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BackProp.pm  view on Meta::CPAN

			# Inner loop connects every neuron in this 'layer' to one input of every neuron in
			# the next 'layer'. Remeber, layers only exist in terms of where the connections
			# are divided. For example, if a person requested 2 layers and 3 neurons per layer,
			# then there would be 6 neurons in the {NET}->[] list, and $div would be set to
			# 3. So we would loop over and every 3 neurons we would connect each of those 3 
			# neurons to one input of every neuron in the next set of 3 neurons. Of course, this
			# is an example. 3 and 2 are set by the new() constructor.
			
			# Flag values:
			# 0 - (default) - 
			# 	My feed-foward style: Each neuron in layer X is connected to one input of every
			#	neuron in layer Y. The best and most proven flag style.
			#
			#   ^   ^   ^               
			#	O\  O\ /O       Layer Y
			#   ^\\/^/\/^
			#	| //|\/\|
			#   |/ \|/ \|		
			#	O   O   O       Layer X
			#   ^   ^   ^
			#
			# 1	-
			#	In addition to flag 0, each neuron in layer X is connected to every input of 
			#	the neurons ahead of itself in layer X.
			# 2 - ("L-U Style") - 
			#	No, its not "Learning-Unit" style. It gets its name from this: In a 2 layer, 3
			#	neuron network, the connections form a L-U pair, or a W, however you want to look
			#	at it.   
			#
			#   ^   ^   ^
			#	|	|	|
			#	O-->O-->O
			#   ^   ^   ^
			#	|	|   |
			#   |   |   |
			#	O-->O-->O

BackProp.pm  view on Meta::CPAN

There is an optional parameter of $outputs, which specifies the number
of output neurons to provide. If $outputs is not specified, $outputs
defaults to equal $size. $outputs may not exceed $size. If $outputs
exceeds $size, the new() constructor will return undef.

The optional parameter, $topology_flag, defaults to 0 when not used. There are
three valid topology flag values:

B<0> I<default>
My feed-foward style: Each neuron in layer X is connected to one input of every
neuron in layer Y. The best and most proven flag style.

	^   ^   ^               
	O\  O\ /O       Layer Y
	^\\/^/\/^
	| //|\/\|
	|/ \|/ \|		
	O   O   O       Layer X
	^   ^   ^


(Sorry about the bad art...I am no ASCII artist! :-)


B<1>
In addition to flag 0, each neuron in layer X is connected to every input of 
the neurons ahead of itself in layer X.

B<2> I<("L-U Style")>
No, its not "Learning-Unit" style. It gets its name from this: In a 2 layer, 3
neuron network, the connections form a L-U pair, or a W, however you want to look
at it.   


	^   ^   ^
	|   |   |
	O-->O-->O
	^   ^   ^
	|   |   |
	|   |   |

docs.htm  view on Meta::CPAN

Returns a newly created neural network from an <CODE>AI::NeuralNet::BackProp</CODE>
object. The network will have <CODE>$layers</CODE> number layers in it
and each layer will have <CODE>$size</CODE> number of neurons in that layer.
<P>There is an optional parameter of $outputs, which specifies the number
of output neurons to provide. If $outputs is not specified, $outputs
defaults to equal $size. $outputs may not exceed $size. If $outputs
exceeds $size, the <CODE>new()</CODE> constructor will return undef.</P>
<P>The optional parameter, $topology_flag, defaults to 0 when not used. There are
three valid topology flag values:</P>
<P><STRONG>0</STRONG> <EM>default</EM>
My feed-foward style: Each neuron in layer X is connected to one input of every
neuron in layer Y. The best and most proven flag style.</P>
<PRE>
        ^   ^   ^               
        O\  O\ /O       Layer Y
        ^\\/^/\/^
        | //|\/\|
        |/ \|/ \|               
        O   O   O       Layer X
        ^   ^   ^</PRE>
<P>(Sorry about the bad art...I am no ASCII artist! :-)</P>
<P><STRONG>1</STRONG>
In addition to flag 0, each neuron in layer X is connected to every input of 
the neurons ahead of itself in layer X.</P>
<P><STRONG>2</STRONG> <EM>(``L-U Style'')</EM>
No, its not ``Learning-Unit'' style. It gets its name from this: In a 2 layer, 3
neuron network, the connections form a L-U pair, or a W, however you want to look
at it.</P>
<PRE>
        ^   ^   ^
        |   |   |
        O--&gt;O--&gt;O
        ^   ^   ^
        |   |   |
        |   |   |
        O--&gt;O--&gt;O