You can refer to Multiple-Input and Multiple-Output Networks to create a network with multiple inputs.
The following is an example:
%% Define muliple input network.
layers1 = [
imageInputLayer([28 28 3],'Name','input')
convolution2dLayer(3,3,'Padding','same','Name','conv_1')
multiplicationLayer(2,'Name','mul')
regressionLayer('Name','regression')];
lgraph = layerGraph(layers1);
layers2 = [imageInputLayer([28 28 3],'Name','x')];
lgraph = addLayers(lgraph,layers2);
% Connect layers.
lgraph = connectLayers(lgraph,'x','mul/in2');
% Analyze network.
analyzeNetwork(lgraph)
You can refer to List of Deep Learning Layers for the available layers and Deep Learning Custom Layers to create a custom layer for your problem.
