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<li class="toctree-l3 current"><a class="current reference internal" href="#">ComplexAbs</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="#mindradar.ComplexAbs"><code class="docutils literal notranslate"><span class="pre">mindradar.ComplexAbs</span></code></a></li>
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<a href="../../_sources/functionlib/custom_op/complex_abs.rst.txt" rel="nofollow"> 查看页面源码</a>
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<section id="complexabs">
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<h1>ComplexAbs<a class="headerlink" href="#complexabs" title="Link to this heading"></a></h1>
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<dl class="py class">
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<dt class="sig sig-object py" id="mindradar.ComplexAbs">
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<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mindradar.</span></span><span class="sig-name descname"><span class="pre">ComplexAbs</span></span><a class="headerlink" href="#mindradar.ComplexAbs" title="Link to this definition"></a></dt>
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<dd><p>复数tensor逐元素求模。 计算表达式为:</p>
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<div class="math notranslate nohighlight">
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\[out_i = \sqrt{a_i^2 + b_i^2},\]</div>
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<p>其中 <span class="math notranslate nohighlight">\(a\)</span> 和 <span class="math notranslate nohighlight">\(b\)</span> 分别是复数的实部和虚部。</p>
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<dl class="simple">
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<dt>输入：</dt><dd><ul class="simple">
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<li><p><strong>input</strong> : 输入的复数tensor</p></li>
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</ul>
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</dd>
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<dt>输出：</dt><dd><p>tensor， 复数tensor的模</p>
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</dd>
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<dt>支持平台：</dt><dd><p><code class="docutils literal notranslate"><span class="pre">CPU</span></code></p>
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</dd>
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</dl>
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<div class="admonition note">
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<p class="admonition-title">备注</p>
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<p>输入数据类型为 <code class="docutils literal notranslate"><span class="pre">complex64</span></code> 时，输出数据类型为 <code class="docutils literal notranslate"><span class="pre">float32</span></code>。
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数据类型为 <code class="docutils literal notranslate"><span class="pre">complex128</span></code> 时，输出数据类型为 <code class="docutils literal notranslate"><span class="pre">float64</span></code>。其他数据类型不支持。</p>
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<p>示例代码：</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos">1</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindspore</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">ms</span>
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<span class="linenos">2</span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
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<span class="linenos">3</span><span class="kn">import</span><span class="w"> </span><span class="nn">mindradar</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mr</span>
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<span class="linenos">4</span>
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<span class="linenos">5</span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mi">3</span><span class="n">j</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">7</span><span class="p">)</span> <span class="o">/</span> <span class="mi">7</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span>
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<span class="linenos">6</span><span class="n">x_tensor</span> <span class="o">=</span> <span class="n">ms</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<span class="hll"><span class="linenos">7</span><span class="n">net</span> <span class="o">=</span> <span class="n">mr</span><span class="o">.</span><span class="n">ComplexAbs</span><span class="p">()</span>
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</span><span class="linenos">8</span><span class="n">out</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">x_tensor</span><span class="p">)</span>
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<span class="linenos">9</span><span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
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</pre></div>
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