# NAME

AI::Nerl::Network - 3-layer neural network with backpropagation

# SYNOPSIS

```
use AI::Nerl::Network;
use PDL;
my $x = pdl([0,0,1,1],
[0,1,0,1],
[1,0,0,1]);
my $y = pdl([1,1,0,1]);
my $nn = AI::Nerl::Network->new(
l1 => 3, # 3 inputs
l2 => 18, # 18 hidden neurons
l3 => 1, # 1 output
alpha => .3, # learning rate
lambda => .01, # 'squashing' parameter
);
$nn->train($x,$y, passes=>45);
my ($cost,$num_correct) = $nn->cost($x,$y);
#$nn wasn't programmed with this input. could be anything:
print $nn->run(pdl([0,0,0]));
```

# DESCRIPTION

# METHODS

## train($x,$y, %params)

Train with backpropagation using $x as input & $y as target. $x and $y are both pdls. If there are multiple cases, each one will occupy a column (dimension 2) of the pdl. If your dimensions are off, you will experience an pdl error of some sort.

### %params

#### passes

number of passes.

## run($x)

` $output = $nn->run($x);`

## cost($x,$y)

` ($cost,$num_correct) = $nn->cost($x,$y);`

Calculate the 'cost' of the network. This is basically the difference between the actual output ($nn->run($x)) and the the target output($y), added to the sum of the neural weights if you're penalizing weights with lambda. The cost should **Always** decrease after being trained with ($x,$y).

This function returns both the cost, and the number of "correct" responses if using output neurons for classification.

# SEE ALSO

http://en.wikipedia.org/wiki/Feedforward_neural_network#Multi-layer_perceptron

http://en.wikipedia.org/wiki/Backpropagation

# AUTHOR

Zach Morgan `<zpmorgan@gmail.com>`

# COPYRIGHT

Copyright 2012 by Zach Morgan

This package is free software; you can redistribute it and/or modify it under the same terms as Perl itself.