Club Amiga de Montreal - CAM/CAM_CD_1.iso/files/593a.lha/Neuro_v1.0/Neuro.doc

Text File | 1991-12-12 | 16KB | 369 lines

                                 ---------
                                 | Neuro |
                                 ---------

                        A neural network simulator
                                Version 1.0

                                    by
                               Berthold Ruf
                                    and
                               Ulrich Wisser



 Contents:
 =========
     0.  ............. Copyright
     1.  ............. Introduction
     2.  ............. Getting started
     3.  ............. Description of the menu items
     3.1 .................. Project
     3.2 .................. Action
     3.3 .................. Settings
     4.  ............. Notes to the internal structure of the program
     5.  ............. Known Bugs
     6.  ............. Appendix



0. Copyright:
=============

This  program  is  shareware  and NOT (!) public domain.  When copying this
program be sure to include the following files:

     -neuro                       the program
     -neuro.doc                   this documentation
     -req.library                 copy this file to your libs: directory
                                  Thanks to Bruce & Colin for the excellent
                                  library. Their address is:
                                           Bruce Dawson, Colin Fox
                                           Box, 1215 Daviestreet.
                                           Vancouver, B.C., V6E 1N4
     -data/Numbers                demonstrates, how the net can recognize
                                  numbers, works very well
     -data/Encoder                Example of finding an internal
                                  binary-like representaion
     -data/Letters                the letters in a 5x5-representation are
                                  all quite simular, one pixel is used by
                                  many patterns, this example shows the
                                  problems for neural networks to learn
                                  patterns like these.
     -data/Pattern05x05.06        very easy to learn, try it yourself!
     -data/Pattern10x10.04        works in Hopfield-mode quite well


IT  IS  FORBIDDEN TO SPREAD THE PROGRAM SKIPPING ONE OF THESE FILES OR WITH
ANY  FILE BEING ALTERED!  REMEBER THIS PROGRAM IS STILL UNDER COPYRIGHT AND
BY  THIS REASON PROTECTED BY LAW!  THE AUTHORS DON'T GUARANTEE ANY FUNCTION
OF  THE  PROGRAM  AND ARE NOT LIABLE FOR ANY DAMAGE RESULTING IN THE USE OF
THIS PROGRAM!


If  you  like  this  program  and use it more frequently, please send DM 20
(Europe) or $20 (Oversea) to:

                       Berthold Ruf / Ulrich Wisser
                              Mauerstrasse 44

                               W-5100 Aachen
                                  Germany


You will get a free update of the next version, as soon as it is available.

If  you  have  any  questions  concerning  our  program  or suggestions for
improving it, please write or email us.  Our internet-addresses are:

                   [e-mail]
                 [e-mail]

                           voice : [phone]


Many  thanks  to  our beta-testers, who have helped to improve our program.
Especially AnTu did a great job.  He also created the nice icon.


1. Introduction:
================

The  program "Neuro" is a neural network simulator.  It learns patterns and
recognizes them, also if the input is incomplete!  It can for example learn
the pixel representation of some letters.  After the user entered a part of
a pattern the network will complete this pattern.

Used  in  the  mode "Backpropagation" it finds internal represantations for
patterns.   So  the  net is able to "discover" the binary code for example,
when  it  has  to  learn 8 patterns, but has only 3 Bits (hidden units), to
represent  them  internally (known as the Encoder-problem, see the included
example-file "Encoder").


Two types of neural networks are implemented in this version:
                             -Backpropagation
                             -Hopfield
In the next version we also plan to realize the Boltzmann-mashine.

It  is  not possible to give a full explanation here, if you are interested
in  details,  please  refer  to  the  books  listed  in  the appendix.  The
backpropagation-algorithm  is  probably  the  one  which might get the best
results.  The net is able to learn most of the pattern-association problems
very  well.  The hopfield-net also very often produces good results, though
it  can't  distinguish  between  a  pattern and its reverse partner, so you
might sometimes get a reverse pattern as an answer.  Unfortunately it has a
very small capacity so you can't enter too many patterns.  In this case you
will  get a warning message.

The programm offers the opportunity, to explore the possibilities (but also
the limitations) of neural networks.


2. Getting started:
===================

Before starting you should copy the "req.library" to your libs:  directory.
The program is started from CLI (or shell) with the command

                                   neuro

or  from  the  workbench  by  doubleclicking  the  Icon.  No parameters are
necessary.

For  a first try, select "Load all" in the menu "project" and load the file
"data/Numbers".   You have loaded now a neural network which has learned to
recognize  the  ten  digits  (0-9).  To see them, select "info" in the menu
"project".   Now  press  a  key  or  a  mouse  button  and  select the item
"question"  in  the menu "Action".  You now will see three grids.  The left
one  is for your input, the middle one shows the internal representation of
your  input  and  the  right one shows the answer of the network.  Click in
some  squares  of  the  input  grid, so that it looks simular to one of the
digits  as  shown  in  the  info.  Now click "question" in order to see the
answer of the network.  If it doesn't look simular to one of the digits you
can  take  this  answer again as an input by selecting the gadget "Retake".
You  also  can  find  out,  how  certain the network is about its answer by
clicking into "Black&White" which then will change to "Grey":  Some squares
will become grey.  The more black a square is, the more sure the network is
that this square has to be black.


3. Description of the menu items:
=================================

3.1 Project:
----------------------

-Load:   You  either  can  load  the patterns only or the patterns plus all
 parameters  and the  weights  of  the  matrix  (the result of the learning
 process).  When  the  program is started you either must load something or
 generate a new pattern file with the included editor (see below).
 When you select "Load patterns", an ASCII-file containing the descritption
 of the patterns to be learned is loaded. For a detailed descritpion of the
 structure of the ASCII-file see chapter 4.
 The  file  loaded  by  "Load  all" is a Non-ASCII-file.  It contains the
 patterns,  the  mode  of the net (Backpropagation/Hopfield), the
 weights  and the current values of the parameters, which can be changed in
 the "Setting/parameter" menu.

-Save:   You  only  can save the patterns with this menu item, which is not
 nessessary  since  you have generated or changed patterns with the editor.
 After  learning, you should choose "Save all" in order to save the results
 of the learning process.

-Clear:   This  will  reset the complet program.  All patterns and learning
 results will be deleted!

-Edit:  This is a very simple editor, but it works and it should suffice to
 create or change patterns.
--New:   If  you want to create a completely new pattern file, select "Edit
  new"  (the  old patterns in memory will be forgotten then!).  You have to
  tell  the  editor,  how  many  rows  and columns you want to use for your
  patterns.   Then  you can click with the mouse in these squares which you
  want  to  have  black  in  your pattern.  When finished, select "Take" in
  order  to  use  this  pattern.   The grid should become empty and you can
  generate  the  next  one.   You can page through the patterns forward and
  backwards  using  the "Prev" and "Next" gadgets.  If you want to remove a
  pattern from the list choose "Remove".  To clear the grid choose "Clear".
  A maximum of 30 patterns can be edited.
--Old:   You  can change your edited patterns later or a given pattern file
  by  selecting  "Edit old", which always refers to the current patterns in
  memory.

-Info:   Gives  information about free memory, size, number, parameters and
 layout  of  the patterns.  When the mode is set to backpropagation it also
 displays  the  learning  error.   The net has performed its task very well
 when the error is nearby zero.  It can take a few seconds to calculate it,
 so don't panic!

-About: To see the beautiful greeting-picture again.

-Quit:  should be no problem!


3.2 Action:
-----------

-Learn:   Selecting  "learn"  starts  either  the  backpropagation-  or the
hopfield-learning  algorithm,  depending  on  your  choice of the mode.

 If  the  learning-process  is  normally  completed, you'll get the message
 "Learning   finished!!!".    If   you  have  choosen  the  backpropagation
 algorithm, you have to enter the number of learning iterations, that means
 how often a (randomly choosen) pattern is presented to the net in order to
 learn it.  In most cases you will need 500 to 20000 iterations to get good
 results.   It can sometimes take a few hours (depending on the size of the
 patterns)  to  finish.   A display shows, how many iterations are still to
 do,  it  is  refreshed always after 10 iterations (in order to save time).
 By striking any key a requester will apear and ask you, if you really want
 to  abort.   Select  Abort  to abort or continue to (guess what) continue.
 The  results  up  to  this  moment won't be lost!.  In Hopfield mode for a
 pattern  file  it's  only  necessary  to  call  this menu item once and it
 doesn't make sense here to call "Learn" several times.

-Question:   After  selecting  "question", a window will appear, displaying
 the  mode (of the network) in the first line .  Below you see three grids:
 the  left  one  is for your input, the right one is used by the network to
 display  the  answer  of  the  network to your question and the middle one
 displays the hidden units of the network (not in the Hopfield-mode), which
 show the internal represantation of the current pattern.

 Click  with  the left mouse button in those squares of the input grid, you
 want to have black.  After clicking the "question"-gadget the network will
 begin  its  search.   In  Hopfield-mode  you  can see on the left side the
 current energy-value.  Here it can take a while until you see the answer.

 "Clear" will clear all three grids.

 "Re-Take"  (in Backpropagation-mode) will take the current output again as
 a input and automatically start the query.

 "Black&White" / "Grey" is only available in the Backpropagation-mode.  The
 hidden  units  and  the output units contain internally values between 0.0
 and  1.0.   In "Black&White"-mode all values higher than 0.5 are displayed
 black  the  other  values  are displayed white.  In "Grey"-mode the values
 from  0.0  to  0.1  are displayed white, the values from 0.1 to 0.2 in the
 brightest  grey  and  so  on.   So  in  this mode you get more information
 espacially about how certain the net is about its answer.

 "Go-On"  (only  in  Hopfield-mode)  :  If the network has found a solution
 which  does not correspond to one of the given patterns, it is possible to
 "unlearn"  this  pattern  by  clicking  "Go-On".   This  will  reduce  the
 probability  of finding this solution.  You should not use this option too
 often.   The  efficiency  of  this  unlearning-procedure  is  given by the
 parameter Mu (see below).



3.3 Settings:
-------------

-Parameter:   Here  you can set several parameters for the current selected
 mode.   For  all  parameters  there  is  a  default-value,  which  will be
 displayed  here  and  used  by  the algorithm unless you change the value.
 When  entering erroneous values outside of the allowed interval the screen
 will flash and the entered value will be replaced by the default value.
--Backpropagation:   Eta determines the learning rate and must have a value
  between  0  and  1.   The bigger Eta, the faster the net will learn.  But
  with  a  value of Eta nearby 1, there will the danger, that the net won't
  learn  all  patterns.  It's sometimes a good strategy to begin with large
  values  and  then  to  reduce  Eta  down  to  a  very small value.  Theta
  determines  the  bias of each unit (see literature) and should have small
  positive  or  negative values.  The number of hidden units tells the net,
  how  many  units  it  can  use  to find an internal representation of the
  patterns.  As a default value there is one hidden unit for one pattern.
--Hopfield:   Theta:   As in Backpropagation.  Mu:  Must be between 0 and 1
  and  influences  the  unlearning-procedure  invoced  by  "Go-On"  in  the
  question-requester.   The  current  output  pattern  will  be  completely
  deleted, if Mu=1.0.  Unfortunately it also will reduce the probability of
  finding the other patterns.  With Mu=0.0 "Go-On" will cause no changes in
  the weights.  In this case the net only will continue its search.

-Mode:   Here you can set the program to one of the two implemented network
 types  (boltzmann will be realized in our next version).  The behaviour of
 the complete program depends on your choice in this menu item.

-Status:   This  is  only  for  specialists  of  interest.  It displays the
 weight-matrix in  a  very  (!) simple way.  In our next version we plan to
 improve  this display.  You see a lot of "+" and "-", where "+" stands for
 exhibitory  connections  (weight  >= 0.0) , "-" for inhibitory connections
 (weight < 0.0).

-Random-Gen.:   The  random  number  generator  is  always initialized by a
 special variable called Seed. In order to change the random aspects of the
 networks  (e.g.  when initializing the weights with random values) you can
 change the Seed-value. Seed must be in the range of 0..65536.

-Colour:   If  you  like  it,  you can change the colours.  The last eleven
 colours are  used  for  the  grey-scaled  representation of the answers in
 backpropagation-mode and should change continuously (use spread).

-Close WB:  Closes the workbench, if there are no open windows with running
 programs. The workbench will open again when you quit the program.
 This option may speed up the program a bit.

4. Notes to the internal structure of the program
=================================================

The pattern file is an ASCII file which has the following structure:

1.line : Number of patterns
2.line : Number of columns
3.line : Number of rows
Then  in  each line follows one line of the pattern containing only '.' and
'*'.  The patterns are separated one blank line. Even the last Pattern needs +
this blank line.

If  you  have,  for  example,  two  patterns with 7 rows and 8 columns, the
pattern file looks like this:
2
8
7
...**...
..***...
.****...
...**...
...**...
...**...
...**...
.******.

..****..
.**..**.
.....**.
....**..
...**...
..**....
.**.....
.******.

(eof)

The  name-convention  of  the  pattern-files is:
       "Pattern" + #Rows + "x" + #Columns + "." + #Patterns.
So  "Pattern10x08.15" stands for a pattern file with 15 patterns, having 10
rows and 8 columns.

The files created by "Save all" can't be edited by an editor.


5. Known Bugs:
==============

When entering erroneous parameters into a gadget, the cursor in this gadget
will not disappear.
If you should find more bugs, we'd be happy to get a note from you!

6. Appendix:
============

Interesting books:

J. McClelland, D. Rumelhart: Parallel Distributed Processing. Explorations
in the Microstructure of Cognition. The MIT Press 1986.

E. Schoeneburg. Neuronale Netzwerke. Markt und Technik 1990. (in german)