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. Its documented here for convenience only..

Requirements:	No special requirements
Platforms:	Linux
Keyword:	Ai,  Ainnflexbackprop,  Backprop,  Libraries,  Network,  Network-,  Neural,  Neural Net,  Nnflex,  Perl,  Programming,  Project
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