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   <div class="section" id="loss">
 <h1>损失(Loss)<a class="headerlink" href="#loss" title="Permalink to this headline">¶</a></h1>
 <p>损失模块包括一组训练损失实例。 有些是从C ++实现转换而来的，其余的都是直接使用python Tensor实现的。</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">loss</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">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">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropy</span><span class="p">()</span>
 <span class="n">l</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="bp">True</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"># l is tensor with 3 loss values</span>
 <span class="n">g</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>  <span class="c1"># g is a tensor containing all gradients of x w.r.t l</span>
 </pre></div>
 </div>
 <hr class="docutils" />
 <div class="section" id="class-singa-loss-loss">
 <h2>class singa.loss.Loss<a class="headerlink" href="#class-singa-loss-loss" title="Permalink to this headline">¶</a></h2>
 <p>基类：<code class="docutils literal"><span class="pre">object</span></code></p>
 <p>损失类的基类</p>
 <p>封装C++损失类的子类可以使用此基类继承的forward，backward和evaluate函数。 其他子类需要重写这些函数</p>
 <div class="section" id="backward">
 <h3>backward()<a class="headerlink" href="#backward" title="Permalink to this headline">¶</a></h3>
 <p><strong>返回值：</strong> 与损失相对应的梯度</p>
 </div>
 <hr class="docutils" />
 <div class="section" id="evaluate-flag-x-y">
 <h3>evaluate(flag, x, y)<a class="headerlink" href="#evaluate-flag-x-y" title="Permalink to this headline">¶</a></h3>
 <p><strong>参数：</strong></p>
 <ul class="simple">
 <li><strong>flag (int)</strong> – 必须是kEval</li>
 <li><strong>x (Tensor)</strong> – 预测Tensor</li>
 <li><strong>y (Tensor)</strong> – 真实Tensor</li>
 </ul>
 <p><strong>返回值：</strong> 所有样本的平均损失</p>
 </div>
 <hr class="docutils" />
 <div class="section" id="forward-flag-x-y">
 <h3>forward(flag, x, y)<a class="headerlink" href="#forward-flag-x-y" title="Permalink to this headline">¶</a></h3>
 <p>计算损失值</p>
 <p><strong>参数：</strong></p>
 <ul class="simple">
 <li><strong>flag</strong> – kTrain/kEval或布尔值。如果是kTrain/True，那么在下一次调用forward前会先调用backward计算梯度。</li>
 <li><strong>x (Tensor)</strong> – 预测Tensor</li>
 <li><strong>y (Tensor)</strong> – 真实Tensor, x.shape[0]必须和y.shape[0]相同</li>
 </ul>
 <p><strong>返回值：</strong> tensor，每个样本对应一个浮点型损失值</p>
 </div>
 </div>
 <hr class="docutils" />
 <div class="section" id="class-singa-loss-softmaxcrossentropy">
 <h2>class singa.loss.SoftmaxCrossEntropy<a class="headerlink" href="#class-singa-loss-softmaxcrossentropy" title="Permalink to this headline">¶</a></h2>
 <p>基类：<code class="docutils literal"><span class="pre">singa.loss.Loss</span></code></p>
 <p>此损失函数是SoftMax和交叉熵损失的结合。它通过SoftMax函数转换输入，然后根据真实值计算交叉熵损失。 对于每个样本，真实值可以是一个整数作为标签索引; 或二进制数组，指示标签分布。 因此，真实值可以是1维或2维tensor。 对于一批样品，数据/特征tensor可以是1维（对于单个样品）或2维（对于一组样本）。</p>
 </div>
 <hr class="docutils" />
 <div class="section" id="class-singa-loss-squarederror">
 <h2>class singa.loss.SquaredError<a class="headerlink" href="#class-singa-loss-squarederror" title="Permalink to this headline">¶</a></h2>
 <p>基类：<code class="docutils literal"><span class="pre">singa.loss.Loss</span></code></p>
 <p>此损失用来衡量预测值和真实值之间的平方差。它通过Python Tensor操作实现。</p>
 <hr class="docutils" />
 <div class="section" id="id1">
 <h3>backward()<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
 <p>计算与损失相对应变量的梯度。</p>
 <p><strong>返回值：</strong> x - y</p>
 </div>
 <hr class="docutils" />
 <div class="section" id="id2">
 <h3>evaluate(flag, x, y)<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
 <p>计算平均误差。</p>
 <p><strong>返回值：</strong> 浮点型数</p>
 </div>
 <hr class="docutils" />
 <div class="section" id="id3">
 <h3>forward(flag, x, y)<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
 <p>通过0.5 * ||x-y||^2计算损失。</p>
 <p><strong>参数：</strong></p>
 <ul class="simple">
 <li><strong>flag (int)</strong> – kTrain或kEval；如果是kTrain，那么在下一次调用forward前会先调用backward计算梯度。</li>
 <li><strong>x (Tensor)</strong> – 预测Tensor</li>
 <li><strong>y (Tensor)</strong> – 真实Tensor, 每个样本对应一个整型数, 取值为[0, x.shape[1])。</li>
 </ul>
 <p><strong>返回值：</strong> tensor，每个样本对应一个损失值</p>
 <hr class="docutils" />
 </div>
 </div>
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	<div class="section" id="loss">
	<h1>损失(Loss)<a class="headerlink" href="#loss" title="Permalink to this headline">¶</a></h1>
	<p>损失模块包括一组训练损失实例。有些是从C ++实现转换而来的，其余的都是直接使用python Tensor实现的。</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">loss</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">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">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropy</span><span class="p">()</span>
	<span class="n">l</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="bp">True</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"># l is tensor with 3 loss values</span>
	<span class="n">g</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> <span class="c1"># g is a tensor containing all gradients of x w.r.t l</span>
	</pre></div>
	</div>
	<hr class="docutils" />
	<div class="section" id="class-singa-loss-loss">
	<h2>class singa.loss.Loss<a class="headerlink" href="#class-singa-loss-loss" title="Permalink to this headline">¶</a></h2>
	<p>基类：<code class="docutils literal"><span class="pre">object</span></code></p>
	<p>损失类的基类</p>
	<p>封装C++损失类的子类可以使用此基类继承的forward，backward和evaluate函数。其他子类需要重写这些函数</p>
	<div class="section" id="backward">
	<h3>backward()<a class="headerlink" href="#backward" title="Permalink to this headline">¶</a></h3>
	<p><strong>返回值：</strong> 与损失相对应的梯度</p>
	</div>
	<hr class="docutils" />
	<div class="section" id="evaluate-flag-x-y">
	<h3>evaluate(flag, x, y)<a class="headerlink" href="#evaluate-flag-x-y" title="Permalink to this headline">¶</a></h3>
	<p><strong>参数：</strong></p>
	<ul class="simple">
	<li><strong>flag (int)</strong> – 必须是kEval</li>
	<li><strong>x (Tensor)</strong> – 预测Tensor</li>
	<li><strong>y (Tensor)</strong> – 真实Tensor</li>
	</ul>
	<p><strong>返回值：</strong> 所有样本的平均损失</p>
	</div>
	<hr class="docutils" />
	<div class="section" id="forward-flag-x-y">
	<h3>forward(flag, x, y)<a class="headerlink" href="#forward-flag-x-y" title="Permalink to this headline">¶</a></h3>
	<p>计算损失值</p>
	<p><strong>参数：</strong></p>
	<ul class="simple">
	<li><strong>flag</strong> – kTrain/kEval或布尔值。如果是kTrain/True，那么在下一次调用forward前会先调用backward计算梯度。</li>
	<li><strong>x (Tensor)</strong> – 预测Tensor</li>
	<li><strong>y (Tensor)</strong> – 真实Tensor, x.shape[0]必须和y.shape[0]相同</li>
	</ul>
	<p><strong>返回值：</strong> tensor，每个样本对应一个浮点型损失值</p>
	</div>
	</div>
	<hr class="docutils" />
	<div class="section" id="class-singa-loss-softmaxcrossentropy">
	<h2>class singa.loss.SoftmaxCrossEntropy<a class="headerlink" href="#class-singa-loss-softmaxcrossentropy" title="Permalink to this headline">¶</a></h2>
	<p>基类：<code class="docutils literal"><span class="pre">singa.loss.Loss</span></code></p>
	<p>此损失函数是SoftMax和交叉熵损失的结合。它通过SoftMax函数转换输入，然后根据真实值计算交叉熵损失。对于每个样本，真实值可以是一个整数作为标签索引; 或二进制数组，指示标签分布。因此，真实值可以是1维或2维tensor。对于一批样品，数据/特征tensor可以是1维（对于单个样品）或2维（对于一组样本）。</p>
	</div>
	<hr class="docutils" />
	<div class="section" id="class-singa-loss-squarederror">
	<h2>class singa.loss.SquaredError<a class="headerlink" href="#class-singa-loss-squarederror" title="Permalink to this headline">¶</a></h2>
	<p>基类：<code class="docutils literal"><span class="pre">singa.loss.Loss</span></code></p>
	<p>此损失用来衡量预测值和真实值之间的平方差。它通过Python Tensor操作实现。</p>
	<hr class="docutils" />
	<div class="section" id="id1">
	<h3>backward()<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
	<p>计算与损失相对应变量的梯度。</p>
	<p><strong>返回值：</strong> x - y</p>
	</div>
	<hr class="docutils" />
	<div class="section" id="id2">
	<h3>evaluate(flag, x, y)<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
	<p>计算平均误差。</p>
	<p><strong>返回值：</strong> 浮点型数</p>
	</div>
	<hr class="docutils" />
	<div class="section" id="id3">
	<h3>forward(flag, x, y)<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
	<p>通过0.5 * \|\|x-y\|\|^2计算损失。</p>
	<p><strong>参数：</strong></p>
	<ul class="simple">
	<li><strong>flag (int)</strong> – kTrain或kEval；如果是kTrain，那么在下一次调用forward前会先调用backward计算梯度。</li>
	<li><strong>x (Tensor)</strong> – 预测Tensor</li>
	<li><strong>y (Tensor)</strong> – 真实Tensor, 每个样本对应一个整型数, 取值为[0, x.shape[1])。</li>
	</ul>
	<p><strong>返回值：</strong> tensor，每个样本对应一个损失值</p>
	<hr class="docutils" />
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