Tensorflow的初始 - 类的数量

Question

I'm wondering about the number of classes in Tensorflow's Inception implementation.

In their training script , they load the training set consisting of images and labels. Next, to calculate the loss, they define the number of classes as:

# Number of classes in the Dataset label set plus 1.
# Label 0 is reserved for an (unused) background class.
num_classes = dataset.num_classes() + 1

You can see that they use an "unused background class". You can also see this approach when they create their training set: build_image_data.py

So, why would you need such an unused background class? (Especially because you get one additional but useless prediction from the output layer)

Answer 1

It's a convention we use for all our image datasets, and it didn't seem worth the trouble to break it for this particular model. As an aside, I wish all academic classification datasets had a 'none of the above' class in their test evaluation. A classifier which doesn't know when it doesn't know is not that useful in practice. (vanhoucke)

https://groups.google.com/a/tensorflow.org/forum/#!topic/discuss/9G-c2K_GCmk