How to replace the one-hot-encoded label with a (Gaussian) distribution in the training of a CNN for classification ?

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hyeonjin kim 2022-1-24

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评论： hyeonjin kim 2022-1-27

Hi there,

I am trying to train a CNN for classification using imagedatastore, in which the cross entropy loss needs to be calculated between the softmax output and a (Gaussian) distribution instead of its one-hot-encoded version of the label.

In case I need to customize Matlab functions such that my own function (that converts an original label into a distribution) runs instead of 'onehotencode', where (or in which function) does this 'onehotencode' run ?

There may be other simpler ways to achieve my goal. I would greatly appreciate if anyone could help me on this.

Best regards,

HK

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Answer 1

yanqi liu 2022-1-24

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yes，sir，may be just compute vector，such as

class_info = [1; 2; 2; 1]
class_info = 4×1
     1
     2
     2
     1
class_num = length(unique(class_info));
Y = zeros(length(class_info), class_num);
for i = 1 : length(class_info)
    Y(i, class_info(i)) = 1;
end
Y
Y = 4×2
     1     0
     0     1
     0     1
     1     0

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yanqi liu 2022-1-27

yes，it is same question i faced，may be can not make multi output in this environment，so just use label value. but in TensorFlow or Pytorch，it is easy to make multi output，and just add loss can train model

hyeonjin kim 2022-1-27

Dear Yanqi, thanks very much for sharing your thoughts. I will try some more to tackle this issue, but might ultimately have to go for Pytorch, for instance, as you told.

Best !

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