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class="row"> <div aria-label="main navigation" class="sphinxsidebar leftsidebar" role="navigation"> <div class="sphinxsidebarwrapper"> <ul> <li class="toctree-l1"><a class="reference internal" href="../python/index.html">Python Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../r/index.html">R Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../julia/index.html">Julia Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../c++/index.html">C++ Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../scala/index.html">Scala Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="index.html">Perl Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../how_to/index.html">HowTo Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../architecture/index.html">System Documents</a></li> <li class="toctree-l1"><a class="reference internal" href="../../tutorials/index.html">Tutorials</a></li> </ul> </div> </div> <div class="content"> <div class="page-tracker"></div> <div class="section" id="mxnet-perl-symbolic-api"> <h1>MXNet Perl Symbolic API<a class="headerlink" href="#mxnet-perl-symbolic-api" title="Permalink to this headline">¶</a></h1> Topics: <ul class="simple"> <li><a class="reference external" href="#how-to-compose-symbols">How to Compose Symbols</a> introduces operator overloading of symbols.</li> <li><a class="reference external" href="#symbol-attributes">Symbol Attributes</a> describes how to attach attributes to symbols.</li> <li><a class="reference external" href="#serialization">Serialization</a> explains how to save and load symbols.</li> <li><a class="reference external" href="#executing-symbols">Executing Symbols</a> explains how to evaluate the symbols with data.</li> <li><a class="reference external" href="#multiple-outputs">Multiple Outputs</a> explains how to configure multiple outputs.</li> </ul> <div class="section" id="how-to-compose-symbols"> <h2>How to Compose Symbols<a class="headerlink" href="#how-to-compose-symbols" title="Permalink to this headline">¶</a></h2> The symbolic API provides a way to configure computation graphs. You can configure the graphs either at the level of neural network layer operations or as fine-grained operations. The following example configures a two-layer neural network. <div class="highlight-perl"><div class="highlight"><pre>pdl> use AI::MXNet qw(mx) pdl> $data = mx->symbold->Variable("data") pdl> $fc1 = mx->symbol->FullyConnected(data => $data, name => "fc1", num_hidden" -> 128) pdl> $act1 = mx->symbol->Activation(data => $fc1, name => "relu1", act_type => "relu") pdl> $fc2 = mx->symbol->FullyConnected(data => $act1, name => "fc2", num_hidden => 64) pdl> $net = mx->symbol->SoftmaxOutput(data => $fc2, name => "out") </pre></div> </div> The basic arithmetic operators (plus, minus, div, multiplication) are overloaded for element-wise operations of symbols. The following example creates a computation graph that adds two inputs together. <div class="highlight-perl"><div class="highlight"><pre>pdl> use AI::MXNet qw(mx) pdl> $a = mx->symbol->Variable("a") pdl> $b = mx->symbol->Variable("b") pdl> $c = $a + $b </pre></div> </div> </div> <div class="section" id="symbol-attributes"> <h2>Symbol Attributes<a class="headerlink" href="#symbol-attributes" title="Permalink to this headline">¶</a></h2> You can add an attribute to a symbol by providing an attribute hash when you create a symbol. <div class="highlight-perl"><div class="highlight"><pre>$data = mx->symbol->Variable("data", attr => { mood => "angry" }) $op = mx->symbol->Convolution(data => $data, kernel => [1, 1], num_filter => 1, attr => { mood => "so so" }) </pre></div> </div> For proper communication with the C++ backend, both the key and values of the attribute dictionary should be strings. To retrieve the attributes, use <code class="docutils literal">->attr($key)</code>: <div class="highlight-default"><div class="highlight"><pre> $data->attr("mood") </pre></div> </div> To attach attributes, you can use <code class="docutils literal">AI::MXNet::AttrScope</code>. <code class="docutils literal">AI::MXNet::AttrScopeAttrScope</code> automatically adds the specified attributes to all of the symbols created within that scope. The user can also inherit this object to change naming behavior. For example: <div class="highlight-perl"><div class="highlight"><pre>use AI::MXNet qw(mx); use Test::More tests => 3; my ($data, $gdata); { local($mx::AttrScope) = mx->AttrScope(group=>4, data=>'great'); $data = mx->sym->Variable("data", attr => { dtype => "data", group => "1" }); $gdata = mx->sym->Variable("data2"); } ok($gdata->attr("group") == 4); ok($data->attr("group") == 1); my $exceedScopeData = mx->sym->Variable("data3"); ok((not defined $exceedScopeData->attr("group")), "No group attr in global attr scope"); </pre></div> </div> </div> <div class="section" id="serialization"> <h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to this headline">¶</a></h2> There are two ways to save and load the symbols. You can use the <code class="docutils literal">mx->symbol->save</code> and <code class="docutils literal">mxnet->symbol->load</code> functions to serialize the <code class="docutils literal">AI::MXNet::Symbol</code> objects. The advantage of using <code class="docutils literal">save</code> and <code class="docutils literal">load</code> functions is that it is language agnostic and cloud friendly. The symbol is saved in JSON format. You can also get a JSON string directly using <code class="docutils literal">$symbol->tojson</code>. The following example shows how to save a symbol to an S3 bucket, load it back, and compare two symbols using a JSON string. <div class="highlight-perl"><div class="highlight"><pre>pdl> use AI::MXNet qw(mx) pdl> $a = mx->sym->Variable("a") pdl> $b = mx->sym->Variable("b") pdl> $c = $a + $b pdl> $c->save("s3://my-bucket/symbol-c.json") pdl> $c2 = $c->load("s3://my-bucket/symbol-c.json") pdl> ok($c->tojson eq $c2->tojson) ok 1 </pre></div> </div> </div> <div class="section" id="executing-symbols"> <h2>Executing Symbols<a class="headerlink" href="#executing-symbols" title="Permalink to this headline">¶</a></h2> After you have assembled a set of symbols into a computation graph, the MXNet engine can evaluate them. If you are training a neural network, this is typically handled by the high-level <a class="reference internal" href="module.html">AI::MXNet::Module package</a> and the [<code class="docutils literal">fit()</code>] function. For neural networks used in “feed-forward”, “prediction”, or “inference” mode (all terms for the same thing: running a trained network), the input arguments are the input data, and the weights of the neural network that were learned during training. To manually execute a set of symbols, you need to create an [<code class="docutils literal">AI::MXNet::Executor</code>] object, which is typically constructed by calling the [<code class="docutils literal">simple_bind(<parameters>)</code>] method on a AI::MXNet::Symbol. </div> <div class="section" id="multiple-outputs"> <h2>Multiple Outputs<a class="headerlink" href="#multiple-outputs" title="Permalink to this headline">¶</a></h2> To group the symbols together, use the <a class="reference external" href="#mxnet.symbol.Group">AI::MXNet::Symbol->Group</a> function. <div class="highlight-perl"><div class="highlight"><pre>pdl> use AI::MXNet qw(mx) pdl> use Data::Dumper pdl> $data = mx->sym->Variable("data") pdl> $fc1 = mx->sym->FullyConnected($data, name => "fc1", num_hidden => 128) pdl> $act1 = mx->sym->Activation($fc1, name => "relu1", act_type => "relu") pdl> $fc2 = mx->sym->FullyConnected($act1, name => "fc2", num_hidden => 64) pdl> $net = mx->sym->SoftmaxOutput($fc2, name => "softmax") pdl> $group = mx->sym->Group([$fc1, $net]) pdl> print Dumper($group->list_outputs()) $VAR1 = [ 'fc1_output', 'softmax_output' ]; </pre></div> </div> After you get the <code class="docutils literal">Group</code>, you can bind on <code class="docutils literal">group</code> instead. The resulting executor will have two outputs, one for fc1_output and one for softmax_output. </div> </div> </div> </div> <div aria-label="main navigation" class="sphinxsidebar rightsidebar" role="navigation"> <div class="sphinxsidebarwrapper"> <h3><a href="../../index.html">Table Of Contents</a></h3> <ul> <li><a class="reference internal" href="#">MXNet Perl Symbolic API</a><ul> <li><a class="reference internal" href="#how-to-compose-symbols">How to Compose Symbols</a></li> <li><a class="reference internal" href="#symbol-attributes">Symbol Attributes</a></li> <li><a class="reference internal" href="#serialization">Serialization</a></li> <li><a class="reference internal" href="#executing-symbols">Executing Symbols</a></li> <li><a class="reference internal" href="#multiple-outputs">Multiple Outputs</a></li> </ul> </li> </ul> </div> </div> </div><div class="footer"> <div class="section-disclaimer"> <div class="container"> <div> <img height="60" src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/apache_incubator_logo.png"/> Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. 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