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

Asked by Osama Tabbakh on 28 Jun 2019
Latest activity Commented on by 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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3 Answers

Answer by 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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Could you please refer to one of this elementary text?
Sorry, I lost all of my several hundred books via a moving van error..
See your library.
Greg
MAYBE I SHOULD HAVE SAID any ELEMENTARY TEXT ON NNs.
GREG

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Answer by 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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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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Answer by 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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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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