Why do I get the error"Training using trainNetwork failed. Duplicate table variable name: 'input'"
1 vue (au cours des 30 derniers jours)
Afficher commentaires plus anciens
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.
0 commentaires
Réponses (1)
Cris LaPierre
le 11 Mai 2023
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");
0 commentaires
Voir également
Catégories
En savoir plus sur Define Shallow Neural Network Architectures dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!