Hello Andrey,
In this case you can use the "combine" function to create a combinedDatastore object of the form accepted by trainNetwork:
labelDsTrain = arrayDatastore(imdsTrain1.Labels); % Assuming the true labels are those of imdsTrain1
cdsTrain = combine(imdsTrain1, imdsTrain2, labelDsTrain);
In order to train a multi-input network, your data must be in the form of a datastore that outputs a cell array with (numInputs + 1) columns. In this case numInputs = 2, so the first two outputs are the images inputs to the network, and the final output is the label of the pair of images.
You can do a similar combination to make a validation datastore too. With a datastore of this form, you can train with
net = trainNetwork(cdsTrain, lgraph, options);
For more information on how to create datastores for deep learning, you can refer to this documentation:
You may also find this example of training a multi-input network for classification useful:
