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    <li class="toctree-l3"><a href="#_1">搜索空间参数的配置</a></li>
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                <h2 id="_1">搜索空间参数的配置<a class="headerlink" href="#_1" title="Permanent link">#</a></h2>
<p>通过参数配置搜索空间。更多搜索空间的使用可以参考<a href="../../search_space/">search_space</a></p>
<p><strong>参数：</strong></p>
<ul>
<li><strong>input_size(int|None)</strong>：- <code>input_size</code>表示输入feature map的大小。</li>
<li><strong>output_size(int|None)</strong>：- <code>output_size</code>表示输出feature map的大小。</li>
<li><strong>block_num(int|None)</strong>：- <code>block_num</code>表示搜索空间中block的数量。</li>
<li><strong>block_mask(list|None)</strong>：- <code>block_mask</code>是一组由0、1组成的列表，0表示当前block是normal block，1表示当前block是reduction block。如果设置了<code>block_mask</code>，则主要以<code>block_mask</code>为主要配置，<code>input_size</code>，<code>output_size</code>和<code>block_num</code>三种配置是无效的。</li>
</ul>
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<p>Note:<br>
1. reduction block表示经过这个block之后的feature map大小下降为之前的一半，normal block表示经过这个block之后feature map大小不变。<br>
2. <code>input_size</code>和<code>output_size</code>用来计算整个模型结构中reduction block数量。</p>
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<h2 id="sanas">SANAS<a class="headerlink" href="#sanas" title="Permanent link">#</a></h2>
<dl>
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<dt>paddleslim.nas.SANAS(configs, server_addr=("", 8881), init_temperature=100, reduce_rate=0.85, search_steps=300, save_checkpoint='./nas_checkpoint', load_checkpoint=None, is_server=True)<a href="https://github.com/PaddlePaddle/PaddleSlim/blob/develop/paddleslim/nas/sa_nas.py#L36">源代码</a></dt>
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<dd>SANAS（Simulated Annealing Neural Architecture Search）是基于模拟退火算法进行模型结构搜索的算法，一般用于离散搜索任务。</dd>
</dl>
<p><strong>参数：</strong></p>
<ul>
<li><strong>configs(list<tuple>)</strong> - 搜索空间配置列表，格式是<code>[(key, {input_size, output_size, block_num, block_mask})]</code>或者<code>[(key)]</code>（MobileNetV2、MobilenetV1和ResNet的搜索空间使用和原本网络结构相同的搜索空间，所以仅需指定<code>key</code>即可）, <code>input_size</code> 和<code>output_size</code>表示输入和输出的特征图的大小，<code>block_num</code>是指搜索网络中的block数量，<code>block_mask</code>是一组由0和1组成的列表，0代表不进行下采样的block，1代表下采样的block。 更多paddleslim提供的搜索空间配置可以参考。</li>
<li><strong>server_addr(tuple)</strong> - SANAS的地址，包括server的ip地址和端口号，如果ip地址为None或者为""的话则默认使用本机ip。默认：（"", 8881）。</li>
<li><strong>init_temperature(float)</strong> - 基于模拟退火进行搜索的初始温度。默认：100。</li>
<li><strong>reduce_rate(float)</strong> - 基于模拟退火进行搜索的衰减率。默认：0.85。</li>
<li><strong>search_steps(int)</strong> - 搜索过程迭代的次数。默认：300。</li>
<li><strong>save_checkpoint(str|None)</strong> - 保存checkpoint的文件目录，如果设置为None的话则不保存checkpoint。默认：<code>./nas_checkpoint</code>。</li>
<li><strong>load_checkpoint(str|None)</strong> - 加载checkpoint的文件目录，如果设置为None的话则不加载checkpoint。默认：None。</li>
<li><strong>is_server(bool)</strong> - 当前实例是否要启动一个server。默认：True。</li>
</ul>
<p><strong>返回：</strong>
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一个SANAS类的实例</p>
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<p><strong>示例代码：</strong>
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<div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">paddleslim.nas</span> <span class="kn">import</span> <span class="n">SANAS</span>
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<span class="n">config</span> <span class="o">=</span> <span class="p">[(</span><span class="s1">&#39;MobileNetV2Space&#39;</span><span class="p">)]</span>
<span class="n">sanas</span> <span class="o">=</span> <span class="n">SANAS</span><span class="p">(</span><span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
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</pre></div></p>
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<dl>
<dt>paddlesim.nas.SANAS.tokens2arch(tokens)</dt>
<dd>通过一组token得到实际的模型结构，一般用来把搜索到最优的token转换为模型结构用来做最后的训练。</dd>
</dl>
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<p>Note:<br>
tokens是一个列表，token映射到搜索空间转换成相应的网络结构，一组token对应唯一的一个网络结构。</p>
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<p><strong>参数：</strong></p>
<ul>
<li><strong>tokens(list):</strong> - 一组token。</li>
</ul>
<p><strong>返回：</strong>
根据传入的token得到一个模型结构实例。</p>
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<p><strong>示例代码：</strong>
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<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">as</span> <span class="nn">fluid</span>
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<span class="nb">input</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;input&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">archs</span> <span class="o">=</span> <span class="n">sanas</span><span class="o">.</span><span class="n">token2arch</span><span class="p">(</span><span class="n">tokens</span><span class="p">)</span>
<span class="k">for</span> <span class="n">arch</span> <span class="ow">in</span> <span class="n">archs</span><span class="p">:</span>
    <span class="n">output</span> <span class="o">=</span> <span class="n">arch</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
    <span class="nb">input</span> <span class="o">=</span> <span class="n">output</span>
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</pre></div></p>
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<dl>
<dt>paddleslim.nas.SANAS.next_archs()</dt>
<dd>获取下一组模型结构。</dd>
</dl>
<p><strong>返回：</strong>
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返回模型结构实例的列表，形式为list。</p>
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<p><strong>示例代码：</strong>
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<div class="codehilite"><pre><span></span><span class="kn">import</span> <span class="nn">paddle.fluid</span> <span class="k">as</span> <span class="nn">fluid</span>
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<span class="nb">input</span> <span class="o">=</span> <span class="n">fluid</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;input&#39;</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
<span class="n">archs</span> <span class="o">=</span> <span class="n">sanas</span><span class="o">.</span><span class="n">next_archs</span><span class="p">()</span>
<span class="k">for</span> <span class="n">arch</span> <span class="ow">in</span> <span class="n">archs</span><span class="p">:</span>
    <span class="n">output</span> <span class="o">=</span> <span class="n">arch</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
    <span class="nb">input</span> <span class="o">=</span> <span class="n">output</span>
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</pre></div></p>
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<dl>
<dt>paddleslim.nas.SANAS.reward(score)</dt>
<dd>把当前模型结构的得分情况回传。</dd>
</dl>
<p><strong>参数：</strong></p>
<ul>
<li><strong>score<float>:</strong> - 当前模型的得分，分数越大越好。</li>
</ul>
<p><strong>返回：</strong>
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模型结构更新成功或者失败，成功则返回<code>True</code>，失败则返回<code>False</code>。</p>
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<dl>
<dt>paddleslim.nas.SANAS.current_info()</dt>
<dd>返回当前token和搜索过程中最好的token和reward。</dd>
</dl>
<p><strong>返回：</strong>
搜索过程中最好的token，reward和当前训练的token，形式为dict。</p>
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