel commands. I think you should be using TrainA and TrainB.
TrainA = imageDatastore("C:\Users\Administrator\Desktop\data1","IncludeSubfolders",true,"LabelSource","foldernames");
[TrainA_Train, TrainA_Valid, TrainA_test] = splitEachLabel(imdsTraingray,0.6,0.2,"randomized");
TrainB = imageDatastore("C:\Users\Administrator\Desktop\data2","IncludeSubfolders",true,"LabelSource","foldernames");
[TrainB_Train, TrainB_Valid, TrainB_test] = splitEachLabel(imdsTraingray,0.6,0.2,"randomized");
Try the following instead
TrainA = imageDatastore("C:\Users\Administrator\Desktop\data1","IncludeSubfolders",true,"LabelSource","foldernames");
[TrainA_Train, TrainA_Valid, TrainA_test] = splitEachLabel(TrainA,0.6,0.2,"randomized");
TrainB = imageDatastore("C:\Users\Administrator\Desktop\data2","IncludeSubfolders",true,"LabelSource","foldernames");
[TrainB_Train, TrainB_Valid, TrainB_test] = splitEachLabel(TrainB,0.6,0.2,"randomized");

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