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 <div class="section" id="ndarray-api">
 <span id="ndarray-api"></span><h1>NDArray API<a class="headerlink" href="#ndarray-api" title="Permalink to this headline">¶</a></h1>
 <div class="section" id="overview">
 <span id="overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
 <p>A <code class="docutils literal"><span class="pre">AI::MXNet::NDArray</span></code> is a multidimensional container of items of the same type and
 size. Various methods for data manipulation and computation are provided.</p>
 <div class="highlight-perl"><div class="highlight"><pre><span></span><span class="n">pdl</span><span class="o">></span> <span class="nv">$x</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">nd</span><span class="o">-></span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
 <span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$x</span><span class="o">-></span><span class="nn">aspdl</span><span class="o">-></span><span class="n">shape</span>
 <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
 <span class="n">pdl</span><span class="o">></span> <span class="nv">$y</span> <span class="o">=</span> <span class="nv">$x</span> <span class="o">+</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">nd</span><span class="o">-></span><span class="n">ones</span><span class="p">(</span><span class="nv">$x</span><span class="o">-></span><span class="n">shape</span><span class="p">)</span><span class="o">*</span><span class="mi">3</span>
 <span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$y</span><span class="o">-></span><span class="n">aspdl</span>
 <span class="p">[</span>
  <span class="p">[</span><span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span><span class="p">]</span>
  <span class="p">[</span><span class="mi">7</span> <span class="mi">8</span> <span class="mi">9</span><span class="p">]</span>
 <span class="p">]</span>
 <span class="n">pdl</span><span class="o">></span> <span class="nv">$z</span> <span class="o">=</span> <span class="nv">$y</span><span class="o">-></span><span class="n">as_in_context</span><span class="p">(</span><span class="nn">mx</span><span class="o">-></span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
 <span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$z</span><span class="p">,</span><span class="s">"\n"</span>
 <span class="o"><</span><span class="nn">AI::MXNet::</span><span class="n">NDArray</span> <span class="mi">2</span><span class="n">x3</span> <span class="nv">@gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span>
 </pre></div>
 </div>
 <p>A detailed tutorial is available at
 <a class="reference external" href="http://mxnet.io/tutorials/basic/ndarray.html">http://mxnet.io/tutorials/basic/ndarray.html</a>.</p>
 <p>Note: AI::MXNet::NDarray is similar to numpy.ndarray in some aspects. But the difference is not negligible. For example</p>
 <ul class="simple">
 <li>AI::MXNet::NDArray->T does real data transpose to return new a copied array, instead
 of returning a view of the input array.</li>
 <li>AI::MXNet::NDArray->dot performs dot between the last axis of the first input array
 and the first axis of the second input, while numpy.dot uses the second
 last axis of the input array.</li>
 </ul>
 <p>In additional, NDArray supports GPU computation and various neural
 network layers.</p>
 <p>AI::MXNet::NDarray also provides almost same routines as AI::MXNet::symbol. Most
 routines between these two packages share the same C++ operator source
 codes. But AI::MXNet::NDarray differs from AI::MXNet::Symbol in several aspects:</p>
 <ul class="simple">
 <li>AI::MXNet::NDArray adopts imperative programming, namely sentences are executed
 step-by-step so that the results can be obtained immediately.</li>
 </ul>
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	<div class="section" id="ndarray-api">
	<span id="ndarray-api"></span><h1>NDArray API<a class="headerlink" href="#ndarray-api" title="Permalink to this headline">¶</a></h1>
	<div class="section" id="overview">
	<span id="overview"></span><h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
	<p>A <code class="docutils literal"><span class="pre">AI::MXNet::NDArray</span></code> is a multidimensional container of items of the same type and
	size. Various methods for data manipulation and computation are provided.</p>
	<div class="highlight-perl"><div class="highlight"><pre><span></span><span class="n">pdl</span><span class="o">></span> <span class="nv">$x</span> <span class="o">=</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">nd</span><span class="o">-></span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
	<span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$x</span><span class="o">-></span><span class="nn">aspdl</span><span class="o">-></span><span class="n">shape</span>
	<span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
	<span class="n">pdl</span><span class="o">></span> <span class="nv">$y</span> <span class="o">=</span> <span class="nv">$x</span> <span class="o">+</span> <span class="nn">mx</span><span class="o">-></span><span class="nn">nd</span><span class="o">-></span><span class="n">ones</span><span class="p">(</span><span class="nv">$x</span><span class="o">-></span><span class="n">shape</span><span class="p">)</span><span class="o">*</span><span class="mi">3</span>
	<span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$y</span><span class="o">-></span><span class="n">aspdl</span>
	<span class="p">[</span>
	<span class="p">[</span><span class="mi">4</span> <span class="mi">5</span> <span class="mi">6</span><span class="p">]</span>
	<span class="p">[</span><span class="mi">7</span> <span class="mi">8</span> <span class="mi">9</span><span class="p">]</span>
	<span class="p">]</span>
	<span class="n">pdl</span><span class="o">></span> <span class="nv">$z</span> <span class="o">=</span> <span class="nv">$y</span><span class="o">-></span><span class="n">as_in_context</span><span class="p">(</span><span class="nn">mx</span><span class="o">-></span><span class="n">gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
	<span class="n">pdl</span><span class="o">></span> <span class="k">print</span> <span class="nv">$z</span><span class="p">,</span><span class="s">"\n"</span>
	<span class="o"><</span><span class="nn">AI::MXNet::</span><span class="n">NDArray</span> <span class="mi">2</span><span class="n">x3</span> <span class="nv">@gpu</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">></span>
	</pre></div>
	</div>
	<p>A detailed tutorial is available at
	<a class="reference external" href="http://mxnet.io/tutorials/basic/ndarray.html">http://mxnet.io/tutorials/basic/ndarray.html</a>.</p>
	<p>Note: AI::MXNet::NDarray is similar to numpy.ndarray in some aspects. But the difference is not negligible. For example</p>
	<ul class="simple">
	<li>AI::MXNet::NDArray->T does real data transpose to return new a copied array, instead
	of returning a view of the input array.</li>
	<li>AI::MXNet::NDArray->dot performs dot between the last axis of the first input array
	and the first axis of the second input, while numpy.dot uses the second
	last axis of the input array.</li>
	</ul>
	<p>In additional, NDArray supports GPU computation and various neural
	network layers.</p>
	<p>AI::MXNet::NDarray also provides almost same routines as AI::MXNet::symbol. Most
	routines between these two packages share the same C++ operator source
	codes. But AI::MXNet::NDarray differs from AI::MXNet::Symbol in several aspects:</p>
	<ul class="simple">
	<li>AI::MXNet::NDArray adopts imperative programming, namely sentences are executed
	step-by-step so that the results can be obtained immediately.</li>
	</ul>
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	<ul>
	<li><a class="reference internal" href="#">NDArray API</a><ul>
	<li><a class="reference internal" href="#overview">Overview</a></li>
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