For your reference, the doc below explains how to create shallow neural networks with multiple outputs.
num_input = 1;
num_output1=2;
num_output2=1;
num_neuron=16;
NN=network;
NN.numInputs=1;
NN.numLayers=3;
NN.biasConnect(1:end)=1;
NN.inputConnect(1,1)=1;
NN.layerConnect=[0 0 0;1 0 0;1 0 0];
NN.outputConnect=[0 1 1];
NN.layers{1}.transferFcn='poslin';
NN.layers{1}.size=num_neuron;
NN.layers{2}.transferFcn='poslin';
NN.layers{2}.size=num_output1;
NN.layers{3}.transferFcn='poslin';
NN.layers{3}.size=num_output2;
NN.initFcn='initlay';
NN.inputs{1}.size=num_input;
NN.trainFcn='trainlm';
actual_input=rand(num_input,10);
actual_output1=rand(num_output1,10);
actual_output2=rand(num_output2,10);
NN_initialized=configure(NN,actual_input,{actual_output1; actual_output2});
% view(NN_initialized)
As a final note, I still would like to recommend you use Deep Network Designer for a more "intuitive" designing.
