During classification I sometimes get "nan" values
<p>I've trained a caffemodel with GoogLeNet. During testing I have a very high accuracy:</p>

<pre><code>I0122 06:00:54.384351 2039975936 solver.cpp:409]     Test net output #0: loss1/loss1 = 0.433825 (* 0.3 = 0.130148 loss) 
I0122 06:00:54.385201 2039975936 solver.cpp:409]     Test net output #1: loss1/top-1 = 0.8764 
I0122 06:00:54.385234 2039975936 solver.cpp:409]     Test net output #2: loss1/top-5 = 0.969 
I0122 06:00:54.385243 2039975936 solver.cpp:409]     Test net output #3: loss2/loss1 = 0.327197 (* 0.3 = 0.0981591 loss) 
I0122 06:00:54.385251 2039975936 solver.cpp:409]     Test net output #4: loss2/top-1 = 0.8918 
I0122 06:00:54.385256 2039975936 solver.cpp:409]     Test net output #5: loss2/top-5 = 0.984601 
I0122 06:00:54.385262 2039975936 solver.cpp:409]     Test net output #6: loss3/loss3 = 0.304042 (* 1 = 0.304042 loss) 
I0122 06:00:54.385268 2039975936 solver.cpp:409]     Test net output #7: loss3/top-1 = 0.9228 
I0122 06:00:54.385273 2039975936 solver.cpp:409]     Test net output #8: loss3/top-5 = 0.9768
</code></pre>

<p>No I have a python classifier which looks like this:</p>

<pre><code>caffe.Classifier(MODEL_FILE, PRETRAINED,
                       mean=np.load('train_image_mean.npy').mean(1).mean(1),
                       channel_swap=(2, 1, 0),
                       raw_scale=255,
                       image_dims=(256, 256))
</code></pre>

<p>I ran the classifier through all my validation data. The accuarcy is very high. But I get some "nan" probability values for some input images. What's the reason for that? What does "nan" mean? Is it "I did not recognize any class"? </p>

<p>Edit:
This question is not a <a href="https://stackoverflow.com/questions/33962226/">duplication</a> since it refers to the classification and not to the training</p>

<p>Thank you. </p>