chore: import upstream snapshot with attribution

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<h1>CIFAR10 群归一化实验</h1>
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<div class="highlight"><pre><span class="lineno">12</span><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="lineno">13</span>
<span class="lineno">14</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">15</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
<span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.cifar10</span> <span class="kn">import</span> <span class="n">CIFAR10Configs</span><span class="p">,</span> <span class="n">CIFAR10VGGModel</span>
<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml_nn.normalization.batch_norm</span> <span class="kn">import</span> <span class="n">BatchNorm</span></pre></div>
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<h3>用于 CIFAR-10 分类的 VGG 模型</h3>
<p>这源于<a href="../../experiments/cifar10.html">通用的 VGG 风格架构</a></p>
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<div class="highlight"><pre><span class="lineno">20</span><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">CIFAR10VGGModel</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">27</span> <span class="k">def</span> <span class="nf">conv_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
<span class="lineno">28</span> <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="lineno">29</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="lineno">30</span> <span class="n">BatchNorm</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
<span class="lineno">31</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="lineno">32</span> <span class="p">)</span></pre></div>
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<div class="highlight"><pre><span class="lineno">34</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="lineno">35</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">([[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">],</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">]])</span></pre></div>
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<h3>创建模型</h3>
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<div class="highlight"><pre><span class="lineno">38</span><span class="nd">@option</span><span class="p">(</span><span class="n">CIFAR10Configs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="lineno">39</span><span class="k">def</span> <span class="nf">model</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">CIFAR10Configs</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">43</span> <span class="k">return</span> <span class="n">Model</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
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<div class="highlight"><pre><span class="lineno">46</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
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<p>创建实验</p>
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<div class="highlight"><pre><span class="lineno">48</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;cifar10&#39;</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">&#39;batch norm&#39;</span><span class="p">)</span></pre></div>
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<p>创建配置</p>
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<div class="highlight"><pre><span class="lineno">50</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">CIFAR10Configs</span><span class="p">()</span></pre></div>
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<p>装载配置</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">52</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="p">{</span>
<span class="lineno">53</span> <span class="s1">&#39;optimizer.optimizer&#39;</span><span class="p">:</span> <span class="s1">&#39;Adam&#39;</span><span class="p">,</span>
<span class="lineno">54</span> <span class="s1">&#39;optimizer.learning_rate&#39;</span><span class="p">:</span> <span class="mf">2.5e-4</span><span class="p">,</span>
<span class="lineno">55</span> <span class="s1">&#39;train_batch_size&#39;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span>
<span class="lineno">56</span> <span class="p">})</span></pre></div>
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<p>开始实验并运行训练循环</p>
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<div class="highlight"><pre><span class="lineno">58</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">59</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
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<div class="highlight"><pre><span class="lineno">63</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">64</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<h1>批量标准化的 MNIST 实验</h1>
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<div class="highlight"><pre><span class="lineno">12</span><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="lineno">13</span><span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="lineno">14</span><span class="kn">import</span> <span class="nn">torch.utils.data</span>
<span class="lineno">15</span>
<span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
<span class="lineno">18</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="lineno">19</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.mnist</span> <span class="kn">import</span> <span class="n">MNISTConfigs</span>
<span class="lineno">20</span><span class="kn">from</span> <span class="nn">labml_nn.normalization.batch_norm</span> <span class="kn">import</span> <span class="n">BatchNorm</span></pre></div>
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<h3>型号定义</h3>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">23</span><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
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<a href='#section-2'>#</a>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">28</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="lineno">29</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
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<p>请注意,我们省略了 bias 参数</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">31</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></pre></div>
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<a href='#section-4'>#</a>
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<p>具有 20 个通道的批量归一化(卷积层的输出)。此图层的输入将具有形状<code class="highlight"><span></span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="n">height</span><span class="p">(</span><span class="mi">24</span><span class="p">),</span> <span class="n">width</span><span class="p">(</span><span class="mi">24</span><span class="p">)]</span></code>
</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">34</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">BatchNorm</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span></pre></div>
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</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">36</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-6'>
<div class='docs'>
<div class='section-link'>
<a href='#section-6'>#</a>
</div>
<p>使用 50 个通道进行批量归一化。此图层的输入将具有形状<code class="highlight"><span></span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="n">height</span><span class="p">(</span><span class="mi">8</span><span class="p">),</span> <span class="n">width</span><span class="p">(</span><span class="mi">8</span><span class="p">)]</span></code>
</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">39</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn2</span> <span class="o">=</span> <span class="n">BatchNorm</span><span class="p">(</span><span class="mi">50</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-7'>
<div class='docs'>
<div class='section-link'>
<a href='#section-7'>#</a>
</div>
<p></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">41</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">*</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">500</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-8'>
<div class='docs'>
<div class='section-link'>
<a href='#section-8'>#</a>
</div>
<p>使用 500 个通道进行批量归一化(完全连接层的输出)。此图层的输入将具有形状<code class="highlight"><span></span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">500</span><span class="p">]</span></code>
</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">44</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn3</span> <span class="o">=</span> <span class="n">BatchNorm</span><span class="p">(</span><span class="mi">500</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-9'>
<div class='docs'>
<div class='section-link'>
<a href='#section-9'>#</a>
</div>
<p></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">46</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-10'>
<div class='docs'>
<div class='section-link'>
<a href='#section-10'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">48</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="lineno">49</span> <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bn1</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="lineno">50</span> <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="lineno">51</span> <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bn2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="lineno">52</span> <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="lineno">53</span> <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span> <span class="o">*</span> <span class="mi">4</span> <span class="o">*</span> <span class="mi">50</span><span class="p">)</span>
<span class="lineno">54</span> <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bn3</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">)))</span>
<span class="lineno">55</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-11'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-11'>#</a>
</div>
<h3>创建模型</h3>
<p>我们使用<a href="../../experiments/mnist.html#MNISTConfigs"><code class="highlight"><span></span><span class="n">MNISTConfigs</span></code>
</a>配置并设置一个新函数来计算模型。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">58</span><span class="nd">@option</span><span class="p">(</span><span class="n">MNISTConfigs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="lineno">59</span><span class="k">def</span> <span class="nf">model</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">MNISTConfigs</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-12'>
<div class='docs'>
<div class='section-link'>
<a href='#section-12'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">66</span> <span class="k">return</span> <span class="n">Model</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-13'>
<div class='docs'>
<div class='section-link'>
<a href='#section-13'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">69</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
</div>
</div>
<div class='section' id='section-14'>
<div class='docs'>
<div class='section-link'>
<a href='#section-14'>#</a>
</div>
<p>创建实验</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">71</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;mnist_batch_norm&#39;</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-15'>
<div class='docs'>
<div class='section-link'>
<a href='#section-15'>#</a>
</div>
<p>创建配置</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">73</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">MNISTConfigs</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-16'>
<div class='docs'>
<div class='section-link'>
<a href='#section-16'>#</a>
</div>
<p>装载配置</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">75</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="p">{</span>
<span class="lineno">76</span> <span class="s1">&#39;optimizer.optimizer&#39;</span><span class="p">:</span> <span class="s1">&#39;Adam&#39;</span><span class="p">,</span>
<span class="lineno">77</span> <span class="s1">&#39;optimizer.learning_rate&#39;</span><span class="p">:</span> <span class="mf">0.001</span><span class="p">,</span>
<span class="lineno">78</span> <span class="p">})</span></pre></div>
</div>
</div>
<div class='section' id='section-17'>
<div class='docs'>
<div class='section-link'>
<a href='#section-17'>#</a>
</div>
<p>开始实验并运行训练循环</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">80</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">81</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-18'>
<div class='docs'>
<div class='section-link'>
<a href='#section-18'>#</a>
</div>
<p></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">85</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">86</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<h1><a href="index.html">深度规范</a>实验</h1>
<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/normalization/deep_norm/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">13</span><span></span><span class="kn">import</span> <span class="nn">copy</span>
<span class="lineno">14</span>
<span class="lineno">15</span><span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">16</span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="lineno">17</span>
<span class="lineno">18</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">19</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
<span class="lineno">20</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="lineno">21</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.nlp_autoregression</span> <span class="kn">import</span> <span class="n">NLPAutoRegressionConfigs</span>
<span class="lineno">22</span><span class="kn">from</span> <span class="nn">labml_nn.normalization.deep_norm</span> <span class="kn">import</span> <span class="n">DeepNormTransformerLayer</span>
<span class="lineno">23</span><span class="kn">from</span> <span class="nn">labml_nn.transformers</span> <span class="kn">import</span> <span class="n">MultiHeadAttention</span>
<span class="lineno">24</span><span class="kn">from</span> <span class="nn">labml_nn.transformers.feed_forward</span> <span class="kn">import</span> <span class="n">FeedForward</span></pre></div>
</div>
</div>
<div class='section' id='section-1'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-1'>#</a>
</div>
<h2>自回归模型</h2>
<p>这是一个使用 DeepNorm 的自回归变压器模型。</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">27</span><span class="k">class</span> <span class="nc">AutoregressiveTransformer</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-2'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-2'>#</a>
</div>
<ul><li><code class="highlight"><span></span><span class="n">n_tokens</span></code>
是词汇表中代币的数量</li>
<li><code class="highlight"><span></span><span class="n">d_model</span></code>
是嵌入的大小</li>
<li><code class="highlight"><span></span><span class="n">n_layers</span></code>
是变压器层的数量</li>
<li><code class="highlight"><span></span><span class="n">layer</span></code>
是层。我们在变压器上使用这个<code class="highlight"><span></span><span class="n">n_layers</span></code>
副本。</li></ul>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">34</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_tokens</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">n_layers</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">layer</span><span class="p">:</span> <span class="n">DeepNormTransformerLayer</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-3'>
<div class='docs'>
<div class='section-link'>
<a href='#section-3'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">41</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-4'>
<div class='docs'>
<div class='section-link'>
<a href='#section-4'>#</a>
</div>
<p><code class="highlight"><span></span><span class="n">n_layers</span></code>
层的变压器</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">43</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">layer</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_layers</span><span class="p">)])</span></pre></div>
</div>
</div>
<div class='section' id='section-5'>
<div class='docs'>
<div class='section-link'>
<a href='#section-5'>#</a>
</div>
<p>令牌嵌入层</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">46</span> <span class="bp">self</span><span class="o">.</span><span class="n">emb</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">n_tokens</span><span class="p">,</span> <span class="n">d_model</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-6'>
<div class='docs'>
<div class='section-link'>
<a href='#section-6'>#</a>
</div>
<p>读出层</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">48</span> <span class="bp">self</span><span class="o">.</span><span class="n">readout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">d_model</span><span class="p">,</span> <span class="n">n_tokens</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-7'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-7'>#</a>
</div>
<ul><li><code class="highlight"><span></span><span class="n">x</span></code>
是形状的输入标记<code class="highlight"><span></span><span class="p">[</span><span class="n">seq_len</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">]</span></code>
</li></ul>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">50</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-8'>
<div class='docs'>
<div class='section-link'>
<a href='#section-8'>#</a>
</div>
<p>获取令牌嵌入</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">55</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">emb</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-9'>
<div class='docs'>
<div class='section-link'>
<a href='#section-9'>#</a>
</div>
<p>变压器编码</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">57</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-10'>
<div class='docs'>
<div class='section-link'>
<a href='#section-10'>#</a>
</div>
<p>获取日志</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">59</span> <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">readout</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-11'>
<div class='docs'>
<div class='section-link'>
<a href='#section-11'>#</a>
</div>
<p>返回结果</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">62</span> <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="kc">None</span></pre></div>
</div>
</div>
<div class='section' id='section-12'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-12'>#</a>
</div>
<h2>配置</h2>
<p>这继承自 <a href="../../experiments/nlp_autoregression.html#NLPAutoRegressionConfigs"><code class="highlight"><span></span><span class="n">NLPAutoRegressionConfigs</span></code>
</a></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">65</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">NLPAutoRegressionConfigs</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-13'>
<div class='docs'>
<div class='section-link'>
<a href='#section-13'>#</a>
</div>
<p>型号</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">74</span> <span class="n">model</span><span class="p">:</span> <span class="n">AutoregressiveTransformer</span></pre></div>
</div>
</div>
<div class='section' id='section-14'>
<div class='docs'>
<div class='section-link'>
<a href='#section-14'>#</a>
</div>
<p>层数</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">77</span> <span class="n">n_layers</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span></pre></div>
</div>
</div>
<div class='section' id='section-15'>
<div class='docs'>
<div class='section-link'>
<a href='#section-15'>#</a>
</div>
<p><span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord coloredeq eqc" style=""><span class="mord mathnormal" style="margin-right:0.0037em">α</span></span></span></span></span></span><span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.8888799999999999em;vertical-align:-0.19444em;"></span><span class="mord coloredeq eqd" style=""><span class="mord mathnormal" style="margin-right:0.05278em">β</span></span></span></span></span></span>于 DeepNorm</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">80</span> <span class="n">deep_norm_alpha</span><span class="p">:</span> <span class="nb">float</span>
<span class="lineno">81</span> <span class="n">deep_norm_beta</span><span class="p">:</span> <span class="nb">float</span></pre></div>
</div>
</div>
<div class='section' id='section-16'>
<div class='docs'>
<div class='section-link'>
<a href='#section-16'>#</a>
</div>
<p>关注的头部数量</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">84</span> <span class="n">n_heads</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span></pre></div>
</div>
</div>
<div class='section' id='section-17'>
<div class='docs'>
<div class='section-link'>
<a href='#section-17'>#</a>
</div>
<p>嵌入大小</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">86</span> <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">64</span></pre></div>
</div>
</div>
<div class='section' id='section-18'>
<div class='docs'>
<div class='section-link'>
<a href='#section-18'>#</a>
</div>
<p>每个注意头的大小</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">88</span> <span class="n">d_k</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16</span></pre></div>
</div>
</div>
<div class='section' id='section-19'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-19'>#</a>
</div>
<h4>计算<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord coloredeq eqc" style=""><span class="mord mathnormal" style="margin-right:0.0037em">α</span></span></span></span></span></span></h4>
<p><span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord coloredeq eqc" style=""><span class="mord mathnormal" style="margin-right:0.0037em">α</span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.20402em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">2</span><span class="mord mathnormal" style="margin-right:0.10903em;">M</span><span class="mclose"><span class="mclose">)</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.9540200000000001em;"><span style="top:-3.363em;margin-right:0.05em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight"><span class="mopen nulldelimiter sizing reset-size3 size6"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8443142857142858em;"><span style="top:-2.656em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mtight"><span class="mord mtight">4</span></span></span></span><span style="top:-3.2255000000000003em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line mtight" style="border-bottom-width:0.049em;"></span></span><span style="top:-3.384em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mtight"><span class="mord mtight">1</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.344em;"><span></span></span></span></span></span><span class="mclose nulldelimiter sizing reset-size3 size6"></span></span></span></span></span></span></span></span></span></span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">91</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">deep_norm_alpha</span><span class="p">)</span>
<span class="lineno">92</span><span class="k">def</span> <span class="nf">_deep_norm_alpha</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-20'>
<div class='docs'>
<div class='section-link'>
<a href='#section-20'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">98</span> <span class="k">return</span> <span class="p">(</span><span class="mf">2.</span> <span class="o">*</span> <span class="n">c</span><span class="o">.</span><span class="n">n_layers</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="mf">4.</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-21'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-21'>#</a>
</div>
<h4>计算<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.8888799999999999em;vertical-align:-0.19444em;"></span><span class="mord coloredeq eqd" style=""><span class="mord mathnormal" style="margin-right:0.05278em">β</span></span></span></span></span></span></h4>
<p><span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.8888799999999999em;vertical-align:-0.19444em;"></span><span class="mord coloredeq eqd" style=""><span class="mord mathnormal" style="margin-right:0.05278em">β</span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1.20402em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">8</span><span class="mord mathnormal" style="margin-right:0.10903em;">M</span><span class="mclose"><span class="mclose">)</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.9540200000000001em;"><span style="top:-3.363em;margin-right:0.05em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight"></span><span class="mord mtight"><span class="mopen nulldelimiter sizing reset-size3 size6"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8443142857142858em;"><span style="top:-2.656em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mtight"><span class="mord mtight">4</span></span></span></span><span style="top:-3.2255000000000003em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line mtight" style="border-bottom-width:0.049em;"></span></span><span style="top:-3.384em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mtight"><span class="mord mtight">1</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.344em;"><span></span></span></span></span></span><span class="mclose nulldelimiter sizing reset-size3 size6"></span></span></span></span></span></span></span></span></span></span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">101</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">deep_norm_beta</span><span class="p">)</span>
<span class="lineno">102</span><span class="k">def</span> <span class="nf">_deep_norm_beta</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-22'>
<div class='docs'>
<div class='section-link'>
<a href='#section-22'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">108</span> <span class="k">return</span> <span class="p">(</span><span class="mf">8.</span> <span class="o">*</span> <span class="n">c</span><span class="o">.</span><span class="n">n_layers</span><span class="p">)</span> <span class="o">**</span> <span class="o">-</span><span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="mf">4.</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-23'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-23'>#</a>
</div>
<h4>初始化模型</h4>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">111</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="lineno">112</span><span class="k">def</span> <span class="nf">_model</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
</div>
</div>
<div class='section' id='section-24'>
<div class='docs'>
<div class='section-link'>
<a href='#section-24'>#</a>
</div>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">116</span> <span class="n">m</span> <span class="o">=</span> <span class="n">AutoregressiveTransformer</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">n_tokens</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">d_model</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">n_layers</span><span class="p">,</span>
<span class="lineno">117</span> <span class="n">DeepNormTransformerLayer</span><span class="p">(</span><span class="n">d_model</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">d_model</span><span class="p">,</span>
<span class="lineno">118</span> <span class="n">deep_norm_alpha</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">deep_norm_alpha</span><span class="p">,</span>
<span class="lineno">119</span> <span class="n">deep_norm_beta</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">deep_norm_beta</span><span class="p">,</span>
<span class="lineno">120</span> <span class="n">feed_forward</span><span class="o">=</span><span class="n">FeedForward</span><span class="p">(</span><span class="n">d_model</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">d_model</span><span class="p">,</span>
<span class="lineno">121</span> <span class="n">d_ff</span><span class="o">=</span><span class="n">c</span><span class="o">.</span><span class="n">d_model</span> <span class="o">*</span> <span class="mi">4</span><span class="p">),</span>
<span class="lineno">122</span> <span class="n">self_attn</span><span class="o">=</span><span class="n">MultiHeadAttention</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">n_heads</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">d_model</span><span class="p">,</span>
<span class="lineno">123</span> <span class="n">dropout_prob</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)))</span>
<span class="lineno">124</span>
<span class="lineno">125</span> <span class="k">return</span> <span class="n">m</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
</div>
</div>
<div class='section' id='section-25'>
<div class='docs doc-strings'>
<div class='section-link'>
<a href='#section-25'>#</a>
</div>
<h4>创建并运行实验</h4>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">128</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
</div>
</div>
<div class='section' id='section-26'>
<div class='docs'>
<div class='section-link'>
<a href='#section-26'>#</a>
</div>
<p>创建实验</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">133</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;deep_norm&quot;</span><span class="p">,</span> <span class="n">writers</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;screen&#39;</span><span class="p">,</span> <span class="s1">&#39;web_api&#39;</span><span class="p">})</span></pre></div>
</div>
</div>
<div class='section' id='section-27'>
<div class='docs'>
<div class='section-link'>
<a href='#section-27'>#</a>
</div>
<p>创建配置</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">135</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span></pre></div>
</div>
</div>
<div class='section' id='section-28'>
<div class='docs'>
<div class='section-link'>
<a href='#section-28'>#</a>
</div>
<p>覆盖配置</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">137</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="p">{</span></pre></div>
</div>
</div>
<div class='section' id='section-29'>
<div class='docs'>
<div class='section-link'>
<a href='#section-29'>#</a>
</div>
<p>使用角色等级分词器</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">139</span> <span class="s1">&#39;tokenizer&#39;</span><span class="p">:</span> <span class="s1">&#39;character&#39;</span><span class="p">,</span></pre></div>
</div>
</div>
<div class='section' id='section-30'>
<div class='docs'>
<div class='section-link'>
<a href='#section-30'>#</a>
</div>
<p>提示分隔符为空</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">141</span> <span class="s1">&#39;prompt_separator&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span></pre></div>
</div>
</div>
<div class='section' id='section-31'>
<div class='docs'>
<div class='section-link'>
<a href='#section-31'>#</a>
</div>
<p>开始采样提示</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">143</span> <span class="s1">&#39;prompt&#39;</span><span class="p">:</span> <span class="s1">&#39;It is &#39;</span><span class="p">,</span></pre></div>
</div>
</div>
<div class='section' id='section-32'>
<div class='docs'>
<div class='section-link'>
<a href='#section-32'>#</a>
</div>
<p>使用小莎士比亚数据集</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">145</span> <span class="s1">&#39;text&#39;</span><span class="p">:</span> <span class="s1">&#39;tiny_shakespeare&#39;</span><span class="p">,</span></pre></div>
</div>
</div>
<div class='section' id='section-33'>
<div class='docs'>
<div class='section-link'>
<a href='#section-33'>#</a>
</div>
<p>使用上下文大小为<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">256</span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">148</span> <span class="s1">&#39;seq_len&#39;</span><span class="p">:</span> <span class="mi">256</span><span class="p">,</span></pre></div>
</div>
</div>
<div class='section' id='section-34'>
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<div class='section-link'>
<a href='#section-34'>#</a>
</div>
<p>训练 32 个时代</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">150</span> <span class="s1">&#39;epochs&#39;</span><span class="p">:</span> <span class="mi">32</span><span class="p">,</span></pre></div>
</div>
</div>
<div class='section' id='section-35'>
<div class='docs'>
<div class='section-link'>
<a href='#section-35'>#</a>
</div>
<p>批量大小<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">16</span></span></span></span></span></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">152</span> <span class="s1">&#39;batch_size&#39;</span><span class="p">:</span> <span class="mi">16</span><span class="p">,</span></pre></div>
</div>
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<div class='section' id='section-36'>
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<div class='section-link'>
<a href='#section-36'>#</a>
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<p>在训练和验证之间切换每个纪元的<span ><span class="katex"><span aria-hidden="true" class="katex-html"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">10</span></span></span></span></span>次数</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">154</span> <span class="s1">&#39;inner_iterations&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span></pre></div>
</div>
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<div class='section-link'>
<a href='#section-37'>#</a>
</div>
<p>层数</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">157</span> <span class="s1">&#39;n_layers&#39;</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span></pre></div>
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<p>没有预热的 Adam 优化器</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">161</span> <span class="s1">&#39;optimizer.optimizer&#39;</span><span class="p">:</span> <span class="s1">&#39;Adam&#39;</span><span class="p">,</span>
<span class="lineno">162</span> <span class="s1">&#39;optimizer.learning_rate&#39;</span><span class="p">:</span> <span class="mf">1.25e-4</span><span class="p">,</span>
<span class="lineno">163</span> <span class="p">})</span></pre></div>
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<a href='#section-39'>#</a>
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<p>设置用于保存和加载的模型</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">166</span> <span class="n">experiment</span><span class="o">.</span><span class="n">add_pytorch_models</span><span class="p">({</span><span class="s1">&#39;model&#39;</span><span class="p">:</span> <span class="n">conf</span><span class="o">.</span><span class="n">model</span><span class="p">})</span></pre></div>
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<p>开始实验</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">169</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span></pre></div>
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<p>跑步训练</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">171</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
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<p></p>
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<div class="highlight"><pre><span class="lineno">175</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">176</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<h1>CIFAR10 群归一化实验</h1>
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<div class="highlight"><pre><span class="lineno">12</span><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="lineno">13</span>
<span class="lineno">14</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">15</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
<span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.cifar10</span> <span class="kn">import</span> <span class="n">CIFAR10Configs</span><span class="p">,</span> <span class="n">CIFAR10VGGModel</span>
<span class="lineno">18</span><span class="kn">from</span> <span class="nn">labml_nn.normalization.group_norm</span> <span class="kn">import</span> <span class="n">GroupNorm</span></pre></div>
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<h3>VGG model for CIFAR-10 classification</h3>
<p>This derives from the <a href="../../experiments/cifar10.html">generic VGG style architecture</a>.</p>
</div>
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<div class="highlight"><pre><span class="lineno">21</span><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">CIFAR10VGGModel</span><span class="p">):</span></pre></div>
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<a href='#section-2'>#</a>
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<div class="highlight"><pre><span class="lineno">28</span> <span class="k">def</span> <span class="nf">conv_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
<span class="lineno">29</span> <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="lineno">30</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="lineno">31</span> <span class="n">fnorm</span><span class="o">.</span><span class="n">GroupNorm</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">groups</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">),</span><span class="c1">#new</span>
<span class="lineno">32</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="lineno">33</span> <span class="p">)</span></pre></div>
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<a href='#section-3'>#</a>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">35</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">groups</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">):</span>
<span class="lineno">36</span> <span class="bp">self</span><span class="o">.</span><span class="n">groups</span> <span class="o">=</span> <span class="n">groups</span><span class="c1">#input param:groups to conv_block</span>
<span class="lineno">37</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">([[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">],</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">]])</span></pre></div>
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<div class="highlight"><pre><span class="lineno">40</span><span class="k">class</span> <span class="nc">Configs</span><span class="p">(</span><span class="n">CIFAR10Configs</span><span class="p">):</span></pre></div>
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<p>组数</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">42</span> <span class="n">groups</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16</span></pre></div>
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<h3>创建模型</h3>
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<div class="highlight"><pre><span class="lineno">45</span><span class="nd">@option</span><span class="p">(</span><span class="n">Configs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="lineno">46</span><span class="k">def</span> <span class="nf">model</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Configs</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">50</span> <span class="k">return</span> <span class="n">Model</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">groups</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
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<div class="highlight"><pre><span class="lineno">53</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
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<a href='#section-9'>#</a>
</div>
<p>创建实验</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">55</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;cifar10&#39;</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">&#39;group norm&#39;</span><span class="p">)</span></pre></div>
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<a href='#section-10'>#</a>
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<p>创建配置</p>
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<div class="highlight"><pre><span class="lineno">57</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">Configs</span><span class="p">()</span></pre></div>
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<a href='#section-11'>#</a>
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<p>装载配置</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">59</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="p">{</span>
<span class="lineno">60</span> <span class="s1">&#39;optimizer.optimizer&#39;</span><span class="p">:</span> <span class="s1">&#39;Adam&#39;</span><span class="p">,</span>
<span class="lineno">61</span> <span class="s1">&#39;optimizer.learning_rate&#39;</span><span class="p">:</span> <span class="mf">2.5e-4</span><span class="p">,</span>
<span class="lineno">62</span> <span class="p">})</span></pre></div>
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<a href='#section-12'>#</a>
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<p>开始实验并运行训练循环</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">64</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">65</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
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<p></p>
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<div class="highlight"><pre><span class="lineno">69</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">70</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<h1><a href="https://nn.labml.ai/normalization/group_norm/index.html">群组标准化</a></h1>
<p>这是 <a href="https://pytorch.org">PyTorch</a><a href="https://arxiv.org/abs/1803.08494">群组标准化</a>论文的实现。</p>
<p><a href="https://nn.labml.ai/normalization/batch_norm/index.html">批量标准化</a>适用于足够大的批量大小,但对于小批量来说却不太好,因为它会在批次上进行标准化。由于设备的内存容量,无法训练批量较大的大型模型。</p>
<p>本文介绍了群组归一化,它将一组特征归一化为一个组。这是基于这样的观察,即诸如 <a href="https://en.wikipedia.org/wiki/Scale-invariant_feature_transform">SIFT</a><a href="https://en.wikipedia.org/wiki/Histogram_of_oriented_gradients">HO</a> G之类的经典特征是按组划分的特征。该论文建议将特征信道分成组,然后分别对每个组内的所有信道进行标准化。</p>
<p>这是使用实例标准化的 <a href="https://nn.labml.ai/normalization/group_norm/experiment.html">CIFAR 10 分类模型</a></p>
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<ul><li><a href="batch_norm/index.html">批量标准化</a></li>
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<p>这演示了如何在卷积神经网络中使用实例归一化层进行分类。并不是说实例规范化是为风格转移而设计的,这只是一个演示。</p>
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<div class="highlight"><pre><span class="lineno">16</span><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="lineno">17</span>
<span class="lineno">18</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">19</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
<span class="lineno">20</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.cifar10</span> <span class="kn">import</span> <span class="n">CIFAR10Configs</span><span class="p">,</span> <span class="n">CIFAR10VGGModel</span>
<span class="lineno">21</span><span class="kn">from</span> <span class="nn">labml_nn.normalization.instance_norm</span> <span class="kn">import</span> <span class="n">InstanceNorm</span></pre></div>
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<h3>用于 CIFAR-10 分类的 VGG 模型</h3>
<p>这源于<a href="../../experiments/cifar10.html">通用的 VGG 风格架构</a></p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">24</span><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">CIFAR10VGGModel</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">31</span> <span class="k">def</span> <span class="nf">conv_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
<span class="lineno">32</span> <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="lineno">33</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="lineno">34</span> <span class="n">InstanceNorm</span><span class="p">(</span><span class="n">out_channels</span><span class="p">),</span>
<span class="lineno">35</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="lineno">36</span> <span class="p">)</span></pre></div>
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<div class="highlight"><pre><span class="lineno">38</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="lineno">39</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">([[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">],</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">]])</span></pre></div>
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<a href='#section-4'>#</a>
</div>
<h3>创建模型</h3>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">42</span><span class="nd">@option</span><span class="p">(</span><span class="n">CIFAR10Configs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="lineno">43</span><span class="k">def</span> <span class="nf">_model</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">CIFAR10Configs</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">47</span> <span class="k">return</span> <span class="n">Model</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
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<a href='#section-6'>#</a>
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<div class="highlight"><pre><span class="lineno">50</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
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<p>创建实验</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">52</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;cifar10&#39;</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">&#39;instance norm&#39;</span><span class="p">)</span></pre></div>
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<div class='section-link'>
<a href='#section-8'>#</a>
</div>
<p>创建配置</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">54</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">CIFAR10Configs</span><span class="p">()</span></pre></div>
</div>
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<p>装载配置</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">56</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="p">{</span>
<span class="lineno">57</span> <span class="s1">&#39;optimizer.optimizer&#39;</span><span class="p">:</span> <span class="s1">&#39;Adam&#39;</span><span class="p">,</span>
<span class="lineno">58</span> <span class="s1">&#39;optimizer.learning_rate&#39;</span><span class="p">:</span> <span class="mf">2.5e-4</span><span class="p">,</span>
<span class="lineno">59</span> <span class="p">})</span></pre></div>
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<a href='#section-10'>#</a>
</div>
<p>开始实验并运行训练循环</p>
</div>
<div class='code'>
<div class="highlight"><pre><span class="lineno">61</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">62</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
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<p></p>
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<div class="highlight"><pre><span class="lineno">66</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">67</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<h1><a href="https://nn.labml.ai/normalization/instance_norm/index.html">实例规范化</a></h1>
<p>这是 P <a href="https://pytorch.org">yTorch</a> 实现<a href="https://arxiv.org/abs/1607.08022">实例规范化:快速风格化的缺失成分</a></p>
<p>引入了实例规范化以改进<a href="https://paperswithcode.com/task/style-transfer">样式传输</a>。它基于这样的观察,即风格化不应依赖于内容图像的对比度。由于卷积网络很难学习 “对比度归一化”,本文介绍了实例规范化来做到这一点。</p>
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<p>这是<a href="https://arxiv.org/abs/1607.06450">层规范化</a><a href="https://pytorch.org">PyTorch</a> 实现。</p>
<h3><a href="https://nn.labml.ai/normalization/batch_norm/index.html">批量标准化的</a>局限性</h3>
<ul><li>你需要保持跑步手段。</li>
<li>对于 RNN 来说很棘手。每个步骤都需要不同的规范化吗?</li>
<li>不适用于小批量;大型 NLP 模型通常使用小批量进行训练。</li>
<li>需要在分布式训练中计算设备间的均值和方差。</li></ul>
<h2>层规范化</h2>
<p>图层归一化是一种更简单的归一化方法,适用于更广泛的设置。图层归一化会将输入变换为各要素的均值和单位方差为零。<em>请注意,批量归一化修复了每个元素的零均值和单位方差。</em>层归一化对所有元素的每个批次执行此操作。</p>
<p>层归一化通常用于 NLP 任务。</p>
<p>我们在大多数<a href="https://nn.labml.ai/transformers/gpt/index.html">变压器实现</a>中都使用了层归一化。</p>
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<h1>具有权重标准化的 2D 卷积层</h1>
<p>这是具有权<a href="./index.html">重标准化的</a>二维卷积层的实现</p>
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<div class="highlight"><pre><span class="lineno">13</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">14</span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="lineno">15</span><span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="lineno">16</span>
<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml_nn.normalization.weight_standardization</span> <span class="kn">import</span> <span class="n">weight_standardization</span></pre></div>
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<h2>2D 卷积层</h2>
<p>这将扩展标准 2D 卷积层,并在卷积步骤之前标准化权重。</p>
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<div class="highlight"><pre><span class="lineno">20</span><span class="k">class</span> <span class="nc">Conv2d</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">26</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span>
<span class="lineno">27</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="lineno">28</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="lineno">29</span> <span class="n">dilation</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="lineno">30</span> <span class="n">groups</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
<span class="lineno">31</span> <span class="n">bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
<span class="lineno">32</span> <span class="n">padding_mode</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s1">&#39;zeros&#39;</span><span class="p">,</span>
<span class="lineno">33</span> <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-5</span><span class="p">):</span>
<span class="lineno">34</span> <span class="nb">super</span><span class="p">(</span><span class="n">Conv2d</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span>
<span class="lineno">35</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span>
<span class="lineno">36</span> <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span>
<span class="lineno">37</span> <span class="n">dilation</span><span class="o">=</span><span class="n">dilation</span><span class="p">,</span>
<span class="lineno">38</span> <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span>
<span class="lineno">39</span> <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span>
<span class="lineno">40</span> <span class="n">padding_mode</span><span class="o">=</span><span class="n">padding_mode</span><span class="p">)</span>
<span class="lineno">41</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span></pre></div>
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<div class="highlight"><pre><span class="lineno">43</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
<span class="lineno">44</span> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">weight_standardization</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride</span><span class="p">,</span>
<span class="lineno">45</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dilation</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">groups</span><span class="p">)</span></pre></div>
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<p>验证张量大小的简单测试</p>
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<div class="highlight"><pre><span class="lineno">48</span><span class="k">def</span> <span class="nf">_test</span><span class="p">():</span></pre></div>
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<div class="highlight"><pre><span class="lineno">52</span> <span class="n">conv2d</span> <span class="o">=</span> <span class="n">Conv2d</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="lineno">53</span> <span class="kn">from</span> <span class="nn">labml.logger</span> <span class="kn">import</span> <span class="n">inspect</span>
<span class="lineno">54</span> <span class="n">inspect</span><span class="p">(</span><span class="n">conv2d</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
<span class="lineno">55</span> <span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">56</span> <span class="n">inspect</span><span class="p">(</span><span class="n">conv2d</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)))</span>
<span class="lineno">57</span>
<span class="lineno">58</span>
<span class="lineno">59</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">60</span> <span class="n">_test</span><span class="p">()</span></pre></div>
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<h1>CIFAR10 试验,尝试权重标准化和批次通道规范化</h1>
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<div class="highlight"><pre><span class="lineno">12</span><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="lineno">13</span>
<span class="lineno">14</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">experiment</span>
<span class="lineno">15</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">option</span>
<span class="lineno">16</span><span class="kn">from</span> <span class="nn">labml_nn.experiments.cifar10</span> <span class="kn">import</span> <span class="n">CIFAR10Configs</span><span class="p">,</span> <span class="n">CIFAR10VGGModel</span>
<span class="lineno">17</span><span class="kn">from</span> <span class="nn">labml_nn.normalization.batch_channel_norm</span> <span class="kn">import</span> <span class="n">BatchChannelNorm</span>
<span class="lineno">18</span><span class="kn">from</span> <span class="nn">labml_nn.normalization.weight_standardization.conv2d</span> <span class="kn">import</span> <span class="n">Conv2d</span></pre></div>
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<h3>用于 CIFAR-10 分类的 VGG 模型</h3>
<p>这源于<a href="../../experiments/cifar10.html">通用的 VGG 风格架构</a></p>
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<div class="highlight"><pre><span class="lineno">21</span><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">CIFAR10VGGModel</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">28</span> <span class="k">def</span> <span class="nf">conv_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
<span class="lineno">29</span> <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
<span class="lineno">30</span> <span class="n">Conv2d</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
<span class="lineno">31</span> <span class="n">BatchChannelNorm</span><span class="p">(</span><span class="n">out_channels</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span>
<span class="lineno">32</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
<span class="lineno">33</span> <span class="p">)</span></pre></div>
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<div class="highlight"><pre><span class="lineno">35</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="lineno">36</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">([[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> <span class="p">[</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">],</span> <span class="p">[</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">],</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span> <span class="p">[</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">]])</span></pre></div>
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<h3>创建模型</h3>
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<div class="highlight"><pre><span class="lineno">39</span><span class="nd">@option</span><span class="p">(</span><span class="n">CIFAR10Configs</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="lineno">40</span><span class="k">def</span> <span class="nf">_model</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">CIFAR10Configs</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">44</span> <span class="k">return</span> <span class="n">Model</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
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<div class="highlight"><pre><span class="lineno">47</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span></pre></div>
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<p>创建实验</p>
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<div class="highlight"><pre><span class="lineno">49</span> <span class="n">experiment</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;cifar10&#39;</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">&#39;weight standardization&#39;</span><span class="p">)</span></pre></div>
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<p>创建配置</p>
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<div class="highlight"><pre><span class="lineno">51</span> <span class="n">conf</span> <span class="o">=</span> <span class="n">CIFAR10Configs</span><span class="p">()</span></pre></div>
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<p>装载配置</p>
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<div class='code'>
<div class="highlight"><pre><span class="lineno">53</span> <span class="n">experiment</span><span class="o">.</span><span class="n">configs</span><span class="p">(</span><span class="n">conf</span><span class="p">,</span> <span class="p">{</span>
<span class="lineno">54</span> <span class="s1">&#39;optimizer.optimizer&#39;</span><span class="p">:</span> <span class="s1">&#39;Adam&#39;</span><span class="p">,</span>
<span class="lineno">55</span> <span class="s1">&#39;optimizer.learning_rate&#39;</span><span class="p">:</span> <span class="mf">2.5e-4</span><span class="p">,</span>
<span class="lineno">56</span> <span class="s1">&#39;train_batch_size&#39;</span><span class="p">:</span> <span class="mi">64</span><span class="p">,</span>
<span class="lineno">57</span> <span class="p">})</span></pre></div>
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<p>开始实验并运行训练循环</p>
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<div class="highlight"><pre><span class="lineno">59</span> <span class="k">with</span> <span class="n">experiment</span><span class="o">.</span><span class="n">start</span><span class="p">():</span>
<span class="lineno">60</span> <span class="n">conf</span><span class="o">.</span><span class="n">run</span><span class="p">()</span></pre></div>
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<div class="highlight"><pre><span class="lineno">64</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="lineno">65</span> <span class="n">main</span><span class="p">()</span></pre></div>
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<h1><a href="https://nn.labml.ai/normalization/weight_standardization/index.html">重量标准化</a></h1>
<p>这是 <a href="https://pytorch.org">PyTorch</a> 在论文《<a href="https://arxiv.org/abs/1903.10520">使用批次通道标准化和权重标准化的微批量训练》中实现的重量标准化</a>。我们还有一个<a href="https://nn.labml.ai/normalization/batch_channel_norm/index.html">带注释的批处理信道规范化实现</a></p>
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