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

AI::NNFlex::Backprop Description

AI::NNFlex::Backprop project is a fast, pure perl backprop Neural Net simulator. 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(,, ,]); 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. Requirements: · Perl

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