AI-MXNet
view release on metacpan - search on metacpan
view release on metacpan or search on metacpan
lib/AI/MXNet/Metric.pm
lib/AI/MXNet/Executor/Group.pm
lib/AI/MXNet/NDArray.pm
lib/AI/MXNet/RNN/Cell.pm
lib/AI/MXNet/RNN/IO.pm
lib/AI/MXNet/LRScheduler.pm
lib/AI/MXNet/Callback.pm
lib/AI/MXNet/IO.pm
lib/AI/MXNet/Module/Bucketing.pm
lib/AI/MXNet/Module/Base.pm
lib/AI/MXNet/TestUtils.pm
lib/AI/MXNet/Logging.pm
README
lib/AI/MXNet.pm view on Meta::CPAN
=head1 SYNOPSIS
## 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 => $act1, name => 'mp1', kernel => [2,2], stride =>[2,2], pool_type=>'max');
lib/AI/MXNet/Optimizer.pm view on Meta::CPAN
($acc_g + $self->epsilon)->sqrt
*
$grad;
$acc_delta .= $self->rho * $acc_delta + (1 - $self->rho) * $current_delta * $current_delta;
$weight -= $current_delta + $wd * $weight;
}
__PACKAGE__->register;
# For test use
package AI::MXNet::Test;
use Mouse;
extends 'AI::MXNet::Optimizer';
# Create a state to duplicate weight
method create_state(Index $index, AI::MXNet::NDArray $weight)
{
return AI::MXNet::NDArray->zeros(
$weight->shape,
ctx => $weight->context
lib/AI/MXNet/TestUtils.pm view on Meta::CPAN
package AI::MXNet::TestUtils;
use strict;
use warnings;
use PDL;
use Carp;
use Scalar::Util qw(blessed);
use AI::MXNet::Function::Parameters;
use Exporter;
use base qw(Exporter);
@AI::MXNet::TestUtils::EXPORT_OK = qw(same reldiff almost_equal GetMNIST_ubyte
GetCifar10 pdl_maximum pdl_minimum mlp2 conv
check_consistency zip assert enumerate same_array dies_like);
use constant default_numerical_threshold => 1e-6;
=head1 NAME
AI::MXNet::TestUtils - Convenience subs used in tests.
=head2 same
Test if two pdl arrays are the same
Parameters
----------
a : pdl
b : pdl
=cut
func same(PDL $a, PDL $b)
{
return ($a != $b)->sum == 0;
t/AI-MXNet.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 1;
BEGIN { use_ok('AI::MXNet') };
t/test_attr.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 14;
use AI::MXNet qw(mx);
use Storable;
sub contains
{
my ($x, $y) = @_;
while(my ($k, $v) = each %$x)
{
return 0 unless exists $y->{$k};
if(ref $y->{$k} and ref $y->{$k} eq 'HASH')
t/test_conv.t view on Meta::CPAN
use strict;
use warnings;
use AI::MXNet qw(mx);
use AI::MXNet::TestUtils qw(GetMNIST_ubyte);
use Test::More tests => 1;
## speed up the tests when gpu present
my $gpu_present = (`perl -e 'use AI::MXNet qw(mx); print mx->nd->ones([1], ctx => mx->gpu(0))->asscalar' 2>/dev/null` eq '1');
# symbol net
my $batch_size = 100;
### model
my $data = mx->symbol->Variable('data');
my $conv1= mx->symbol->Convolution(data => $data, name => 'conv1', num_filter => 32, kernel => [3,3], stride => [2,2]);
t/test_executor.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 2283;
use AI::MXNet qw(mx);
use AI::MXNet::TestUtils qw(reldiff pdl_maximum pdl_minimum);
use PDL;
sub check_bind_with_uniform
{
my ($uf, $gf, $dim, $sf, $lshape, $rshape) = @_;
my $shape = (random($dim)*int(1000**(1.0/$dim))+1)->floor->unpdl;
my $lhs = mx->symbol->Variable('lhs');
my $rhs = mx->symbol->Variable('rhs');
my $ret;
if(defined $sf)
t/test_infer_shape.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 18;
use AI::MXNet qw(mx);
use AI::MXNet::TestUtils qw(mlp2);
sub _test_shapes
{
my ($sym, $arg_shapes, %expected_shapes) = @_;
my %arg_shape_dict;
@arg_shape_dict{ @{ $sym->list_arguments() } } = @{ $arg_shapes };
while(my ($k, $v) = each %expected_shapes)
{
is_deeply($arg_shape_dict{$k}, $v);
}
t/test_io.t view on Meta::CPAN
use AI::MXNet qw(mx);
use Test::More tests => 31;
use AI::MXNet::TestUtils qw(same reldiff GetMNIST_ubyte GetCifar10);
use PDL;
use PDL::Types;
use PDL::NiceSlice;
$|++;
sub test_Cifar10Rec()
{
GetCifar10();
my $dataiter = mx->io->ImageRecordIter({
t/test_io_image.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 1;
use AI::MXNet qw(mx);
use Time::HiRes qw(time);
sub run_imageiter
{
my ($path_rec, $n, $batch_size) = @_;
$batch_size //= 32;
my $data = mx->img->ImageIter(
batch_size=>$batch_size,
data_shape=>[3, 224, 224],
t/test_kvstore.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 38;
use AI::MXNet qw(mx);
my $shape = [4, 4];
my $keys = [5,7,9];
sub init_kv
{
# init kv
my $kv = mx->kv->create();
# single
t/test_model_parallel.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 4;
use AI::MXNet qw(mx);
use AI::MXNet::TestUtils qw(reldiff);
use AI::MXNet::Base;
sub test_chain
{
my $ctx1 = mx->cpu(0);
my $ctx2 = mx->cpu(1);
my $n = 2;
my $data1 = mx->sym->Variable('data1');
my $data2 = mx->sym->Variable('data2');
my $data3 = mx->sym->Variable('data2');
t/test_module.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 257;
use AI::MXNet qw(mx);
use AI::MXNet::Base;
use AI::MXNet::TestUtils qw(almost_equal enumerate same_array dies_like);
use Data::Dumper;
sub test_module_layout
{
my $sym = mx->sym->Variable('data');
$sym = mx->sym->Activation(data=>$sym, act_type=>'relu', __layout__=>'TNC');
my $dshape = [3, 8, 7];
my $mod = mx->mod->Module(
$sym,
t/test_multi_device_exec.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 10;
use AI::MXNet qw(mx);
use AI::MXNet::Base;
sub test_ctx_group
{
my ($data, $fc1, $act1);
{
local($mx::AttrScope) = mx->AttrScope(ctx_group=>'stage1');
$data = mx->symbol->Variable('data');
$fc1 = mx->symbol->FullyConnected(data => $data, name=>'fc1', num_hidden=>128);
t/test_ndarray.t view on Meta::CPAN
use strict;
use warnings;
use AI::MXNet qw(mx);
use AI::MXNet::TestUtils qw(almost_equal);
use Test::More tests => 10;
sub test_ndarray_reshape
{
my $tensor = mx->nd->array([[[1, 2], [3, 4]],
[[5, 6], [7, 8]]]);
my $true_res = mx->nd->arange(stop => 8) + 1;
is_deeply($tensor->reshape([-1])->aspdl->unpdl, $true_res->aspdl->unpdl);
$true_res = mx->nd->array([[1, 2, 3, 4],
[5, 6, 7, 8]]);
is_deeply($tensor->reshape([2, -1])->aspdl->unpdl, $true_res->aspdl->unpdl);
t/test_optimizers.t view on Meta::CPAN
$weight32 += $mom;
}
}
my $tmp = $weight32->astype($weight->dtype);
$tmp->copyto($weight);
}
}
package main;
use Test::More tests => 1314;
use AI::MXNet::Base;
use PDL::NiceSlice;
use AI::MXNet::TestUtils qw(same reldiff almost_equal);
use AI::MXNet::Function::Parameters;
func compare_optimizer($opt1, $opt2, $shape, $dtype)
{
my $w1 = mx->random->uniform({shape => $shape, dtype=>$dtype});
my $g1 = mx->random->uniform({shape => $shape, dtype=>$dtype});
my $w2 = $w1->copyto(mx->cpu());
my $g2 = $g1->copyto(mx->cpu());
t/test_random.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 8;
use AI::MXNet qw(mx);
use AI::MXNet::TestUtils qw(same);
sub check_with_device
{
my ($device) = @_;
my ($a, $b) = (-10, 10);
my ($mu, $sigma) = (10, 2);
my $shape = [100, 100];
mx->random->seed(128);
my $ret1 = mx->random->normal($mu, $sigma, $shape, { ctx => $device });
my $un1 = mx->random->uniform($a, $b, $shape, { ctx => $device });
t/test_recordio.t view on Meta::CPAN
use strict;
use warnings;
use AI::MXNet qw(mx);
use Test::More tests => 1711;
use File::Temp qw/tempfile/;
use PDL;
sub test_recordio
{
my ($fd, $frec) = tempfile();
my $N = 255;
my $writer = mx->recordio->MXRecordIO($frec, 'w');
for my $i (0..$N-1)
t/test_rnn.t view on Meta::CPAN
use strict;
use warnings;
use AI::MXNet qw(mx);
use AI::MXNet::TestUtils qw(same);
use PDL;
use Test::More tests => 54;
sub test_rnn
{
my $cell = mx->rnn->RNNCell(100, prefix=>'rnn_');
my ($outputs) = $cell->unroll(3, input_prefix=>'rnn_');
$outputs = mx->sym->Group($outputs);
is_deeply([sort keys %{$cell->params->_params}], ['rnn_h2h_bias', 'rnn_h2h_weight', 'rnn_i2h_bias', 'rnn_i2h_weight']);
is_deeply($outputs->list_outputs(), ['rnn_t0_out_output', 'rnn_t1_out_output', 'rnn_t2_out_output']);
my (undef, $outs, undef) = $outputs->infer_shape(rnn_t0_data=>[10,50], rnn_t1_data=>[10,50], rnn_t2_data=>[10,50]);
is_deeply($outs, [[10, 100], [10, 100], [10, 100]]);
t/test_symbol.t view on Meta::CPAN
use strict;
use warnings;
use Test::More tests => 98;
use AI::MXNet qw(mx);
use AI::MXNet::TestUtils qw(mlp2 conv check_consistency zip assert enumerate);
use Storable qw(freeze thaw);
use PDL;
sub test_symbol_compose
{
my $data = mx->symbol->Variable('data');
my $net1 = mx->symbol->FullyConnected(data=>$data, name=>'fc1', num_hidden=>10);
$net1 = mx->symbol->FullyConnected(data=>$net1, name=>'fc2', num_hidden=>100);
is_deeply($net1->list_arguments(), ['data',
'fc1_weight', 'fc1_bias',
view release on metacpan - search on metacpan
( run in 1.623 second using v1.00-cache-2.02-grep-82fe00e-cpan-585fae043c8 )