Perl ABCAI::MXNet that performs artificial intelligence and machine learning with Perl looks amazing
I recently learned. AI::MXNet, which does artificial intelligence programming in Perl, looks amazing. It is a library that performs machine learning using a technique called deep learning.
It is said that it was accepted as Perl's official library by MZNet of the official Amazon cloud.
The interface seems to be almost the same as Python's MXNet library. It is a Perl binding of MXNet written in C ++.
Performing artificial intelligence programming with Perl AI::MXNet (CPAN)
It seems that quite a lot of MXNet features are available from Perl. If you are interested in artificial intelligence programming in Perl, give it a try!
If you have a library, you can do artificial intelligence programming even in Perl. It's amazing! It seems that GPU can also be used!
# # Convolutional NN for recognizing hand-written digits in MNIST dataset
# # It's considered "Hello, World" for Neural Networks
# # For more info about the MNIST problem please refer to http://neuralnetworksanddeeplearning.com/chap1.html
use strict;
use warnings;
use AI::MXNet qw(mx);
use AI::MXNet::TestUtils qw(GetMNIST_ubyte);
use Test::More tests => 1;
# symbol net
my $batch_size = 100;
# ## model
my $data = mx->symbol->Variable('data');
my $conv1 = mx->symbol->Convolution(data => $data, name =>'conv1', num_filter => 32, kernel => [3,3], stride => [2,2]);
my $bn1 = mx->symbol->BatchNorm(data => $conv1, name => "bn1");
my $act1 = mx->symbol->Activation(data => $bn1, name =>'relu1', act_type => "relu");
my $mp1 = mx->symbol->Pooling(data => $act1, name =>'mp1', kernel => [2,2], stride => [2,2], pool_type =>'max') ;
my $conv2 = mx->symbol->Convolution(data => $mp1, name =>'conv2', num_filter => 32, kernel => [3,3], stride => [2,2]);
my $bn2 = mx->symbol->BatchNorm(data => $conv2, name => "bn2");
my $act2 = mx->symbol->Activation(data => $bn2, name =>'relu2', act_type => "relu");
my $mp2 = mx->symbol->Pooling(data => $act2, name =>'mp2', kernel => [2,2], stride => [2,2], pool_type =>'max') ;
my $fl = mx->symbol->Flatten(data => $mp2, name => "flatten");
my $fc1 = mx->symbol->FullyConnected(data => $fl, name => "fc1", num_hidden => 30);
my $act3 = mx->symbol->Activation(data => $fc1, name =>'relu3', act_type => "relu");
my $fc2 = mx->symbol->FullyConnected(data => $act3, name =>'fc2', num_hidden => 10);
my $softmax = mx->symbol->SoftmaxOutput(data => $fc2, name =>'softmax');
# check data
GetMNIST_ubyte ();
my $train_dataiter = mx->io->MNISTIter({{
image => "data/train-images-idx3-ubyte",
label => "data/train-labels-idx1-ubyte",
data_shape => [1, 28, 28],
batch_size => $batch_size, shuffle => 1, flat => 0, silent => 0, seed => 10});
my $val_dataiter = mx->io->MNISTIter({{
image => "data/t10k-images-idx3-ubyte",
label => "data/t10k-labels-idx1-ubyte",
data_shape => [1, 28, 28],
batch_size => $batch_size, shuffle => 1, flat => 0, silent => 0});
my $n_epoch = 1;
my $mod = mx->mod->new(symbol => $softmax);
$mod->fit(
$train_dataiter,
eval_data => $val_dataiter,
optimizer_params => {learning_rate => 0.01, momentum => 0.9},
num_epoch => $n_epoch
);
my $res = $mod->score($val_dataiter, mx->metric->create('acc'));
ok ($res->{accuracy}> 0.8);