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From: "Adeel " <neoresearcher@gmail.com>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Backpropagation Neural network Code problem
Date: Wed, 18 Mar 2009 08:29:02 +0000 (UTC)
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Greg Heath <heath@alumni.brown.edu> wrote in message 

<dae52b80-1b2c-436a-9dd5-d6127ab8b97b@j35g2000yqh.googlegroups.com>...
> On Mar 17, 3:13=A0pm, Greg Heath <he...@alumni.brown.edu> wrote:
> > On Mar 17, 2:40 pm, Greg Heath <he...@alumni.brown.edu> wrote:
> >
> > > On Mar 16, 11:46 pm, "Adeel " <neoresearc...@gmail.com> wrote:
> > > > 1. OK i will insert much more comments in the code.
> >
> > > > 2. You are right that the other layer is output layer, But I had two =
> layers, because output layer had weights (plus bias) on them. Not to mentio=
> n the activation function etc.
> >
> > Two important points on terminology:
> >
> > 1. The input layer is not a neuron layer.
> > 2. The output layer is not a hidden layer. (correct your comment in
> > the code)
> >
> > > > 3. The name of this file is element wise, that is element by element =
> every step, Just a precaution to safe guard against any malfunction or mist=
> ake in the matrix multiplication done by me. (Although i had created one fi=
> le using matrix multiplication).
> >
> > > > 4. OK i agree i did not know that. Plus i did not know what "logarith=
> mic sigmoid"
> 
> WHOOPS! I meant logistic sigmoid. See below
> 
> > > > is. Are u talking about bipolar sigmoid.
> 
> No. Bipolar sigmoid is a scaled tanh. See beow.
> 
> ( I have not used tanh, I had used bipolar sigmoid.). In the book
> "Fundamentals ofneuralnetworks by laurene Fausett of pearson
> education" They had used for both layer the bipolar sigmoid. and
> according to the text they had converge the weight using the bipolar
> sigmoid as an activation function on both the layers. (unless i had
> understood it incorrectly. According to them they had solve the xor
> problem solution in binary representation, to get converge in 3000
> epochs.. Mine never did)
> >
> > logsig(x) =3D 1/(1+exp(-x))
> 
> Logistic Sigmoid
> 
> > tanh(x/2) =3D 2*logsig(x)-1
> >
> > You might be interested in
> >
> > http://groups.google.com/group/comp.ai.neural-nets/
> > msg/1c33c39b186dd026?hl=3Den
> >
> > Note the learning times using the batch L-M algorithm.
> 
> Also
> 
> http://groups.google.com/group/comp.ai.neural-nets/browse_thread/thread/a44=
> 4b0a03ffcdafb?hl=3Den&tvc=3D2&q=3Dgreg-heath+backpropagation+tutorial+
> 
> Hope this helps.
> 
> Greg

Sir.
My basic and only concern is "Why did my algo did not converge ?"

Is binary sigmoid is logistic sigmoid ( logsig(x) ) ?

You are right. Input layer is not included in the layer count. (which i also didnot counted). But i 

counted output layer because and hidden layer  (the layer next to input layer) because both those 

layer had weight which did change in the learning process.. Secondly the text book also called it 

two layer. Another text book "Neural network. Algorithums, applications, and programming techniques 

by James A. Freeman / David M. Skapura" also called it two layer backpropagation network. If i had 

understood it incorrectly I would be greatful if u correct me.
I think in matlab documentation they didnot count the output layer. (i will conform that when i 

check that) if u r saying according to that then i agree with u.

the last link that you send 
http://groups.google.com/group/comp.ai.neural-nets/browse_thread/thread/a44=4b0a03ffcdafb?hl=3Den&tv

c=3D2&q=3Dgreg-heath+backpropagation+tutorial+
or 
http://groups.google.com/group/comp.ai.neural-nets/browse_thread/thread/a44=
didnot render a page the responce was 
Topic not found
"We're sorry, but we were unable to find the topic you were looking for. Perhaps the URL you clicked 

on is out of date or broken? "