apache · sergeykolychev · Aug 8, 2018 · Jul 20, 2018 · Aug 8, 2018
@@ -154,6 +154,17 @@ Select API:&nbsp;
 
 <hr>
 
+## Perl Tutorials
+
+* Getting Started
+    * [Machine learning in Perl](http://blogs.perl.org/users/sergey_kolychev/2017/02/machine-learning-in-perl.html)
+    * [Calculator and Robo-Shakespeare](http://blogs.perl.org/users/sergey_kolychev/2017/04/machine-learning-in-perl-part2-a-calculator-handwritten-digits-and-roboshakespeare.html)
+* Gluon
+    * [DCGAN](http://blogs.perl.org/users/sergey_kolychev/2017/10/machine-learning-in-perl-part3-deep-convolutional-generative-adversarial-network.html)
+    * [Image classification and Style transfer](http://blogs.perl.org/users/sergey_kolychev/2018/07/machine-learning-in-perl-kyuubi-goes-to-a-modelzoo-during-the-starry-night.html)
+
+<hr>
+
 ## Contributing Tutorials
 
 We really appreciate contributions, and tutorials are a great way to share your knowledge and help the community. After you have followed [these steps](https://github.com/apache/incubator-mxnet/tree/master/example#contributing), please submit a pull request on Github.

@@ -1,5 +1,11 @@
 Revision history for Perl extension AI::MXNet::Gluon::Contrib
 
+1.32    Sun Jul 15 12:12:15 PDT 2018
+        - Missing POD fixes.
+
+1.31    Sat Jul 14 08:33:21 PDT 2018
+        - Fixed CPAN indexing issue.
+
 1.3     Tue Jul 10 21:19:13 PDT 2018
         - Initial release
 
@@ -38,5 +38,5 @@
       }
    },
    "release_status" : "stable",
-   "version" : "1.3"
+   "version" : "1.32"
 }
@@ -18,4 +18,4 @@ no_index:
     - inc
 requires:
   AI::MXNet: '1.31'
-version: '1.3'
+version: '1.32'
@@ -39,7 +39,7 @@ my %WriteMakefileArgs = (
     "AI::MXNet" => "1.31",
   },
   "TEST_REQUIRES" => {},
-  "VERSION" => "1.3",
+  "VERSION" => "1.32",
   "test" => {
     "TESTS" => "t/*.t"
   }

@@ -1,5 +1,5 @@
 This archive contains the distribution AI-MXNet-Gluon-Contrib,
-version 1.3:
+version 1.32:
 
   Perl interface to MXNet Gluon Contib modules, a collection of supplemental Gluon blocks.
 

@@ -15,10 +15,15 @@
 # specific language governing permissions and limitations
 # under the License.
 
+package AI::MXNet::Gluon::Contrib;
 use strict;
 use warnings;
 use AI::MXNet;
 use AI::MXNet::Gluon::Contrib::NN::BasicLayers;
-our $VERSION = '1.3';
+our $VERSION = '1.32';
+=head1 NAME 
+
+    AI::MXNet::Gluon::Contrib - A collection of supplemental Gluon blocks.
+=cut
 
 1;
@@ -17,6 +17,13 @@
 
 use strict;
 use warnings;
+package AI::MXNet::Gluon::Contrib::NN::BasicLayers;
+
+=head1 NAME 
+
+    AI::MXNet::Gluon::Contrib::NN::BasicLayers - An additional collection of Gluon's building blocks.
+=cut
+
 use AI::MXNet::Function::Parameters;
 package AI::MXNet::Gluon::NN::Concurrent;
 use AI::MXNet::Gluon::Mouse;

@@ -1,5 +1,8 @@
 Revision history for Perl extension AI::MXNet::Gluon::ModelZoo
 
+1.32    Sun Aug  5 14:25:31 PDT 2018
+        - Updated vgg16/19 models
+
 1.3     Tue Jul 10 21:19:13 PDT 2018
         - Initial release
 
@@ -39,5 +39,5 @@
       }
    },
    "release_status" : "stable",
-   "version" : "1.3"
+   "version" : "1.32"
 }
@@ -19,4 +19,4 @@ no_index:
 requires:
   AI::MXNet: '1.31'
   AI::MXNet::Gluon::Contrib: '1.3'
-version: '1.3'
+version: '1.32'
@@ -40,7 +40,7 @@ my %WriteMakefileArgs = (
     "AI::MXNet::Gluon::Contrib" => "1.3"
   },
   "TEST_REQUIRES" => {},
-  "VERSION" => "1.3",
+  "VERSION" => "1.32",
   "test" => {
     "TESTS" => "t/*.t"
   }

@@ -1,5 +1,5 @@
 This archive contains the distribution AI-MXNet-Gluon-ModelZoo,
-version 1.3:
+version 1.32:
 
   Perl interface to MXNet Gluon ModelZoo, a collection of pretrained machine learning models for computer vision.
 

@@ -26,7 +26,7 @@ use AI::MXNet::Gluon::ModelZoo::Vision;
 use Exporter;
 use base qw(Exporter);
 @AI::MXNet::Gluon::ModelZoo::EXPORT_OK = qw(get_model);
-our $VERSION = '1.3';
+our $VERSION = '1.32';
 
 =head1 NAME
 

@@ -60,10 +60,10 @@ my %_model_sha1 = map { $_->[1] => $_->[0] } (
     ['ee79a8098a91fbe05b7a973fed2017a6117723a8', 'vgg11_bn'],
     ['6bc5de58a05a5e2e7f493e2d75a580d83efde38c', 'vgg13'],
     ['7d97a06c3c7a1aecc88b6e7385c2b373a249e95e', 'vgg13_bn'],
-    ['649467530119c0f78c4859999e264e7bf14471a9', 'vgg16'],
-    ['6b9dbe6194e5bfed30fd7a7c9a71f7e5a276cb14', 'vgg16_bn'],
-    ['f713436691eee9a20d70a145ce0d53ed24bf7399', 'vgg19'],
-    ['9730961c9cea43fd7eeefb00d792e386c45847d6', 'vgg19_bn']
+    ['e660d4569ccb679ec68f1fd3cce07a387252a90a', 'vgg16'],
+    ['7f01cf050d357127a73826045c245041b0df7363', 'vgg16_bn'],
+    ['ad2f660d101905472b83590b59708b71ea22b2e5', 'vgg19'],
+    ['f360b758e856f1074a85abd5fd873ed1d98297c3', 'vgg19_bn']
 );
 
 my $apache_repo_url = 'http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/';

@@ -1,4 +1,10 @@
 Revision history for Perl extension AI::MXNet
+
+1.32    Sun Aug  5 14:25:31 PDT 2018
+        - Several new metric classes
+        - Expanded documentation
+        - Bugfixes.
+
 1.31    Tue Jul 10 21:19:13 PDT 2018
         - Memory leak fix for Gluon API
         - Added summary function for Gluon models

@@ -30,7 +30,7 @@
       },
       "runtime" : {
          "requires" : {
-            "AI::MXNetCAPI" : "1.3",
+            "AI::MXNetCAPI" : "1.32",
             "AI::NNVMCAPI" : "1.3",
             "Function::Parameters" : "1.0705",
             "Hash::Ordered" : "0.012",
@@ -45,5 +45,5 @@
       }
    },
    "release_status" : "stable",
-   "version" : "1.31"
+   "version" : "1.32"
 }
@@ -17,12 +17,12 @@ no_index:
     - t
     - inc
 requires:
-  AI::MXNetCAPI: '1.3'
+  AI::MXNetCAPI: '1.32'
   AI::NNVMCAPI: '1.3'
   Function::Parameters: '1.0705'
   Hash::Ordered: '0.012'
   GraphViz: '2.14'
   Mouse: v2.1.0
   PDL: '2.007'
   PDL::CCS: '1.23.4'
-version: '1.31'
+version: '1.32'
@@ -46,7 +46,7 @@ my %WriteMakefileArgs = (
     "GraphViz" => "2.14"
   },
   "TEST_REQUIRES" => {},
-  "VERSION" => "1.31",
+  "VERSION" => "1.32",
   "test" => {
     "TESTS" => "t/*.t"
   }

@@ -1,5 +1,5 @@
 This archive contains the distribution AI-MXNet,
-version 1.31:
+version 1.32:
 
   Perl interface to MXNet machine learning library
 

@@ -51,7 +51,7 @@ use AI::MXNet::Gluon;
 use AI::MXNet::NDArray::Sparse;
 use AI::MXNet::Symbol::Sparse;
 use AI::MXNet::Engine;
-our $VERSION = '1.31';
+our $VERSION = '1.32';
 
 sub import
 {
@@ -187,9 +187,104 @@ AI::MXNet - Perl interface to MXNet machine learning library
     my $res = $mod->score($val_dataiter, mx->metric->create('acc'));
     ok($res->{accuracy} > 0.8);
 
+    ## Gluon MNIST example
+
+    my $net = nn->Sequential();
+    $net->name_scope(sub {
+        $net->add(nn->Dense(128, activation=>'relu'));
+        $net->add(nn->Dense(64, activation=>'relu'));
+        $net->add(nn->Dense(10));
+    });
+    $net->hybridize;
+
+    # data
+    sub transformer
+    {
+        my ($data, $label) = @_;
+        $data = $data->reshape([-1])->astype('float32')/255;
+        return ($data, $label);
+    }
+    my $train_data = gluon->data->DataLoader(
+        gluon->data->vision->MNIST('./data', train=>1, transform => \&transformer),
+        batch_size=>$batch_size, shuffle=>1, last_batch=>'discard'
+    );
+
+    ## training
+    sub train
+    {
+        my ($epochs, $ctx) = @_;
+        # Collect all parameters from net and its children, then initialize them.
+        $net->initialize(mx->init->Xavier(magnitude=>2.24), ctx=>$ctx);
+        # Trainer is for updating parameters with gradient.
+        my $trainer = gluon->Trainer($net->collect_params(), 'sgd', { learning_rate => $lr, momentum => $momentum });
+        my $metric = mx->metric->Accuracy();
+        my $loss = gluon->loss->SoftmaxCrossEntropyLoss();
+
+        for my $epoch (0..$epochs-1)
+        {
+            # reset data iterator and metric at begining of epoch.
+            $metric->reset();
+            enumerate(sub {
+                my ($i, $d) = @_;
+                my ($data, $label) = @$d;
+                $data = $data->as_in_context($ctx);
+                $label = $label->as_in_context($ctx);
+                # Start recording computation graph with record() section.
+                # Recorded graphs can then be differentiated with backward.
+                my $output;
+                autograd->record(sub {
+                    $output = $net->($data);
+                    my $L = $loss->($output, $label);
+                    $L->backward;
+                });
+                # take a gradient step with batch_size equal to data.shape[0]
+                $trainer->step($data->shape->[0]);
+                # update metric at last.
+                $metric->update([$label], [$output]);
+
+                if($i % $log_interval == 0 and $i > 0)
+                {
+                    my ($name, $acc) = $metric->get();
+                    print "[Epoch $epoch Batch $i] Training: $name=$acc\n";
+                }
+            }, \@{ $train_data });
+
+            my ($name, $acc) = $metric->get();
+            print "[Epoch $epoch] Training: $name=$acc\n";
+
+            my ($val_name, $val_acc) = test($ctx);
+            print "[Epoch $epoch] Validation: $val_name=$val_acc\n"
+        }
+        $net->save_parameters('mnist.params');
+    }
+
+    train($epochs, $cuda ? mx->gpu(0) : mx->cpu);
+
 =head1 DESCRIPTION
 
     Perl interface to MXNet machine learning library.
+    MXNet supports the Perl programming language. 
+    The MXNet Perl package brings flexible and efficient GPU computing and 
+    state-of-art deep learning to Perl.
+    It enables you to write seamless tensor/matrix computation with multiple GPUs in Perl.
+    It also lets you construct and customize the state-of-art deep learning models in Perl,
+    and apply them to tasks, such as image classification and data science challenges.
+
+    One important thing to internalize is that Perl interface is written to be as close as possible to the Python’s API,
+    so most, if not all of Python’s documentation and examples should just work in Perl after making few changes 
+    in order to make the code a bit more Perlish. In nutshell just add $ sigils and replace . = \n with -> => ; 
+    and in 99% of cases that’s all that is needed there.
+    In addition please refer to very detailed MXNet Python API Documentation.
+
+    AI::MXNet supports new imperative PyTorch like Gluon MXNet interface.
+    Please get acquainted with this new interface at Deep Learning - The Straight Dope.
+
+    For specific Perl Gluon usage please refer to Perl examples and tests directories on github,
+    but be assured that the Python and Perl usage are extremely close in order to make the use 
+    of the Python Gluon docs and examples as easy as possible.
+
+    AI::MXNet is seamlessly glued with PDL, the C++ level state can be easily initialized from PDL
+    and the results can be transferred to PDL objects in order to allow you to use all the glory and power of the PDL!
 
 =head1 BUGS AND INCOMPATIBILITIES