AI-Nerl

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lib/AI/Nerl.pm  view on Meta::CPAN

Zach Morgan

=cut



has scale_input => (
   is => 'ro',
   isa => 'Num',
   required => 0,
   default => 0,
);
has l2 => ( #hidden layer.
   is => 'ro',
   isa => 'Num',
   default => 30,
);

has [qw/ train_x 
         train_y /] => (
   is => 'ro',
   isa => 'PDL',
   required => 0, #training can be done manually.
);
has [qw/ test_x cv_x
         test_y cv_y /] => (

lib/AI/Nerl.pm  view on Meta::CPAN

has network => (
   required=>0,
   is => 'rw',
   isa => 'AI::Nerl::Network',
);

has passes=> (
   is => 'rw',
   isa => 'Int',
   default => 10,
);

has basis => (
   is => 'ro',
   isa => 'AI::Nerl',
   required => 0,
);

#initialize $self->network, but don't train.
# any parameters AI::Nerl::Network takes are fine here.

lib/AI/Nerl/Network.pm  view on Meta::CPAN

=cut

# Simple nn with 1 hidden layer
# train with $nn->train(data,labels);

has scale_input => (
   is => 'ro',
   required => 0,
   isa => 'Num',
   default => 0,
);

# number of input,hidden,output neurons
has [qw/ l1 l2 l3 /] => (
   is => 'ro',
   isa => 'Int',
);

has theta1 => (
   is => 'ro',

lib/AI/Nerl/Network.pm  view on Meta::CPAN

);
has b2 => (
   is => 'ro',
   isa => 'PDL',
   lazy => 1,
   builder => '_mk_b2',
);
has alpha => ( #learning rate
   isa => 'Num',
   is => 'rw',
   default => .6,
);
has lambda => (
   isa => 'Num',
   is => 'rw',
   default => .01,
);

sub _mk_theta1{
   my $self = shift;
   return grandom($self->l1, $self->l2) * .01;
}
sub _mk_theta2{
   my $self = shift;
   return grandom($self->l2, $self->l3) * .01;
}