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‎docs/activations/index.html‎

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<div class="highlight"><pre><span class="lineno">1</span><span></span><span class="kn">from</span> <span class="nn">.swish</span> <span class="kn">import</span> <span class="n">Swish</span></pre></div>
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<div class="highlight"><pre><span class="lineno">1</span><span></span><span class="kn">from</span> <span class="nn">.swish</span> <span class="kn">import</span> <span class="n">Swish</span></pre></div>
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<div class='footer'>
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<a href="https://papers.labml.ai">Trending Research Papers</a>
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<a href="https://labml.ai">labml.ai</a>
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‎docs/activations/swish.html‎

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<div class="highlight"><pre><span class="lineno">1</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
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<div class="highlight"><pre><span class="lineno">1</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
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<span class="lineno">2</span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
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<span class="lineno">3</span>
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<span class="lineno">4</span><span class="kn">from</span> <span class="nn">labml_helpers.module</span> <span class="kn">import</span> <span class="n">Module</span></pre></div>
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<div class="highlight"><pre><span class="lineno">7</span><span class="k">class</span> <span class="nc">Swish</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">7</span><span class="k">class</span> <span class="nc">Swish</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span></pre></div>
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<div class="highlight"><pre><span class="lineno">8</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<div class="highlight"><pre><span class="lineno">8</span> <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="lineno">10</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigmoid</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sigmoid</span><span class="p">()</span></pre></div>
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<div class="highlight"><pre><span class="lineno">12</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="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
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<span class="lineno">13</span> <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></pre></div>
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<a href="https://papers.labml.ai">Trending Research Papers</a>
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‎docs/adaptive_computation/index.html‎

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<h1>Neural Networks with Adaptive Computation</h1>
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<h1>Neural Networks with Adaptive Computation</h1>
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<p>These are neural network architectures that change the computation complexity based on the
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<li>🚧 TODO: Adaptive Computation Time for Recurrent Neural Networks</li>
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<a href="https://labml.ai">labml.ai</a>
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