AI::NNFlex::Backprop 0.24

AI::NNFlex::Backprop project is a fast, pure perl backprop Neural Net simulator.

SYNOPSIS

use AI::NNFlex::Backprop;

my $network = AI::NNFlex::Backprop->new(config parameter=>value);

$network->add_layer(nodes=>x,activationfunction=>'function');

$network->init();



use AI::NNFlex::Dataset;

my $dataset = AI::NNFlex::Dataset->new([
[INPUTARRAY],[TARGETOUTPUT],
[INPUTARRAY],[TARGETOUTPUT]]);

my $sqrError = 10;

while ($sqrError >0.01)

{

$sqrError = $dataset->learn($network);

}

$network->lesion({'nodes'=>PROBABILITY,'connections'=>PROBABILITY});

$network->dump_state(filename=>'badgers.wts');

$network->load_state(filename=>'badgers.wts');

my $outputsRef = $dataset->run($network);

my $outputsRef = $network->output(layer=>2,round=>1);

AI::NNFlex::Backprop is a class to generate feedforward, backpropagation neural nets. It inherits various constructs from AI::NNFlex & AI::NNFlex::Feedforward, but is documented here as a standalone.
The code should be simple enough to use for teaching purposes, but a simpler implementation of a simple backprop network is included in the example file bp.pl. This is derived from Phil Brierleys freely available java code at www.philbrierley.com.

AI::NNFlex::Backprop leans towards teaching NN and cognitive modelling applications. Future modules are likely to include more biologically plausible nets like DeVries & Principes Gamma model.

Full documentation for AI::NNFlex::Dataset can be found in the modules own perldoc. It's documented here for convenience only.

Product's homepage

Requirements:

· Perl