train with multiple input to get two classes output

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Rayan Matlob 2022-7-11

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1757585-train-with-multiple-input-to-get-two-classes-output

评论： Ben 2022-7-13

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I have two folders

folder_1 with two subfolders(good, bad) each with (900 images and 100 image) respectively

folder_2 with two subfolders(good, bad) each with (900 images and 100 image) respectively. when training with pretrained (resnet50) on the "Deep netwoek designer" console, i get the next error about categorical response? any explaination please.

imds_1 = imageDatastore('C:\Users\Folder_1', ...
    'IncludeSubfolders',true, ...
    'FileExtensions','.jpg', ...
    'LabelSource','foldernames');
[imdsTrain_1,imdsValidation_1] = splitEachLabel(imds_1,0.75); %split the data into training and validation
imds_2 = imageDatastore('C:\Users\Folder_2', ...
    'IncludeSubfolders',true, ...
    'FileExtensions','.jpg', ...
    'LabelSource','foldernames');
[imdsTrain_2,imdsValidation_2] = splitEachLabel(imds_2,0.75); %split the data into training and validation
%  'train_ok.txt' contain the labels of the images in (imdsTrain_1 or imdsTrain_2)           750x1
%  'val_ok.txt'   contain the labels of the images in (imdsValidation_1 or imdsValidation_2) 250x1
labelStore = tabularTextDatastore('train_ok.txt','TextscanFormats','%C',"ReadVariableNames",false);
labelStoreCell = transform(labelStore,@setcat_and_table_to_cell);
train_multi = combine(imdsTrain_1,imdsTrain_2,labelStoreCell);
train_multi.read
labelStore2 = tabularTextDatastore('val_ok.txt','TextscanFormats','%C',"ReadVariableNames",false);
labelStoreCell2 = transform(labelStore2,@setcat_and_table_to_cell);
val_multi = combine(imdsValidation_1,imdsValidation_2,labelStoreCell2);
val_multi.read
%train_multi.read 750x1
    {224×224×3 uint8}    {224×224×3 uint8}    {[Good        ]}
    {224×224×3 uint8}    {224×224×3 uint8}    {[bad         ]}
    {224×224×3 uint8}    {224×224×3 uint8}    {[bad         ]}  ...  
%val_multi.read 250x1
    {224×224×3 uint8}    {224×224×3 uint8}    {[Good        ]}
    {224×224×3 uint8}    {224×224×3 uint8}    {[Good        ]}
    {224×224×3 uint8}    {224×224×3 uint8}    {[bad         ]} ....
function [dataout] = setcat_and_table_to_cell(datain)
validcats = ["Good", "bad"];
datain.(1) = setcats(datain.(1),validcats);
dataout = table2cell(datain);
end

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Rayan Matlob 2022-7-12

编辑：Rayan Matlob 2022-7-13

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@Ben, Dear Sir, every thing you said is just correct, even the concatenation is perfectly worked. The error is disappeared.

one last thing you also mentioned and was correct which is the data, it is first all the "Good" images, then all the "bad", and in order to make it random i used

imdsTrain_1 = shuffle(imdsTrain_1);
imdsTrain_2 = shuffle(imdsTrain_2);

The problem now is the order of both (imdsTrain_1 and imdsTrain_2) is not the same! , and the same for (imdsValidation_1 and imdsValidation_2). how to make the shuffle reorder the images of both (imdsTrain_1 and imdsTrain_2) in the same manner?

Ben 2022-7-13

You can do cds = combine(imdsTrain_1,imdsTrain_2) and call shuffle(cds). You will want to also combine an arrayDatastore (or other Shuffleable datastore) containing the labels to use this for training.

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