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# AI::NeuralNet::BackProp 0.77

Date Added: August 04, 2010  |  Visits: 615

AI::NeuralNet::BackProp is a simple back-prop neural net that uses Deltas and Hebbs rule. SYNOPSIS use AI::NeuralNet::BackProp; # Create a new network with 1 layer, 5 inputs, and 5 outputs. my \$net = new AI::NeuralNet::BackProp(1,5,5); # Add a small amount of randomness to the network \$net->random(0.001); # Demonstrate a simple learn() call my @inputs = ( 0,0,1,1,1 ); my @ouputs = ( 1,0,1,0,1 ); print \$net->learn(@inputs, @outputs),"n"; # Create a data set to learn my @set = ( [ 2,2,3,4,1 ], [ 1,1,1,1,1 ], [ 1,1,1,1,1 ], [ 0,0,0,0,0 ], [ 1,1,1,0,0 ], [ 0,0,0,1,1 ] ); # Demo learn_set() my \$f = \$net->learn_set(@set); print "Forgetfulness: \$f unitn"; # Crunch a bunch of strings and return array refs my \$phrase1 = \$net->crunch("I love neural networks!"); my \$phrase2 = \$net->crunch("Jay Lenno is wierd."); my \$phrase3 = \$net->crunch("The rain in spain..."); my \$phrase4 = \$net->crunch("Tired of word crunching yet?"); # Make a data set from the array refs my @phrases = ( \$phrase1, \$phrase2, \$phrase3, \$phrase4 ); # Learn the data set \$net->learn_set(@phrases); # Run a test phrase through the network my \$test_phrase = \$net->crunch("I love neural networking!"); my \$result = \$net->run(\$test_phrase); # Get this, it prints "Jay Leno is networking!" ... LOL! print \$net->uncrunch(\$result),"n"; AI::NeuralNet::BackProp is the flagship package for this file. It implements a nerual network similar to a feed-foward, back-propagtion network; learning via a mix of a generalization of the Delta rule and a disection of Hebbs rule. The actual neruons of the network are implemented via the AI::NeuralNet::BackProp::neuron package.

 Requirements: No special requirements Platforms: Linux Keyword: Ai,  Aineuralnetbackprop,  Backprop,  Crunch,  Learn,  Libraries,  Net-,  Network,  Neural,  Neural Net,  Neuralnet,  Programming,  Simple Users rating: 0/10