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docs: generated API documentation (483 pages)
2026-07-24 11:55:47 +00:00

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&#160;<span id="projectnumber">3.1.0</span>
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<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="classvde_1_1sdf_1_1GradientDescent-members.html">所有成员列表</a> </div>
<div class="headertitle">
<div class="title">vde::sdf::GradientDescent类 参考</div> </div>
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<p>固定学习率的简单梯度下降优化器
<a href="classvde_1_1sdf_1_1GradientDescent.html#details">更多...</a></p>
<p><code>#include &lt;<a class="el" href="sdf__optimize_8h_source.html">sdf_optimize.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:a3278d73a572d2439849e704278fe288e"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classvde_1_1sdf_1_1GradientDescent.html#a3278d73a572d2439849e704278fe288e">GradientDescent</a> (double lr)</td></tr>
<tr class="memdesc:a3278d73a572d2439849e704278fe288e"><td class="mdescLeft">&#160;</td><td class="mdescRight">构造优化器 <a href="classvde_1_1sdf_1_1GradientDescent.html#a3278d73a572d2439849e704278fe288e">更多...</a><br /></td></tr>
<tr class="separator:a3278d73a572d2439849e704278fe288e"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad2b8fd13bd3621248f58b96ad203cf20"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classvde_1_1sdf_1_1GradientDescent.html#ad2b8fd13bd3621248f58b96ad203cf20">step</a> (std::vector&lt; double &gt; &amp;params, const std::function&lt; double(const std::vector&lt; double &gt; &amp;, std::vector&lt; double &gt; &amp;)&gt; &amp;grad_fn)</td></tr>
<tr class="memdesc:ad2b8fd13bd3621248f58b96ad203cf20"><td class="mdescLeft">&#160;</td><td class="mdescRight">执行单步梯度下降 <a href="classvde_1_1sdf_1_1GradientDescent.html#ad2b8fd13bd3621248f58b96ad203cf20">更多...</a><br /></td></tr>
<tr class="separator:ad2b8fd13bd3621248f58b96ad203cf20"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a60a9114d4dc5df50682390ab1747615d"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classvde_1_1sdf_1_1GradientDescent.html#a60a9114d4dc5df50682390ab1747615d">set_learning_rate</a> (double lr)</td></tr>
<tr class="memdesc:a60a9114d4dc5df50682390ab1747615d"><td class="mdescLeft">&#160;</td><td class="mdescRight">设置学习率 <a href="classvde_1_1sdf_1_1GradientDescent.html#a60a9114d4dc5df50682390ab1747615d">更多...</a><br /></td></tr>
<tr class="separator:a60a9114d4dc5df50682390ab1747615d"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a733ab9edb64d4013c59d48940eef202a"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classvde_1_1sdf_1_1GradientDescent.html#a733ab9edb64d4013c59d48940eef202a">learning_rate</a> () const</td></tr>
<tr class="memdesc:a733ab9edb64d4013c59d48940eef202a"><td class="mdescLeft">&#160;</td><td class="mdescRight">获取学习率 <a href="classvde_1_1sdf_1_1GradientDescent.html#a733ab9edb64d4013c59d48940eef202a">更多...</a><br /></td></tr>
<tr class="separator:a733ab9edb64d4013c59d48940eef202a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1691b2a9296d4c0e2dc6a86c57d5620e"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classvde_1_1sdf_1_1GradientDescent.html#a1691b2a9296d4c0e2dc6a86c57d5620e">iteration</a> () const</td></tr>
<tr class="memdesc:a1691b2a9296d4c0e2dc6a86c57d5620e"><td class="mdescLeft">&#160;</td><td class="mdescRight">获取当前迭代次数 <a href="classvde_1_1sdf_1_1GradientDescent.html#a1691b2a9296d4c0e2dc6a86c57d5620e">更多...</a><br /></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><p>固定学习率的简单梯度下降优化器 </p>
<p>管理迭代计数和学习率，提供 <a class="el" href="classvde_1_1sdf_1_1GradientDescent.html#ad2b8fd13bd3621248f58b96ad203cf20" title="执行单步梯度下降">step()</a> 方法执行单步参数更新。</p>
<div class="fragment"><div class="line"><span class="keyword">auto</span> params = <a class="code" href="namespacevde_1_1sdf.html#ad6473a81281335b9b3aff1f1f145c448">collect_params</a>(root);</div>
<div class="line"><a class="code" href="classvde_1_1sdf_1_1GradientDescent.html#a3278d73a572d2439849e704278fe288e">GradientDescent</a> gd(0.01);</div>
<div class="line"><span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; 100; ++i) {</div>
<div class="line"> <span class="keyword">auto</span> loss = gd.step(param_vals, [&amp;](<span class="keyword">auto</span>&amp; vals, <span class="keyword">auto</span>&amp; grad) {</div>
<div class="line"> <span class="comment">// 填充 grad，返回 loss</span></div>
<div class="line"> });</div>
<div class="line"> <span class="keywordflow">if</span> (loss &lt; tol) <span class="keywordflow">break</span>;</div>
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<div class="ttc" id="aclassvde_1_1sdf_1_1GradientDescent_html_a3278d73a572d2439849e704278fe288e"><div class="ttname"><a href="classvde_1_1sdf_1_1GradientDescent.html#a3278d73a572d2439849e704278fe288e">vde::sdf::GradientDescent::GradientDescent</a></div><div class="ttdeci">GradientDescent(double lr)</div><div class="ttdoc">构造优化器</div><div class="ttdef"><b>Definition:</b> <a href="sdf__optimize_8h_source.html#l00434">sdf_optimize.h:434</a></div></div>
<div class="ttc" id="anamespacevde_1_1sdf_html_ad6473a81281335b9b3aff1f1f145c448"><div class="ttname"><a href="namespacevde_1_1sdf.html#ad6473a81281335b9b3aff1f1f145c448">vde::sdf::collect_params</a></div><div class="ttdeci">std::vector&lt; ParamRef &gt; collect_params(SdfNodePtr &amp;root)</div><div class="ttdoc">从叶节点收集所有可变数值参数</div></div>
</div><!-- fragment -->
<p class="definition">在文件 <a class="el" href="sdf__optimize_8h_source.html">sdf_optimize.h</a> 第 <a class="el" href="sdf__optimize_8h_source.html#l00428">428</a> 行定义.</p>
</div><h2 class="groupheader">构造及析构函数说明</h2>
<a id="a3278d73a572d2439849e704278fe288e"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a3278d73a572d2439849e704278fe288e">&#9670;&nbsp;</a></span>GradientDescent()</h2>
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<td class="mlabels-left">
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<td class="memname">vde::sdf::GradientDescent::GradientDescent </td>
<td>(</td>
<td class="paramtype">double&#160;</td>
<td class="paramname"><em>lr</em></td><td>)</td>
<td></td>
</tr>
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</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span><span class="mlabel">explicit</span></span> </td>
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</div><div class="memdoc">
<p>构造优化器 </p>
<dl class="params"><dt>参数</dt><dd>
<table class="params">
<tr><td class="paramname">lr</td><td>学习率（步长因子） </td></tr>
</table>
</dd>
</dl>
<p class="definition">在文件 <a class="el" href="sdf__optimize_8h_source.html">sdf_optimize.h</a> 第 <a class="el" href="sdf__optimize_8h_source.html#l00434">434</a> 行定义.</p>
</div>
</div>
<h2 class="groupheader">成员函数说明</h2>
<a id="a1691b2a9296d4c0e2dc6a86c57d5620e"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a1691b2a9296d4c0e2dc6a86c57d5620e">&#9670;&nbsp;</a></span>iteration()</h2>
<div class="memitem">
<div class="memproto">
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<tr>
<td class="mlabels-left">
<table class="memname">
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<td class="memname">int vde::sdf::GradientDescent::iteration </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td> const</td>
</tr>
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</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
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</table>
</div><div class="memdoc">
<p>获取当前迭代次数 </p>
<p class="definition">在文件 <a class="el" href="sdf__optimize_8h_source.html">sdf_optimize.h</a> 第 <a class="el" href="sdf__optimize_8h_source.html#l00459">459</a> 行定义.</p>
</div>
</div>
<a id="a733ab9edb64d4013c59d48940eef202a"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a733ab9edb64d4013c59d48940eef202a">&#9670;&nbsp;</a></span>learning_rate()</h2>
<div class="memitem">
<div class="memproto">
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<tr>
<td class="mlabels-left">
<table class="memname">
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<td class="memname">double vde::sdf::GradientDescent::learning_rate </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td> const</td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>获取学习率 </p>
<p class="definition">在文件 <a class="el" href="sdf__optimize_8h_source.html">sdf_optimize.h</a> 第 <a class="el" href="sdf__optimize_8h_source.html#l00456">456</a> 行定义.</p>
</div>
</div>
<a id="a60a9114d4dc5df50682390ab1747615d"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a60a9114d4dc5df50682390ab1747615d">&#9670;&nbsp;</a></span>set_learning_rate()</h2>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
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<td class="memname">void vde::sdf::GradientDescent::set_learning_rate </td>
<td>(</td>
<td class="paramtype">double&#160;</td>
<td class="paramname"><em>lr</em></td><td>)</td>
<td></td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>设置学习率 </p>
<p class="definition">在文件 <a class="el" href="sdf__optimize_8h_source.html">sdf_optimize.h</a> 第 <a class="el" href="sdf__optimize_8h_source.html#l00453">453</a> 行定义.</p>
</div>
</div>
<a id="ad2b8fd13bd3621248f58b96ad203cf20"></a>
<h2 class="memtitle"><span class="permalink"><a href="#ad2b8fd13bd3621248f58b96ad203cf20">&#9670;&nbsp;</a></span>step()</h2>
<div class="memitem">
<div class="memproto">
<table class="memname">
<tr>
<td class="memname">double vde::sdf::GradientDescent::step </td>
<td>(</td>
<td class="paramtype">std::vector&lt; double &gt; &amp;&#160;</td>
<td class="paramname"><em>params</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const std::function&lt; double(const std::vector&lt; double &gt; &amp;, std::vector&lt; double &gt; &amp;)&gt; &amp;&#160;</td>
<td class="paramname"><em>grad_fn</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div><div class="memdoc">
<p>执行单步梯度下降 </p>
<p>调用 grad_fn 获取当前参数的梯度和损失值， 然后沿负梯度方向更新参数: param_i -= lr · grad_i。</p>
<dl class="params"><dt>参数</dt><dd>
<table class="params">
<tr><td class="paramname">params</td><td>参数向量（原地修改） </td></tr>
<tr><td class="paramname">grad_fn</td><td>梯度计算函数，签名为 double(const std::vector&lt;double&gt;&amp; vals, std::vector&lt;double&gt;&amp; grad_out) 接受当前参数值，填充 grad_out 并返回损失标量。 </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>返回</dt><dd>当前损失值 </dd></dl>
</div>
</div>
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