MATLAB Answers

Why sets Matlab automatically the activation functions for a neural network like this?

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Osama Tabbakh
Osama Tabbakh on 28 Jun 2019
Commented: Osama Tabbakh on 18 Aug 2019
I am asking myself why chooses Matlab always automatically for the hidden layer tan-Sigmoid and for the output layer pureline as an activation function?
If it refers to a study, which discovers, that those activation functions are more efficient than the other, please let me know.

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Answers (3)

Greg Heath
Greg Heath on 29 Jun 2019
That is a standard configuation for a neural net. It's operation is explained in every elementary text.
Thank you for formally accepting my answer
Greg

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Greg Heath
Greg Heath on 30 Jul 2019
Sorry, I lost all of my several hundred books via a moving van error..
See your library.
Greg

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Greg Heath
Greg Heath on 30 Jul 2019
The simplest useful approximation is is a series of blocks with different heights and widths.
The simplest useful DIFFERENTIABLE approximation is is a series of ROUNDED blocks with different heights and lengths.
Combining sigmoids fits the bill!
GREG

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Osama Tabbakh
Osama Tabbakh on 31 Jul 2019
Thank you for your answer, I do not need it just to understand it, but I need some references because I am writing right now my thesis.

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Sai Bhargav Avula
Sai Bhargav Avula on 16 Aug 2019
As mentioned by others thats the default setup in MATLAB.
Coming to comparision between different activation functions.
It is generally recommended to use ReLU as the activation function. If your model suffers form dead neurons during training we should use leaky ReLu or Maxout function.
The Sigmoid and Tanh are generally not preferred as they suffer with vanishing Gradient Problem which causes a lots of problems to train,degrades the accuracy and performance of a deep Neural Network Model.

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Osama Tabbakh
Osama Tabbakh on 18 Aug 2019
I can't use ReLU because I am not using a deep neural network. I need just a reason, why are set by Matlab like that?

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