<p>Section 4 of the <a href="http://arxiv.org/pdf/1512.00567v3.pdf" rel="noreferrer">paper</a> you cite is about <em>auxiliary classifiers</em>. These are classifiers added to the lower levels of the network, that improve training by mitigating the vanishing gradients problem and speedup convergence. For running inference on a trained network, you should use the main classifier, called <code>softmax:0</code> in the model, and <strong>NOT</strong> the auxiliary classifier, called <code>auxiliary_softmax:0</code>.</p>
Does the Inception Model have two softmax outputs?
<p>The Inception v3 model is shown in this image:</p>

<p><img src="https://4.bp.blogspot.com/-TMOLlkJBxms/Vt3HQXpE2cI/AAAAAAAAA8E/7X7XRFOY6Xo/s1600/image03.png" alt="Inception v3 Model"></p>

<p>The image is from this blog-post:</p>

<p><a href="https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html" rel="nofollow">https://research.googleblog.com/2016/03/train-your-own-image-classifier-with.html</a></p>

<p>It seems that there are two Softmax classification outputs. Why is that?</p>

<p>Which one is used in the TensorFlow example as the output tensor with the name 'softmax:0' in this file?</p>

<p><a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/models/image/imagenet/classify_image.py" rel="nofollow">https://github.com/tensorflow/tensorflow/blob/master/tensorflow/models/image/imagenet/classify_image.py</a></p>

<p>The academic paper for the Inception v3 model doesn't seem to have this image of the Inception model:</p>

<p><a href="http://arxiv.org/pdf/1512.00567v3.pdf" rel="nofollow">http://arxiv.org/pdf/1512.00567v3.pdf</a></p>

<p>I'm trying to understand why there are these two branches of the network with seemingly two different softmax-outputs.</p>

<p>Thanks for any clarification!</p>