How to replace the one-hot-encoded label with a (Gaussian) distribution in the training of a CNN for classification ?
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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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yanqi liu
2022-1-24
yes,sir,may be just compute vector,such as
class_info = [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
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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
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