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Multi-input imagedatastore

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OJ27
OJ27 on 31 Aug 2020
Commented: Mohammad Sami on 21 Oct 2020 at 2:13
I am trying to train a network with N inputs to perform binary classification. Each image is multispectral (more than 3 channels rgb). I read in a comment from mathworks staff which creates data and stores it in a datastore to train a network with two inputs: image and vector data.
I have also looked at the example Image Classification using CNN with Multi Input, which uses two inuts to classify digits.
Both of these examples do not use imageDatastore. I was wondering if we could use
combine
to combine the imageDatastore for each data sample. I read in Datastores for Deep Learning that the datastore must be a combined or transformed datastore that returns a cell array with (numInputs+1) columns containing the predictors and the responses, where numInputs is the number of network inputs and numResponses is the number of responses.
My question is the following. Can I have multiple imageDatastore objects and combine them? If so, how do I store the label column? I tried the code below and got an error.
imds1 = imageDatastore(...);
imds2 = imageDatastore(...);
labels = ???
datastore = combine(imds1, imds2, labels);
Usually, we can assign the label through imds1.Labels. I also want to know if once combined, data augmentation can be done on the images and random split for training and validation.

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Answers (1)

Mohammad Sami
Mohammad Sami on 5 Sep 2020
Edited: Mohammad Sami on 5 Sep 2020
You can save the labels as a text file and then create a tabularTextDatastore to read them back.
Something like this could work.
read_size = 4; % readsize must be fixed otherwise concatenation will fail
digitDatasetPath = fullfile(matlabroot,'toolbox','nnet', ...
'nndemos','nndatasets','DigitDataset');
imds = imageDatastore(digitDatasetPath, ...
'IncludeSubfolders',true, ...
'LabelSource','foldernames');
imds.ReadSize = read_size;
labels = imds.Labels; % your labels here
% the order of your images and label must be the same
writematrix(labels,'labels.txt');
% %C = categorical
labelStore = tabularTextDatastore('labels.txt','TextscanFormats','%C',"ReadVariableNames",false);
labelStore.ReadSize = read_size;
labelStoreCell = transform(labelStore,@setcat_and_table_to_cell);
finalStore = combine(imds,labelStoreCell);
% test read
finalStore.read
You will also need to correct the categorical array categories during transformation.
function [dataout] = setcat_and_table_to_cell(datain)
validcats = string(0:9); % define valid labels for categorical array
datain.(1) = setcats(datain.(1),validcats);
dataout = table2cell(datain);
end

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Mohammad Sami
Mohammad Sami on 10 Sep 2020
I see you are using the same data store twice in the combine. This may be causing the issue. If you want to use the same images, perhaps make a second instance of the data store.
Girish Tiwari
Girish Tiwari on 20 Oct 2020 at 10:11
Thanks M. Sami. Works like a charm.
However, I am getting an error while training the network with the datastore as "The output size (7) of the last layer does not match the number of classes (10)".
I have verified that there are only 7 labels and fullyconnected layer also has 7 outputs.
(See my code at: https://www.mathworks.com/matlabcentral/answers/619943-invalid-training-data-the-output-size-7-of-the-last-layer-does-not-match-the-number-of-classes-1)
Mohammad Sami
Mohammad Sami on 21 Oct 2020 at 2:13
It's because we set the 10 categories in the setcat_and_table_to_cell.
You should change the variable validcats to the appropriate 7 categories for your case.

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