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</div> <div id="search-input-wrap"> <form action="../../search.html" autocomplete="off" class="" method="get" role="search"> <div class="form-group inner-addon left-addon"> <i class="glyphicon glyphicon-search"></i> <input class="form-control" name="q" placeholder="Search" type="text"/> </div> <input name="check_keywords" type="hidden" value="yes"> <input name="area" type="hidden" value="default"/> </input></form> <div id="search-preview"></div> </div> <div id="searchIcon"> <span aria-hidden="true" class="glyphicon glyphicon-search"></span> </div>            </div> </div> </div> <script type="text/javascript"> $('body').css('background', 'white'); </script> <div class="container"> <div 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="../../api/python/index.html">Python Documents</a></li> <li class="toctree-l1"><a class="reference 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id="ndarray-scientific-computing-on-cpu-and-gpu"></span><h1>NDArray - Scientific computing on CPU and GPU<a class="headerlink" href="#ndarray-scientific-computing-on-cpu-and-gpu" title="Permalink to this headline">¶</a></h1> <p>NDArray is a tensor data structure similar to numpy’s multi-dimensional array. In addition, it supports asynchronous computation on CPU and GPU.</p> <p>First, let’s import MXNet:</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span> <span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span> </pre></div> </div> <div class="section" id="creating-ndarray"> <span id="creating-ndarray"></span><h2>Creating NDArray<a class="headerlink" href="#creating-ndarray" title="Permalink to this headline">¶</a></h2> <p>There are many ways to create NDArray.</p> <p>Construct from (nested) list:</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">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="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> </pre></div> </div> <p>Construct from numpy array:</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x_numpy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</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="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">x_numpy</span><span class="p">)</span> <span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> </pre></div> </div> <p>Array construction routines:</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># create an 2x3 array of ones</span> <span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">ones</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="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># create an 2x3 array of zeros</span> <span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</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="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># create an 1d-array of 0 to 5 and reshape to 2x3</span> <span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</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="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> </pre></div> </div> <p>You can convert an NDArray to numpy array to retrieve its data with <code class="docutils literal"><span class="pre">.asnumpy()</span></code>:</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> <span class="k">print</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> </pre></div> </div> </div> <div class="section" id="basic-attributes"> <span id="basic-attributes"></span><h2>Basic attributes<a class="headerlink" href="#basic-attributes" title="Permalink to this headline">¶</a></h2> <p>NDArray has some basic attributes that you often want to query:</p> <p><strong>NDArray.shape</strong>: The dimensions of the array. It is a tuple of integers indicating the length of the array along each axis. For a matrix with <code class="docutils literal"><span class="pre">n</span></code> rows and <code class="docutils literal"><span class="pre">m</span></code> columns, its <code class="docutils literal"><span class="pre">shape</span></code> will be <code class="docutils literal"><span class="pre">(n,</span> <span class="pre">m)</span></code>.</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="s1">'x.shape:'</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> </pre></div> </div> <p><strong>NDArray.dtype</strong>: A <code class="docutils literal"><span class="pre">numpy</span></code> <em>type</em> object describing the type of array elements.</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="s1">'x.dtype:'</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> </pre></div> </div> <p><strong>NDArray.size</strong>: the total number of components in the array - equals to the product of the components of its <code class="docutils literal"><span class="pre">shape</span></code></p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="s1">'x.size:'</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> </pre></div> </div> <p><strong>NDArray.context</strong>: The device on which this array is stored, e.g. <code class="docutils literal"><span class="pre">mx.cpu()</span></code> or <code class="docutils literal"><span class="pre">mx.gpu(1)</span></code>.</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="s1">'x.context:'</span><span class="p">,</span> <span class="n">x</span><span class="o">.</span><span class="n">context</span><span class="p">)</span> </pre></div> </div> </div> <div class="section" id="ndarray-operations"> <span id="ndarray-operations"></span><h2>NDArray Operations<a class="headerlink" href="#ndarray-operations" title="Permalink to this headline">¶</a></h2> <p>NDArray supports a wide range of operations. Simple operations can be called with python syntax:</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">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="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span> <span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]])</span> <span class="k">print</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span> </pre></div> </div> <p>You can also call operators from the <code class="docutils literal"><span class="pre">mxnet.ndarray</span></code> (or <code class="docutils literal"><span class="pre">mx.nd</span></code> for short) name space:</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="k">print</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> </pre></div> </div> <p>You can also pass additional flags to operators:</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="k">print</span><span class="p">(</span><span class="s1">'axis=0:'</span><span class="p">,</span> <span class="n">z</span><span class="p">)</span> <span class="n">z</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="k">print</span><span class="p">(</span><span class="s1">'axis=1:'</span><span class="p">,</span> <span class="n">z</span><span class="p">)</span> </pre></div> </div> </div> <div class="section" id="using-gpu"> <span id="using-gpu"></span><h2>Using GPU<a class="headerlink" href="#using-gpu" title="Permalink to this headline">¶</a></h2> <p>Each NDArray lives on a <code class="docutils literal"><span class="pre">Context</span></code>. MXNet supports <code class="docutils literal"><span class="pre">mx.cpu()</span></code> for CPU and <code class="docutils literal"><span class="pre">mx.gpu(0)</span></code>, <code class="docutils literal"><span class="pre">mx.gpu(1)</span></code>, etc for GPU. You can specify context when creating NDArray:</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># creates on CPU (the default).</span> <span class="c1"># Replace mx.cpu() with mx.gpu(0) if you have a GPU.</span> <span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span> <span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> </pre></div> </div> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">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="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">]],</span> <span class="n">ctx</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span> <span class="k">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> </pre></div> </div> <p>You can copy arrays between devices with <code class="docutils literal"><span class="pre">.copyto()</span></code>:</p> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Copy x to cpu. Replace with mx.gpu(0) if you have GPU.</span> <span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span> <span class="k">print</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> </pre></div> </div> <div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Copy x to another NDArray, possibly on another Context.</span> <span class="n">y</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="n">x</span><span class="o">.</span><span class="n">copyto</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="k">print</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> </pre></div> </div> <p>See the <a class="reference internal" href="../basic/ndarray.html"><span class="doc">Advanced NDArray tutorial</span></a> for a more detailed introduction to NDArray API.</p> <div class="btn-group" role="group"> <div class="download-btn"><a download="ndarray.ipynb" href="ndarray.ipynb"><span class="glyphicon glyphicon-download-alt"></span> ndarray.ipynb</a></div></div></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="#">NDArray - Scientific computing on CPU and GPU</a><ul> <li><a class="reference internal" href="#creating-ndarray">Creating NDArray</a></li> <li><a class="reference internal" href="#basic-attributes">Basic attributes</a></li> <li><a class="reference internal" href="#ndarray-operations">NDArray Operations</a></li> <li><a class="reference internal" href="#using-gpu">Using GPU</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"/> <p> Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), <strong>sponsored by the <i>Apache Incubator</i></strong>. 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. </p> <p> "Copyright © 2017-2018, The Apache Software Foundation Apache MXNet, MXNet, Apache, the Apache feather, and the Apache MXNet project logo are either registered trademarks or trademarks of the Apache Software Foundation." </p> </div> </div> </div> </div>  </div> <script crossorigin="anonymous" integrity="sha384-0mSbJDEHialfmuBBQP6A4Qrprq5OVfW37PRR3j5ELqxss1yVqOtnepnHVP9aJ7xS" src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js"></script> <script src="../../_static/js/sidebar.js" type="text/javascript"></script> <script src="../../_static/js/search.js" type="text/javascript"></script> <script src="../../_static/js/navbar.js" type="text/javascript"></script> <script src="../../_static/js/clipboard.min.js" type="text/javascript"></script> <script src="../../_static/js/copycode.js" type="text/javascript"></script> <script src="../../_static/js/page.js" type="text/javascript"></script> <script src="../../_static/js/docversion.js" type="text/javascript"></script> <script type="text/javascript"> $('body').ready(function () { $('body').css('visibility', 'visible'); }); </script> </body> </html>

versions/0.11.0/tutorials/gluon/ndarray.html (346 lines of code) (raw):