train with multiple input to get two classes output
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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.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1062185/image.jpeg)
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
9 个评论
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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