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	README file for the example files encoder.xxx
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Description:	This network learns to code 8 units to 3 units and encode 
============	3 units to 8 units.

The task of an n-m-n-encoder network is to code a 1-of-n coding of n
input units to m = log(n) hidden units and to encode the outputs of
these m units again to a 1-of-n representation of n units.

What makes the problem interesting is that the network is
required to have a small number of hidden units.
Note that these input and output values are internally treated as real
values by the network, although the problem is a binary problem.

Encoders with m = log(n) are called 'tight' encoders, 
	 with m < log(n) are called 'supertight' encoders, 
	 with m > log(n) are called 'loose' encoders.

Note that obviously for this problem no shortcut connections from
input to output are allowed.


Pattern-Files:	encoder.pat
==============

Each of the eight input patterns consists of one '1' at Position i and
'0' at the remaining positions. The output consists of the identical
pattern, therefore the mapping to be learned is the identity
mapping. 


Network-Files:	encoder.net
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Config-Files:	encoder.cfg
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Hints:
======

The following table shows some learning functions one may use to train
the network. In addition, it shows the learning-parameters and the
number of cycles we needed to train the network successfully. These
parameters have not been obtained with extensive studies of
statistical significance. They are given as hints to start your own
training sessions, but should not be cited as optimal or used in
comparisons of learning procedures or network simulators. Surely you
can do better...

Learning-Function               Learning-Parameters       Cycles

Std.-Backpropagation            2.0                         750
Backpropagation with Momentum   0.8  0.6   0.1              300
Quickprop                       0.2  1.75  0.0001            75  
Rprop                           0.2                          75

You may easily build your own n-m-n encoder networks either by adding
or deleting hidden units or by using the BIGNET tool from the info
panel.

To create the 8-4-8 encoder by adding a hidden unit select one hidden
unit with left mouse button (it turns yellow), make it the curent
target unit by pressing the middle mouse button and enter 'u c a'
(units copy all) on the keyboard while the mouse pointer is inside the
2D display window. This creates a duplicate of the selected hidden
unit with all input and output connections copied from the
original. Now initialize the network and train the enlarged 8-4-8
network. Try also the 8-2-8 encoder!

Note that the time to train an n-(log n)-n encoder grows exponentially
with n. A 128-7-128 encoder can still be trained with SNNS, larger
values give problems.


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	End of README file
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