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L2 regularization in sparse stacked autoencoders not clear to me

Asked by Lukas Vareka on 21 Mar 2018
Latest activity Answered by BERGHOUT Tarek on 11 Apr 2019
Dear Matlab users,
on https://www.mathworks.com/help/nnet/ref/trainautoencoder.html , there is a bit of theory behind L2 regularization used in stacked autoencoders. However, the definition of L2 regularization is not clear to me. First, why is the sum running through all hidden layers l = 1..L but not through all neurons in each hidden layer? Second, I do not understand "k is the number of variables in the training data". Does it mean that k corresponds to the dimensionality of feature vectors?
Thanks for any clarification.
Regards, Lukas Vareka

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Answer by BERGHOUT Tarek on 11 Apr 2019

i didnt undrestand your question , could make more clear , there is no k and L2 parameters in the link .
could you give us an example?.
any way i have made many types of autoencoders i upload some of them on my profile:
https://www.mathworks.com/matlabcentral/fileexchange/71115-denoising-autoencoders?s_tid=prof_contriblnk

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