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| <div class="section" id="metric"> |
| <h1>度量(Metric)<a class="headerlink" href="#metric" title="Permalink to this headline">¶</a></h1> |
| <p>该模块包含一组用于评估模型性能的度量类。 特定的度量类可以由C++的实现转换或直接使用Python实现。</p> |
| <p>示例用法：</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</span> |
| <span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">metric</span> |
| |
| <span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span> |
| <span class="n">x</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># randomly genearte the prediction activation</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">SoftMax</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="c1"># normalize the prediction into probabilities</span> |
| <span class="n">y</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">int</span><span class="p">))</span> <span class="c1"># set the truth</span> |
| |
| <span class="n">f</span> <span class="o">=</span> <span class="n">metric</span><span class="o">.</span><span class="n">Accuracy</span><span class="p">()</span> |
| <span class="n">acc</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">evaluate</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="c1"># averaged accuracy over all 3 samples in x</span> |
| </pre></div> |
| </div> |
| <hr class="docutils" /> |
| <div class="section" id="class-singa-metric-metric"> |
| <h2>class singa.metric.Metric<a class="headerlink" href="#class-singa-metric-metric" title="Permalink to this headline">¶</a></h2> |
| <p>基类：<code class="docutils literal"><span class="pre">object</span></code></p> |
| <p>度量类的基类</p> |
| <p>封装C++度量类的子类可以使用继承的前向函数，并评估此基类的函数。 其他子类需要重写这些函数。 用户需要提供预测值和真实值来获取度量值。</p> |
| <hr class="docutils" /> |
| <div class="section" id="forward-x-y"> |
| <h3>forward(x, y)<a class="headerlink" href="#forward-x-y" title="Permalink to this headline">¶</a></h3> |
| <p>为每个样本计算度量值</p> |
| <p><strong>参数：</strong></p> |
| <ul class="simple"> |
| <li><strong>x (Tensor)</strong> – 预测值，每行代表一个样本的预测值</li> |
| <li><strong>y (Tensor)</strong> – 真实值，每行代表一个样本的真实值</li> |
| </ul> |
| <p><strong>返回值：</strong> 浮点数组成的tensor，每个样本对应一个浮点数输出</p> |
| </div> |
| <hr class="docutils" /> |
| <div class="section" id="evaluate-x-y"> |
| <h3>evaluate(x, y)<a class="headerlink" href="#evaluate-x-y" title="Permalink to this headline">¶</a></h3> |
| <p>计算样本的平均度量值</p> |
| <p><strong>参数：</strong></p> |
| <ul class="simple"> |
| <li><strong>x (Tensor)</strong> – 预测值，每列代表一个样本的预测值</li> |
| <li><strong>y (Tensor)</strong> – 真实值，每列代表一个样本的真实值</li> |
| </ul> |
| <p><strong>返回值：</strong> 浮点数组成的tensor，每个样本对应一个浮点数输出</p> |
| </div> |
| </div> |
| <hr class="docutils" /> |
| <div class="section" id="class-singa-metric-accuracy"> |
| <h2>class singa.metric.Accuracy<a class="headerlink" href="#class-singa-metric-accuracy" title="Permalink to this headline">¶</a></h2> |
| <p>基类：<code class="docutils literal"><span class="pre">singa.metric.Metric</span></code></p> |
| <p>对于单标签预测任务，计算top-1精确度。它调用C++函数实现计算。</p> |
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