Box_confidence and box_class_probs

Course Q&A Deep Learning Specialization Convolutional Neural Networks
1

Hello,
I am working through the assigment for the CNN lecture, and was curious about some clarrification.
For both box_confidence and box_class_probs, conceptually these sould be the probability of having an object and then the probability of each of the classes if present.
This nicely allows for multiplication to get the probability of each class.

If I look within the data, I found the data to be surprising.

I was expecting values from 0 to 1, but am finding large numbers, many that are negative. Can I get some clarrification of what these numbers are if they are not the direct probabilities?

Note, box_class_probs does more like probabilities, and it is box_confidence that I am more surprised by the numbers.

print(box_confidence[10,10,:,:])
tf.Tensor(
[[ 0.07983017]
[ 1.3960358 ]
[-2.839337 ]
[ 4.725943 ]
[-5.2636952 ]], shape=(5, 1), dtype=float32)

Thanks so much
Wil

2

Those are test values.

Both box_confidence and box_class_probs are probabilities.
See for yourself using help(yolo_head)

Help on function yolo_head in module yad2k.models.keras_yolo:

yolo_head(feats, anchors, num_classes)
    Convert final layer features to bounding box parameters.
    
    Parameters
    ----------
    feats : tensor
        Final convolutional layer features.
    anchors : array-like
        Anchor box widths and heights.
    num_classes : int
        Number of target classes.
    
    Returns
    -------
    box_xy : tensor
        x, y box predictions adjusted by spatial location in conv layer.
    box_wh : tensor
        w, h box predictions adjusted by anchors and conv spatial resolution.
    box_conf : tensor
        Probability estimate for whether each box contains any object.
    box_class_pred : tensor
        Probability distribution estimate for each box over class labels.
3

Right! Notice that in the test cell they used a normal distribution to generate those test values, so the values do not look like what you would get in a real application of this function. But there is nothing in the computations you are doing that fundamentally depend on those values being between 0 and 1, so it is still a legitimate test of your logic. But I would say at a higher level this is just sort of “bad form”: it would have been just as easy for them to use a uniform distribution as the PRNG function for the input data there. I’ll file a bug about that, but it’s not a high priority item. Thanks for pointing this out!

4

Thanks for all your help again, I’ll take a look at this again tonight.

5

Actually, the probabilities being negative is a problem, because this forces everyone to write a wrong solution.
You can notice that there should be no need to multiply by box_confidence when doing argmax, because you are just multiplying all the compared numbers by the same value. But because the test values wind up negative, this multiplication flips the sign of box_class_probs and the minimum becomes maximum.

6

No it doesn’t. The solution of multiplying by the box_confidence is what the instructions tell you to do. So that is not a “wrong solution”. You make a valid point that if you don’t do that and just use argmax on the original values, then you get a different answer with the incorrect test cases. So if you write the code in your suggested way, you fail the test case. But if you follow the instructions, then your code will pass the test cases. So the test cases force you to literally follow the instructions as written, but don’t allow you to do your clever optimization.

Don’t get me wrong: I agree that the test cases are incorrect. I filed a bug about this more than two years ago, but they elected not to change the test cases. Sorry, but that’s where we are. I can try filing the bug again. It would be a simple change for them to make and I’d have to look up the git issue to understand why they made the choice not to change it.

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