Using GoogLeNet model with classification.cpp

[Xxxx Yyyy]

I train 100k images on Digits 3.0 with Caffe on 2 K6000 GPUs. everything works so far,
and I would like to do inferencing on a Jetson TX1 in C++.
As others have pointed out, with "classification.cpp", this will not work out of the box, due to a simple
assertion, that requires the number of model outputs to be 1 (instead of 3 as in GoogLeNet)
Digits graphical model view name the 3 outputs to be "loss", "accuracy" and "accuracy-top5"
Since I only need top-1 accuracy, and in the classification.cpp the output can be selected with this line of code

  Blob<float>* output_layer = net_->output_blobs()[0]; // [0], [1] or [2]

can someone please tell me, how caffe maps output indexes 0 .. n according to the prototxt model file?
Is this mapping the same as in Digits graphical model viewer
(which starts with "loss" to be the leftmost output --> index 0??)?
Thanks