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			randomweights=>MAX VALUE OF STARTING WEIGHTS);

Add layer adds whatever parameters you specify as attributes of the layer, so if you want to implement additional parameters simply use them in your calling code.

Add layer returns success or failure, and if successful adds a layer object to the $network->{'layers'} array. This layer object contains an attribute $layer->{'nodes'}, which is an array of nodes in the layer.

=head3 init

 Syntax:

 $network->init();

Initialises connections between nodes, sets initial weights. The base AI::NNFlex init method implementes connections backwards and forwards from each node in each layer to each node in the preceeding and following layers. 

init adds the following attributes to each node:

=over

=item *
{'connectedNodesWest'}->{'nodes'} - an array of node objects connected to this node on the west/left

=item *
{'connectedNodesWest'}->{'weights'} - an array of scalar numeric weights for the connections to these nodes


=item *
{'connectedNodesEast'}->{'nodes'} - an array of node objects connected to this node on the east/right

=item *
{'connectedNodesEast'}->{'weights'} - an array of scalar numeric weights for the connections to these nodes

=back

The connections to easterly nodes are not used in feedforward networks.
Init also implements the Bias node if specified in the network config.

=head3 connect

Syntax:
 $network->connect(fromlayer=>1,tolayer=>0);
 $network->connect(fromnode=>'1,1',tonode=>'0,0');

Connect allows you to manually create connections between layers or nodes, including recurrent connections back to the same layer/node. Node indices must be LAYER,NODE, numbered from 0.

Weight assignments for the connection are calculated based on the network wide weight policy (see INIT).

=head3 lesion

 $network->lesion (nodes=>PROBABILITY,connections=>PROBABILITY)

 Damages the network.

B<PROBABILITY>

A value between 0 and 1, denoting the probability of a given node or connection being damaged.

Note: this method may be called on a per network, per node or per layer basis using the appropriate object.

=head1 EXAMPLES

See the code in ./examples. For any given version of NNFlex, xor.pl will contain the latest functionality.


=head1 PREREQs

None. NNFlex should run OK on any version of Perl 5 >. 


=head1 ACKNOWLEDGEMENTS

Phil Brierley, for his excellent free java code, that solved my backprop problem

Dr Martin Le Voi, for help with concepts of NN in the early stages

Dr David Plaut, for help with the project that this code was originally intended for.

Graciliano M.Passos for suggestions & improved code (see SEE ALSO).

Dr Scott Fahlman, whose very readable paper 'An empirical study of learning speed in backpropagation networks' (1988) has driven many of the improvements made so far.

=head1 SEE ALSO

 AI::NNFlex::Backprop
 AI::NNFlex::Feedforward
 AI::NNFlex::Mathlib
 AI::NNFlex::Dataset

 AI::NNEasy - Developed by Graciliano M.Passos 
 (Shares some common code with NNFlex)
 

=head1 TODO

 Lots of things:

 clean up the perldocs some more
 write gamma modules
 write BPTT modules
 write a perceptron learning module
 speed it up
 write a tk gui

=head1 CHANGES

v0.11 introduces the lesion method, png support in the draw module and datasets.

v0.12 fixes a bug in reinforce.pm & adds a reflector in feedforward->run to make $network->run($dataset) work.

v0.13 introduces the momentum learning algorithm and fixes a bug that allowed training to proceed even if the node activation function module can't be loaded

v0.14 fixes momentum and backprop so they are no longer nailed to tanh hidden units only.

v0.15 fixes a bug in feedforward, and reduces the debug overhead

v0.16 changes some underlying addressing of weights, to simplify and speed  

v0.17 is a bugfix release, plus some cleaning of UI

v0.20 changes AI::NNFlex to be a base class, and ships three different network types (i.e. training algorithms). Backprop & momentum are both networks of the feedforward class, and inherit their 'run' method from feedforward.pm. 0.20 also fixes a who...

v0.21 cleans up the perldocs more, and makes nnflex more distinctly a base module. There are quite a number of changes in Backprop in the v0.21 distribution.