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<div class="section" id="torch-nn-functional">
<h1>torch.nn.functional<a class="headerlink" href="#torch-nn-functional" title="Permalink to this heading">¶</a></h1>
<div class="section" id="convolution-functions">
<h2>Convolution functions<a class="headerlink" href="#convolution-functions" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.functional.conv1d"/><a class="reference internal" href="generated/torch.nn.functional.conv1d.html#torch.nn.functional.conv1d" title="torch.nn.functional.conv1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conv1d</span></code></a></p></td>
<td><p>Applies a 1D convolution over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.conv2d"/><a class="reference internal" href="generated/torch.nn.functional.conv2d.html#torch.nn.functional.conv2d" title="torch.nn.functional.conv2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conv2d</span></code></a></p></td>
<td><p>Applies a 2D convolution over an input image composed of several input planes.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.conv3d"/><a class="reference internal" href="generated/torch.nn.functional.conv3d.html#torch.nn.functional.conv3d" title="torch.nn.functional.conv3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conv3d</span></code></a></p></td>
<td><p>Applies a 3D convolution over an input image composed of several input planes.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.conv_transpose1d"/><a class="reference internal" href="generated/torch.nn.functional.conv_transpose1d.html#torch.nn.functional.conv_transpose1d" title="torch.nn.functional.conv_transpose1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conv_transpose1d</span></code></a></p></td>
<td><p>Applies a 1D transposed convolution operator over an input signal composed of several input planes, sometimes also called "deconvolution".</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.conv_transpose2d"/><a class="reference internal" href="generated/torch.nn.functional.conv_transpose2d.html#torch.nn.functional.conv_transpose2d" title="torch.nn.functional.conv_transpose2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conv_transpose2d</span></code></a></p></td>
<td><p>Applies a 2D transposed convolution operator over an input image composed of several input planes, sometimes also called "deconvolution".</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.conv_transpose3d"/><a class="reference internal" href="generated/torch.nn.functional.conv_transpose3d.html#torch.nn.functional.conv_transpose3d" title="torch.nn.functional.conv_transpose3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conv_transpose3d</span></code></a></p></td>
<td><p>Applies a 3D transposed convolution operator over an input image composed of several input planes, sometimes also called "deconvolution"</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.unfold"/><a class="reference internal" href="generated/torch.nn.functional.unfold.html#torch.nn.functional.unfold" title="torch.nn.functional.unfold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unfold</span></code></a></p></td>
<td><p>Extract sliding local blocks from a batched input tensor.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.fold"/><a class="reference internal" href="generated/torch.nn.functional.fold.html#torch.nn.functional.fold" title="torch.nn.functional.fold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fold</span></code></a></p></td>
<td><p>Combine an array of sliding local blocks into a large containing tensor.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="pooling-functions">
<h2>Pooling functions<a class="headerlink" href="#pooling-functions" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.functional.avg_pool1d"/><a class="reference internal" href="generated/torch.nn.functional.avg_pool1d.html#torch.nn.functional.avg_pool1d" title="torch.nn.functional.avg_pool1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">avg_pool1d</span></code></a></p></td>
<td><p>Applies a 1D average pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.avg_pool2d"/><a class="reference internal" href="generated/torch.nn.functional.avg_pool2d.html#torch.nn.functional.avg_pool2d" title="torch.nn.functional.avg_pool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">avg_pool2d</span></code></a></p></td>
<td><p>Applies 2D average-pooling operation in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by step size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>s</mi><mi>H</mi><mo>×</mo><mi>s</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">sH \times sW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">sH</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">s</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span></span></span></span></span> steps.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.avg_pool3d"/><a class="reference internal" href="generated/torch.nn.functional.avg_pool3d.html#torch.nn.functional.avg_pool3d" title="torch.nn.functional.avg_pool3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">avg_pool3d</span></code></a></p></td>
<td><p>Applies 3D average-pooling operation in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>T</mi><mo>×</mo><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kT \times kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by step size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>s</mi><mi>T</mi><mo>×</mo><mi>s</mi><mi>H</mi><mo>×</mo><mi>s</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">sT \times sH \times sW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">s</span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.7667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">sH</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">s</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span></span></span></span></span> steps.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.max_pool1d"/><a class="reference internal" href="generated/torch.nn.functional.max_pool1d.html#torch.nn.functional.max_pool1d" title="torch.nn.functional.max_pool1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_pool1d</span></code></a></p></td>
<td><p>Applies a 1D max pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.max_pool2d"/><a class="reference internal" href="generated/torch.nn.functional.max_pool2d.html#torch.nn.functional.max_pool2d" title="torch.nn.functional.max_pool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_pool2d</span></code></a></p></td>
<td><p>Applies a 2D max pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.max_pool3d"/><a class="reference internal" href="generated/torch.nn.functional.max_pool3d.html#torch.nn.functional.max_pool3d" title="torch.nn.functional.max_pool3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_pool3d</span></code></a></p></td>
<td><p>Applies a 3D max pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.max_unpool1d"/><a class="reference internal" href="generated/torch.nn.functional.max_unpool1d.html#torch.nn.functional.max_unpool1d" title="torch.nn.functional.max_unpool1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_unpool1d</span></code></a></p></td>
<td><p>Compute a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.max_unpool2d"/><a class="reference internal" href="generated/torch.nn.functional.max_unpool2d.html#torch.nn.functional.max_unpool2d" title="torch.nn.functional.max_unpool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_unpool2d</span></code></a></p></td>
<td><p>Compute a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.max_unpool3d"/><a class="reference internal" href="generated/torch.nn.functional.max_unpool3d.html#torch.nn.functional.max_unpool3d" title="torch.nn.functional.max_unpool3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_unpool3d</span></code></a></p></td>
<td><p>Compute a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.lp_pool1d"/><a class="reference internal" href="generated/torch.nn.functional.lp_pool1d.html#torch.nn.functional.lp_pool1d" title="torch.nn.functional.lp_pool1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lp_pool1d</span></code></a></p></td>
<td><p>Apply a 1D power-average pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.lp_pool2d"/><a class="reference internal" href="generated/torch.nn.functional.lp_pool2d.html#torch.nn.functional.lp_pool2d" title="torch.nn.functional.lp_pool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lp_pool2d</span></code></a></p></td>
<td><p>Apply a 2D power-average pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.lp_pool3d"/><a class="reference internal" href="generated/torch.nn.functional.lp_pool3d.html#torch.nn.functional.lp_pool3d" title="torch.nn.functional.lp_pool3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lp_pool3d</span></code></a></p></td>
<td><p>Apply a 3D power-average pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.adaptive_max_pool1d"/><a class="reference internal" href="generated/torch.nn.functional.adaptive_max_pool1d.html#torch.nn.functional.adaptive_max_pool1d" title="torch.nn.functional.adaptive_max_pool1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adaptive_max_pool1d</span></code></a></p></td>
<td><p>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.adaptive_max_pool2d"/><a class="reference internal" href="generated/torch.nn.functional.adaptive_max_pool2d.html#torch.nn.functional.adaptive_max_pool2d" title="torch.nn.functional.adaptive_max_pool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adaptive_max_pool2d</span></code></a></p></td>
<td><p>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.adaptive_max_pool3d"/><a class="reference internal" href="generated/torch.nn.functional.adaptive_max_pool3d.html#torch.nn.functional.adaptive_max_pool3d" title="torch.nn.functional.adaptive_max_pool3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adaptive_max_pool3d</span></code></a></p></td>
<td><p>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.adaptive_avg_pool1d"/><a class="reference internal" href="generated/torch.nn.functional.adaptive_avg_pool1d.html#torch.nn.functional.adaptive_avg_pool1d" title="torch.nn.functional.adaptive_avg_pool1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adaptive_avg_pool1d</span></code></a></p></td>
<td><p>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.adaptive_avg_pool2d"/><a class="reference internal" href="generated/torch.nn.functional.adaptive_avg_pool2d.html#torch.nn.functional.adaptive_avg_pool2d" title="torch.nn.functional.adaptive_avg_pool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adaptive_avg_pool2d</span></code></a></p></td>
<td><p>Apply a 2D adaptive average pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.adaptive_avg_pool3d"/><a class="reference internal" href="generated/torch.nn.functional.adaptive_avg_pool3d.html#torch.nn.functional.adaptive_avg_pool3d" title="torch.nn.functional.adaptive_avg_pool3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adaptive_avg_pool3d</span></code></a></p></td>
<td><p>Apply a 3D adaptive average pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.fractional_max_pool2d"/><a class="reference internal" href="generated/torch.nn.functional.fractional_max_pool2d.html#torch.nn.functional.fractional_max_pool2d" title="torch.nn.functional.fractional_max_pool2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fractional_max_pool2d</span></code></a></p></td>
<td><p>Applies 2D fractional max pooling over an input signal composed of several input planes.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.fractional_max_pool3d"/><a class="reference internal" href="generated/torch.nn.functional.fractional_max_pool3d.html#torch.nn.functional.fractional_max_pool3d" title="torch.nn.functional.fractional_max_pool3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fractional_max_pool3d</span></code></a></p></td>
<td><p>Applies 3D fractional max pooling over an input signal composed of several input planes.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="attention-mechanisms">
<h2>Attention Mechanisms<a class="headerlink" href="#attention-mechanisms" title="Permalink to this heading">¶</a></h2>
<p>The <a class="reference internal" href="nn.attention.bias.html#module-torch.nn.attention.bias" title="torch.nn.attention.bias"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.nn.attention.bias</span></code></a> module contains attention_biases that are designed to be used with
scaled_dot_product_attention.</p>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.functional.scaled_dot_product_attention"/><a class="reference internal" href="generated/torch.nn.functional.scaled_dot_product_attention.html#torch.nn.functional.scaled_dot_product_attention" title="torch.nn.functional.scaled_dot_product_attention"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scaled_dot_product_attention</span></code></a></p></td>
<td><p>Computes scaled dot product attention on query, key and value tensors, using an optional attention mask if passed, and applying dropout if a probability greater than 0.0 is specified.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="non-linear-activation-functions">
<h2>Non-linear activation functions<a class="headerlink" href="#non-linear-activation-functions" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.functional.threshold"/><a class="reference internal" href="generated/torch.nn.functional.threshold.html#torch.nn.functional.threshold" title="torch.nn.functional.threshold"><code class="xref py py-obj docutils literal notranslate"><span class="pre">threshold</span></code></a></p></td>
<td><p>Apply a threshold to each element of the input Tensor.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.threshold_"/><a class="reference internal" href="generated/torch.nn.functional.threshold_.html#torch.nn.functional.threshold_" title="torch.nn.functional.threshold_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">threshold_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.nn.functional.threshold.html#torch.nn.functional.threshold" title="torch.nn.functional.threshold"><code class="xref py py-func docutils literal notranslate"><span class="pre">threshold()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.relu"/><a class="reference internal" href="generated/torch.nn.functional.relu.html#torch.nn.functional.relu" title="torch.nn.functional.relu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">relu</span></code></a></p></td>
<td><p>Applies the rectified linear unit function element-wise.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.relu_"/><a class="reference internal" href="generated/torch.nn.functional.relu_.html#torch.nn.functional.relu_" title="torch.nn.functional.relu_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">relu_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.nn.functional.relu.html#torch.nn.functional.relu" title="torch.nn.functional.relu"><code class="xref py py-func docutils literal notranslate"><span class="pre">relu()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.hardtanh"/><a class="reference internal" href="generated/torch.nn.functional.hardtanh.html#torch.nn.functional.hardtanh" title="torch.nn.functional.hardtanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hardtanh</span></code></a></p></td>
<td><p>Applies the HardTanh function element-wise.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.hardtanh_"/><a class="reference internal" href="generated/torch.nn.functional.hardtanh_.html#torch.nn.functional.hardtanh_" title="torch.nn.functional.hardtanh_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hardtanh_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.nn.functional.hardtanh.html#torch.nn.functional.hardtanh" title="torch.nn.functional.hardtanh"><code class="xref py py-func docutils literal notranslate"><span class="pre">hardtanh()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.hardswish"/><a class="reference internal" href="generated/torch.nn.functional.hardswish.html#torch.nn.functional.hardswish" title="torch.nn.functional.hardswish"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hardswish</span></code></a></p></td>
<td><p>Apply hardswish function, element-wise.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.relu6"/><a class="reference internal" href="generated/torch.nn.functional.relu6.html#torch.nn.functional.relu6" title="torch.nn.functional.relu6"><code class="xref py py-obj docutils literal notranslate"><span class="pre">relu6</span></code></a></p></td>
<td><p>Applies the element-wise function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>ReLU6</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>min</mi><mo></mo><mo stretchy="false">(</mo><mi>max</mi><mo></mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo separator="true">,</mo><mn>6</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{ReLU6}(x) = \min(\max(0,x), 6)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">ReLU6</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">6</span><span class="mclose">)</span></span></span></span></span>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.elu"/><a class="reference internal" href="generated/torch.nn.functional.elu.html#torch.nn.functional.elu" title="torch.nn.functional.elu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">elu</span></code></a></p></td>
<td><p>Apply the Exponential Linear Unit (ELU) function element-wise.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.elu_"/><a class="reference internal" href="generated/torch.nn.functional.elu_.html#torch.nn.functional.elu_" title="torch.nn.functional.elu_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">elu_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.nn.functional.elu.html#torch.nn.functional.elu" title="torch.nn.functional.elu"><code class="xref py py-func docutils literal notranslate"><span class="pre">elu()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.selu"/><a class="reference internal" href="generated/torch.nn.functional.selu.html#torch.nn.functional.selu" title="torch.nn.functional.selu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">selu</span></code></a></p></td>
<td><p>Applies element-wise, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>SELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>s</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>e</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>max</mi><mo></mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo></mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo></mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">SELU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">sc</span><span class="mord mathnormal">a</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mord mathnormal">e</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">1</span><span class="mclose">)))</span></span></span></span></span>, with <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>α</mi><mo>=</mo><mn>1.6732632423543772848170429916717</mn></mrow><annotation encoding="application/x-tex">\alpha=1.6732632423543772848170429916717</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">1.6732632423543772848170429916717</span></span></span></span></span> and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>s</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>e</mi><mo>=</mo><mn>1.0507009873554804934193349852946</mn></mrow><annotation encoding="application/x-tex">scale=1.0507009873554804934193349852946</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">sc</span><span class="mord mathnormal">a</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mord mathnormal">e</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">1.0507009873554804934193349852946</span></span></span></span></span>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.celu"/><a class="reference internal" href="generated/torch.nn.functional.celu.html#torch.nn.functional.celu" title="torch.nn.functional.celu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">celu</span></code></a></p></td>
<td><p>Applies element-wise, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>CELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo></mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo></mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo></mo><mo stretchy="false">(</mo><mi>x</mi><mi mathvariant="normal">/</mi><mi>α</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">CELU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mord">/</span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">1</span><span class="mclose">))</span></span></span></span></span>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.leaky_relu"/><a class="reference internal" href="generated/torch.nn.functional.leaky_relu.html#torch.nn.functional.leaky_relu" title="torch.nn.functional.leaky_relu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">leaky_relu</span></code></a></p></td>
<td><p>Applies element-wise, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>LeakyReLU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo></mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mtext>negative_slope</mtext><mo>∗</mo><mi>min</mi><mo></mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">LeakyReLU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.0044em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">negative_slope</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.leaky_relu_"/><a class="reference internal" href="generated/torch.nn.functional.leaky_relu_.html#torch.nn.functional.leaky_relu_" title="torch.nn.functional.leaky_relu_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">leaky_relu_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.nn.functional.leaky_relu.html#torch.nn.functional.leaky_relu" title="torch.nn.functional.leaky_relu"><code class="xref py py-func docutils literal notranslate"><span class="pre">leaky_relu()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.prelu"/><a class="reference internal" href="generated/torch.nn.functional.prelu.html#torch.nn.functional.prelu" title="torch.nn.functional.prelu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prelu</span></code></a></p></td>
<td><p>Applies element-wise the function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>PReLU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo></mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mtext>weight</mtext><mo>∗</mo><mi>min</mi><mo></mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{PReLU}(x) = \max(0,x) + \text{weight} * \min(0,x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">PReLU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8889em;vertical-align:-0.1944em;"></span><span class="mord text"><span class="mord">weight</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span> where weight is a learnable parameter.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.rrelu"/><a class="reference internal" href="generated/torch.nn.functional.rrelu.html#torch.nn.functional.rrelu" title="torch.nn.functional.rrelu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rrelu</span></code></a></p></td>
<td><p>Randomized leaky ReLU.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.rrelu_"/><a class="reference internal" href="generated/torch.nn.functional.rrelu_.html#torch.nn.functional.rrelu_" title="torch.nn.functional.rrelu_"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rrelu_</span></code></a></p></td>
<td><p>In-place version of <a class="reference internal" href="generated/torch.nn.functional.rrelu.html#torch.nn.functional.rrelu" title="torch.nn.functional.rrelu"><code class="xref py py-func docutils literal notranslate"><span class="pre">rrelu()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.glu"/><a class="reference internal" href="generated/torch.nn.functional.glu.html#torch.nn.functional.glu" title="torch.nn.functional.glu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">glu</span></code></a></p></td>
<td><p>The gated linear unit.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.gelu"/><a class="reference internal" href="generated/torch.nn.functional.gelu.html#torch.nn.functional.gelu" title="torch.nn.functional.gelu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gelu</span></code></a></p></td>
<td><p>When the approximate argument is 'none', it applies element-wise the function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>GELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi><mo>∗</mo><mi mathvariant="normal">Φ</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{GELU}(x) = x * \Phi(x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">GELU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.4653em;"></span><span class="mord mathnormal">x</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">Φ</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.logsigmoid"/><a class="reference internal" href="generated/torch.nn.functional.logsigmoid.html#torch.nn.functional.logsigmoid" title="torch.nn.functional.logsigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logsigmoid</span></code></a></p></td>
<td><p>Applies element-wise <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>LogSigmoid</mtext><mo stretchy="false">(</mo><msub><mi>x</mi><mi>i</mi></msub><mo stretchy="false">)</mo><mo>=</mo><mi>log</mi><mo></mo><mrow><mo fence="true">(</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>exp</mi><mo></mo><mo stretchy="false">(</mo><mo>−</mo><msub><mi>x</mi><mi>i</mi></msub><mo stretchy="false">)</mo></mrow></mfrac><mo fence="true">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{LogSigmoid}(x_i) = \log \left(\frac{1}{1 + \exp(-x_i)}\right)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">LogSigmoid</span></span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.8em;vertical-align:-0.65em;"></span><span class="mop">lo<span style="margin-right:0.01389em;">g</span></span><span class="mspace" style="margin-right:0.1667em;"></span><span class="minner"><span class="mopen delimcenter" style="top:0em;"><span class="delimsizing size2">(</span></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mtight"><span class="mord mathnormal mtight">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3281em;"><span style="top:-2.357em;margin-left:0em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.143em;"><span></span></span></span></span></span></span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 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:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mclose delimcenter" style="top:0em;"><span class="delimsizing size2">)</span></span></span></span></span></span></span></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.hardshrink"/><a class="reference internal" href="generated/torch.nn.functional.hardshrink.html#torch.nn.functional.hardshrink" title="torch.nn.functional.hardshrink"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hardshrink</span></code></a></p></td>
<td><p>Applies the hard shrinkage function element-wise</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.tanhshrink"/><a class="reference internal" href="generated/torch.nn.functional.tanhshrink.html#torch.nn.functional.tanhshrink" title="torch.nn.functional.tanhshrink"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tanhshrink</span></code></a></p></td>
<td><p>Applies element-wise, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Tanhshrink</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi><mo>−</mo><mtext>Tanh</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Tanhshrink}(x) = x - \text{Tanh}(x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Tanhshrink</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">x</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Tanh</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.softsign"/><a class="reference internal" href="generated/torch.nn.functional.softsign.html#torch.nn.functional.softsign" title="torch.nn.functional.softsign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">softsign</span></code></a></p></td>
<td><p>Applies element-wise, the function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>SoftSign</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mi>x</mi><mrow><mn>1</mn><mo>+</mo><mi mathvariant="normal">∣</mi><mi>x</mi><mi mathvariant="normal">∣</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{SoftSign}(x) = \frac{x}{1 + |x|}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">SoftSign</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.2154em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.6954em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mord mtight">∣</span><span class="mord mathnormal mtight">x</span><span class="mord mtight">∣</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">x</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.softplus"/><a class="reference internal" href="generated/torch.nn.functional.softplus.html#torch.nn.functional.softplus" title="torch.nn.functional.softplus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">softplus</span></code></a></p></td>
<td><p>Applies element-wise, the function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Softplus</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mn>1</mn><mi>β</mi></mfrac><mo>∗</mo><mi>log</mi><mo></mo><mo stretchy="false">(</mo><mn>1</mn><mo>+</mo><mi>exp</mi><mo></mo><mo stretchy="false">(</mo><mi>β</mi><mo>∗</mo><mi>x</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Softplus</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.3262em;vertical-align:-0.4811em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.05278em;">β</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 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:0.4811em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">lo<span style="margin-right:0.01389em;">g</span></span><span class="mopen">(</span><span class="mord">1</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.05278em;">β</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal">x</span><span class="mclose">))</span></span></span></span></span>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.softmin"/><a class="reference internal" href="generated/torch.nn.functional.softmin.html#torch.nn.functional.softmin" title="torch.nn.functional.softmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">softmin</span></code></a></p></td>
<td><p>Apply a softmin function.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.softmax"/><a class="reference internal" href="generated/torch.nn.functional.softmax.html#torch.nn.functional.softmax" title="torch.nn.functional.softmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">softmax</span></code></a></p></td>
<td><p>Apply a softmax function.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.softshrink"/><a class="reference internal" href="generated/torch.nn.functional.softshrink.html#torch.nn.functional.softshrink" title="torch.nn.functional.softshrink"><code class="xref py py-obj docutils literal notranslate"><span class="pre">softshrink</span></code></a></p></td>
<td><p>Applies the soft shrinkage function elementwise</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.gumbel_softmax"/><a class="reference internal" href="generated/torch.nn.functional.gumbel_softmax.html#torch.nn.functional.gumbel_softmax" title="torch.nn.functional.gumbel_softmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gumbel_softmax</span></code></a></p></td>
<td><p>Sample from the Gumbel-Softmax distribution (<a class="reference external" href="https://arxiv.org/abs/1611.00712">Link 1</a> <a class="reference external" href="https://arxiv.org/abs/1611.01144">Link 2</a>) and optionally discretize.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.log_softmax"/><a class="reference internal" href="generated/torch.nn.functional.log_softmax.html#torch.nn.functional.log_softmax" title="torch.nn.functional.log_softmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log_softmax</span></code></a></p></td>
<td><p>Apply a softmax followed by a logarithm.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.tanh"/><a class="reference internal" href="generated/torch.nn.functional.tanh.html#torch.nn.functional.tanh" title="torch.nn.functional.tanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tanh</span></code></a></p></td>
<td><p>Applies element-wise, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Tanh</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>tanh</mi><mo></mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mrow><mi>exp</mi><mo></mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>−</mo><mi>exp</mi><mo></mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><mrow><mi>exp</mi><mo></mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>exp</mi><mo></mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Tanh</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">tanh</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.53em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.01em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span><span class="mbin mtight">+</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.485em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span><span class="mbin mtight">−</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.sigmoid"/><a class="reference internal" href="generated/torch.nn.functional.sigmoid.html#torch.nn.functional.sigmoid" title="torch.nn.functional.sigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sigmoid</span></code></a></p></td>
<td><p>Applies the element-wise function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Sigmoid</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>exp</mi><mo></mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Sigmoid</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.3651em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 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:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.hardsigmoid"/><a class="reference internal" href="generated/torch.nn.functional.hardsigmoid.html#torch.nn.functional.hardsigmoid" title="torch.nn.functional.hardsigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hardsigmoid</span></code></a></p></td>
<td><p>Apply the Hardsigmoid function element-wise.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.silu"/><a class="reference internal" href="generated/torch.nn.functional.silu.html#torch.nn.functional.silu" title="torch.nn.functional.silu"><code class="xref py py-obj docutils literal notranslate"><span class="pre">silu</span></code></a></p></td>
<td><p>Apply the Sigmoid Linear Unit (SiLU) function, element-wise.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.mish"/><a class="reference internal" href="generated/torch.nn.functional.mish.html#torch.nn.functional.mish" title="torch.nn.functional.mish"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mish</span></code></a></p></td>
<td><p>Apply the Mish function, element-wise.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.batch_norm"/><a class="reference internal" href="generated/torch.nn.functional.batch_norm.html#torch.nn.functional.batch_norm" title="torch.nn.functional.batch_norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">batch_norm</span></code></a></p></td>
<td><p>Apply Batch Normalization for each channel across a batch of data.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.group_norm"/><a class="reference internal" href="generated/torch.nn.functional.group_norm.html#torch.nn.functional.group_norm" title="torch.nn.functional.group_norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">group_norm</span></code></a></p></td>
<td><p>Apply Group Normalization for last certain number of dimensions.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.instance_norm"/><a class="reference internal" href="generated/torch.nn.functional.instance_norm.html#torch.nn.functional.instance_norm" title="torch.nn.functional.instance_norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">instance_norm</span></code></a></p></td>
<td><p>Apply Instance Normalization independently for each channel in every data sample within a batch.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.layer_norm"/><a class="reference internal" href="generated/torch.nn.functional.layer_norm.html#torch.nn.functional.layer_norm" title="torch.nn.functional.layer_norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">layer_norm</span></code></a></p></td>
<td><p>Apply Layer Normalization for last certain number of dimensions.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.local_response_norm"/><a class="reference internal" href="generated/torch.nn.functional.local_response_norm.html#torch.nn.functional.local_response_norm" title="torch.nn.functional.local_response_norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">local_response_norm</span></code></a></p></td>
<td><p>Apply local response normalization over an input signal.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.rms_norm"/><a class="reference internal" href="generated/torch.nn.functional.rms_norm.html#torch.nn.functional.rms_norm" title="torch.nn.functional.rms_norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rms_norm</span></code></a></p></td>
<td><p>Apply Root Mean Square Layer Normalization.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.normalize"/><a class="reference internal" href="generated/torch.nn.functional.normalize.html#torch.nn.functional.normalize" title="torch.nn.functional.normalize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normalize</span></code></a></p></td>
<td><p>Perform <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>L</mi><mi>p</mi></msub></mrow><annotation encoding="application/x-tex">L_p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.9694em;vertical-align:-0.2861em;"></span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span></span></span></span></span> normalization of inputs over specified dimension.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="linear-functions">
<h2>Linear functions<a class="headerlink" href="#linear-functions" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.functional.linear"/><a class="reference internal" href="generated/torch.nn.functional.linear.html#torch.nn.functional.linear" title="torch.nn.functional.linear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linear</span></code></a></p></td>
<td><p>Applies a linear transformation to the incoming data: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>x</mi><msup><mi>A</mi><mi>T</mi></msup><mo>+</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">y = xA^T + b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.9247em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">x</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">T</span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.bilinear"/><a class="reference internal" href="generated/torch.nn.functional.bilinear.html#torch.nn.functional.bilinear" title="torch.nn.functional.bilinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bilinear</span></code></a></p></td>
<td><p>Applies a bilinear transformation to the incoming data: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><msubsup><mi>x</mi><mn>1</mn><mi>T</mi></msubsup><mi>A</mi><msub><mi>x</mi><mn>2</mn></msub><mo>+</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">y = x_1^T A x_2 + b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0894em;vertical-align:-0.2481em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-2.4519em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">T</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.2481em;"><span></span></span></span></span></span></span><span class="mord mathnormal">A</span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span></p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="dropout-functions">
<h2>Dropout functions<a class="headerlink" href="#dropout-functions" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.functional.dropout"/><a class="reference internal" href="generated/torch.nn.functional.dropout.html#torch.nn.functional.dropout" title="torch.nn.functional.dropout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dropout</span></code></a></p></td>
<td><p>During training, randomly zeroes some elements of the input tensor with probability <code class="xref py py-attr docutils literal notranslate"><span class="pre">p</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.alpha_dropout"/><a class="reference internal" href="generated/torch.nn.functional.alpha_dropout.html#torch.nn.functional.alpha_dropout" title="torch.nn.functional.alpha_dropout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">alpha_dropout</span></code></a></p></td>
<td><p>Apply alpha dropout to the input.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.feature_alpha_dropout"/><a class="reference internal" href="generated/torch.nn.functional.feature_alpha_dropout.html#torch.nn.functional.feature_alpha_dropout" title="torch.nn.functional.feature_alpha_dropout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">feature_alpha_dropout</span></code></a></p></td>
<td><p>Randomly masks out entire channels (a channel is a feature map).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.dropout1d"/><a class="reference internal" href="generated/torch.nn.functional.dropout1d.html#torch.nn.functional.dropout1d" title="torch.nn.functional.dropout1d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dropout1d</span></code></a></p></td>
<td><p>Randomly zero out entire channels (a channel is a 1D feature map).</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.dropout2d"/><a class="reference internal" href="generated/torch.nn.functional.dropout2d.html#torch.nn.functional.dropout2d" title="torch.nn.functional.dropout2d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dropout2d</span></code></a></p></td>
<td><p>Randomly zero out entire channels (a channel is a 2D feature map).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.dropout3d"/><a class="reference internal" href="generated/torch.nn.functional.dropout3d.html#torch.nn.functional.dropout3d" title="torch.nn.functional.dropout3d"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dropout3d</span></code></a></p></td>
<td><p>Randomly zero out entire channels (a channel is a 3D feature map).</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="sparse-functions">
<h2>Sparse functions<a class="headerlink" href="#sparse-functions" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.functional.embedding"/><a class="reference internal" href="generated/torch.nn.functional.embedding.html#torch.nn.functional.embedding" title="torch.nn.functional.embedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">embedding</span></code></a></p></td>
<td><p>Generate a simple lookup table that looks up embeddings in a fixed dictionary and size.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.embedding_bag"/><a class="reference internal" href="generated/torch.nn.functional.embedding_bag.html#torch.nn.functional.embedding_bag" title="torch.nn.functional.embedding_bag"><code class="xref py py-obj docutils literal notranslate"><span class="pre">embedding_bag</span></code></a></p></td>
<td><p>Compute sums, means or maxes of <cite>bags</cite> of embeddings.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.one_hot"/><a class="reference internal" href="generated/torch.nn.functional.one_hot.html#torch.nn.functional.one_hot" title="torch.nn.functional.one_hot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">one_hot</span></code></a></p></td>
<td><p>Takes LongTensor with index values of shape <code class="docutils literal notranslate"><span class="pre">(*)</span></code> and returns a tensor of shape <code class="docutils literal notranslate"><span class="pre">(*,</span> <span class="pre">num_classes)</span></code> that have zeros everywhere except where the index of last dimension matches the corresponding value of the input tensor, in which case it will be 1.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="distance-functions">
<h2>Distance functions<a class="headerlink" href="#distance-functions" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.functional.pairwise_distance"/><a class="reference internal" href="generated/torch.nn.functional.pairwise_distance.html#torch.nn.functional.pairwise_distance" title="torch.nn.functional.pairwise_distance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise_distance</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.nn.PairwiseDistance.html#torch.nn.PairwiseDistance" title="torch.nn.PairwiseDistance"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.PairwiseDistance</span></code></a> for details</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.cosine_similarity"/><a class="reference internal" href="generated/torch.nn.functional.cosine_similarity.html#torch.nn.functional.cosine_similarity" title="torch.nn.functional.cosine_similarity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cosine_similarity</span></code></a></p></td>
<td><p>Returns cosine similarity between <code class="docutils literal notranslate"><span class="pre">x1</span></code> and <code class="docutils literal notranslate"><span class="pre">x2</span></code>, computed along dim.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.pdist"/><a class="reference internal" href="generated/torch.nn.functional.pdist.html#torch.nn.functional.pdist" title="torch.nn.functional.pdist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pdist</span></code></a></p></td>
<td><p>Computes the p-norm distance between every pair of row vectors in the input.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="loss-functions">
<h2>Loss functions<a class="headerlink" href="#loss-functions" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.functional.binary_cross_entropy"/><a class="reference internal" href="generated/torch.nn.functional.binary_cross_entropy.html#torch.nn.functional.binary_cross_entropy" title="torch.nn.functional.binary_cross_entropy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">binary_cross_entropy</span></code></a></p></td>
<td><p>Measure Binary Cross Entropy between the target and input probabilities.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.binary_cross_entropy_with_logits"/><a class="reference internal" href="generated/torch.nn.functional.binary_cross_entropy_with_logits.html#torch.nn.functional.binary_cross_entropy_with_logits" title="torch.nn.functional.binary_cross_entropy_with_logits"><code class="xref py py-obj docutils literal notranslate"><span class="pre">binary_cross_entropy_with_logits</span></code></a></p></td>
<td><p>Calculate Binary Cross Entropy between target and input logits.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.poisson_nll_loss"/><a class="reference internal" href="generated/torch.nn.functional.poisson_nll_loss.html#torch.nn.functional.poisson_nll_loss" title="torch.nn.functional.poisson_nll_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">poisson_nll_loss</span></code></a></p></td>
<td><p>Poisson negative log likelihood loss.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.cosine_embedding_loss"/><a class="reference internal" href="generated/torch.nn.functional.cosine_embedding_loss.html#torch.nn.functional.cosine_embedding_loss" title="torch.nn.functional.cosine_embedding_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cosine_embedding_loss</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.nn.CosineEmbeddingLoss.html#torch.nn.CosineEmbeddingLoss" title="torch.nn.CosineEmbeddingLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">CosineEmbeddingLoss</span></code></a> for details.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.cross_entropy"/><a class="reference internal" href="generated/torch.nn.functional.cross_entropy.html#torch.nn.functional.cross_entropy" title="torch.nn.functional.cross_entropy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cross_entropy</span></code></a></p></td>
<td><p>Compute the cross entropy loss between input logits and target.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.ctc_loss"/><a class="reference internal" href="generated/torch.nn.functional.ctc_loss.html#torch.nn.functional.ctc_loss" title="torch.nn.functional.ctc_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ctc_loss</span></code></a></p></td>
<td><p>Apply the Connectionist Temporal Classification loss.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.gaussian_nll_loss"/><a class="reference internal" href="generated/torch.nn.functional.gaussian_nll_loss.html#torch.nn.functional.gaussian_nll_loss" title="torch.nn.functional.gaussian_nll_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gaussian_nll_loss</span></code></a></p></td>
<td><p>Gaussian negative log likelihood loss.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.hinge_embedding_loss"/><a class="reference internal" href="generated/torch.nn.functional.hinge_embedding_loss.html#torch.nn.functional.hinge_embedding_loss" title="torch.nn.functional.hinge_embedding_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hinge_embedding_loss</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.nn.HingeEmbeddingLoss.html#torch.nn.HingeEmbeddingLoss" title="torch.nn.HingeEmbeddingLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">HingeEmbeddingLoss</span></code></a> for details.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.kl_div"/><a class="reference internal" href="generated/torch.nn.functional.kl_div.html#torch.nn.functional.kl_div" title="torch.nn.functional.kl_div"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kl_div</span></code></a></p></td>
<td><p>Compute the KL Divergence loss.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.l1_loss"/><a class="reference internal" href="generated/torch.nn.functional.l1_loss.html#torch.nn.functional.l1_loss" title="torch.nn.functional.l1_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">l1_loss</span></code></a></p></td>
<td><p>Function that takes the mean element-wise absolute value difference.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.mse_loss"/><a class="reference internal" href="generated/torch.nn.functional.mse_loss.html#torch.nn.functional.mse_loss" title="torch.nn.functional.mse_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mse_loss</span></code></a></p></td>
<td><p>Measures the element-wise mean squared error.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.margin_ranking_loss"/><a class="reference internal" href="generated/torch.nn.functional.margin_ranking_loss.html#torch.nn.functional.margin_ranking_loss" title="torch.nn.functional.margin_ranking_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">margin_ranking_loss</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.nn.MarginRankingLoss.html#torch.nn.MarginRankingLoss" title="torch.nn.MarginRankingLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MarginRankingLoss</span></code></a> for details.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.multilabel_margin_loss"/><a class="reference internal" href="generated/torch.nn.functional.multilabel_margin_loss.html#torch.nn.functional.multilabel_margin_loss" title="torch.nn.functional.multilabel_margin_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multilabel_margin_loss</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.nn.MultiLabelMarginLoss.html#torch.nn.MultiLabelMarginLoss" title="torch.nn.MultiLabelMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiLabelMarginLoss</span></code></a> for details.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.multilabel_soft_margin_loss"/><a class="reference internal" href="generated/torch.nn.functional.multilabel_soft_margin_loss.html#torch.nn.functional.multilabel_soft_margin_loss" title="torch.nn.functional.multilabel_soft_margin_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multilabel_soft_margin_loss</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.nn.MultiLabelSoftMarginLoss.html#torch.nn.MultiLabelSoftMarginLoss" title="torch.nn.MultiLabelSoftMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiLabelSoftMarginLoss</span></code></a> for details.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.multi_margin_loss"/><a class="reference internal" href="generated/torch.nn.functional.multi_margin_loss.html#torch.nn.functional.multi_margin_loss" title="torch.nn.functional.multi_margin_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multi_margin_loss</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.nn.MultiMarginLoss.html#torch.nn.MultiMarginLoss" title="torch.nn.MultiMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiMarginLoss</span></code></a> for details.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.nll_loss"/><a class="reference internal" href="generated/torch.nn.functional.nll_loss.html#torch.nn.functional.nll_loss" title="torch.nn.functional.nll_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nll_loss</span></code></a></p></td>
<td><p>Compute the negative log likelihood loss.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.huber_loss"/><a class="reference internal" href="generated/torch.nn.functional.huber_loss.html#torch.nn.functional.huber_loss" title="torch.nn.functional.huber_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">huber_loss</span></code></a></p></td>
<td><p>Compute the Huber loss.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.smooth_l1_loss"/><a class="reference internal" href="generated/torch.nn.functional.smooth_l1_loss.html#torch.nn.functional.smooth_l1_loss" title="torch.nn.functional.smooth_l1_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">smooth_l1_loss</span></code></a></p></td>
<td><p>Compute the Smooth L1 loss.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.soft_margin_loss"/><a class="reference internal" href="generated/torch.nn.functional.soft_margin_loss.html#torch.nn.functional.soft_margin_loss" title="torch.nn.functional.soft_margin_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">soft_margin_loss</span></code></a></p></td>
<td><p>See <a class="reference internal" href="generated/torch.nn.SoftMarginLoss.html#torch.nn.SoftMarginLoss" title="torch.nn.SoftMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">SoftMarginLoss</span></code></a> for details.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.triplet_margin_loss"/><a class="reference internal" href="generated/torch.nn.functional.triplet_margin_loss.html#torch.nn.functional.triplet_margin_loss" title="torch.nn.functional.triplet_margin_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">triplet_margin_loss</span></code></a></p></td>
<td><p>Compute the triplet loss between given input tensors and a margin greater than 0.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.triplet_margin_with_distance_loss"/><a class="reference internal" href="generated/torch.nn.functional.triplet_margin_with_distance_loss.html#torch.nn.functional.triplet_margin_with_distance_loss" title="torch.nn.functional.triplet_margin_with_distance_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">triplet_margin_with_distance_loss</span></code></a></p></td>
<td><p>Compute the triplet margin loss for input tensors using a custom distance function.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="vision-functions">
<h2>Vision functions<a class="headerlink" href="#vision-functions" title="Permalink to this heading">¶</a></h2>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><p id="torch.nn.functional.pixel_shuffle"/><a class="reference internal" href="generated/torch.nn.functional.pixel_shuffle.html#torch.nn.functional.pixel_shuffle" title="torch.nn.functional.pixel_shuffle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pixel_shuffle</span></code></a></p></td>
<td><p>Rearranges elements in a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>C</mi><mo>×</mo><msup><mi>r</mi><mn>2</mn></msup><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, C \times r^2, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.0641em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8141em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span> to a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo>×</mo><mi>r</mi><mo separator="true">,</mo><mi>W</mi><mo>×</mo><mi>r</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, C, H \times r, W \times r)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8778em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mclose">)</span></span></span></span></span>, where r is the <code class="xref py py-attr docutils literal notranslate"><span class="pre">upscale_factor</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.pixel_unshuffle"/><a class="reference internal" href="generated/torch.nn.functional.pixel_unshuffle.html#torch.nn.functional.pixel_unshuffle" title="torch.nn.functional.pixel_unshuffle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pixel_unshuffle</span></code></a></p></td>
<td><p>Reverses the <a class="reference internal" href="generated/torch.nn.PixelShuffle.html#torch.nn.PixelShuffle" title="torch.nn.PixelShuffle"><code class="xref py py-class docutils literal notranslate"><span class="pre">PixelShuffle</span></code></a> operation by rearranging elements in a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo>×</mo><mi>r</mi><mo separator="true">,</mo><mi>W</mi><mo>×</mo><mi>r</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, C, H \times r, W \times r)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8778em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mclose">)</span></span></span></span></span> to a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>C</mi><mo>×</mo><msup><mi>r</mi><mn>2</mn></msup><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, C \times r^2, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.0641em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8141em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>, where r is the <code class="xref py py-attr docutils literal notranslate"><span class="pre">downscale_factor</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.pad"/><a class="reference internal" href="generated/torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pad</span></code></a></p></td>
<td><p>Pads tensor.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.interpolate"/><a class="reference internal" href="generated/torch.nn.functional.interpolate.html#torch.nn.functional.interpolate" title="torch.nn.functional.interpolate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">interpolate</span></code></a></p></td>
<td><p>Down/up samples the input.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.upsample"/><a class="reference internal" href="generated/torch.nn.functional.upsample.html#torch.nn.functional.upsample" title="torch.nn.functional.upsample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">upsample</span></code></a></p></td>
<td><p>Upsample input.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.upsample_nearest"/><a class="reference internal" href="generated/torch.nn.functional.upsample_nearest.html#torch.nn.functional.upsample_nearest" title="torch.nn.functional.upsample_nearest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">upsample_nearest</span></code></a></p></td>
<td><p>Upsamples the input, using nearest neighbours' pixel values.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.upsample_bilinear"/><a class="reference internal" href="generated/torch.nn.functional.upsample_bilinear.html#torch.nn.functional.upsample_bilinear" title="torch.nn.functional.upsample_bilinear"><code class="xref py py-obj docutils literal notranslate"><span class="pre">upsample_bilinear</span></code></a></p></td>
<td><p>Upsamples the input, using bilinear upsampling.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.nn.functional.grid_sample"/><a class="reference internal" href="generated/torch.nn.functional.grid_sample.html#torch.nn.functional.grid_sample" title="torch.nn.functional.grid_sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">grid_sample</span></code></a></p></td>
<td><p>Compute grid sample.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.nn.functional.affine_grid"/><a class="reference internal" href="generated/torch.nn.functional.affine_grid.html#torch.nn.functional.affine_grid" title="torch.nn.functional.affine_grid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">affine_grid</span></code></a></p></td>
<td><p>Generate 2D or 3D flow field (sampling grid), given a batch of affine matrices <code class="xref py py-attr docutils literal notranslate"><span class="pre">theta</span></code>.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="dataparallel-functions-multi-gpu-distributed">
<h2>DataParallel functions (multi-GPU, distributed)<a class="headerlink" href="#dataparallel-functions-multi-gpu-distributed" title="Permalink to this heading">¶</a></h2>
<div class="section" id="data-parallel">
<h3><span class="hidden-section">data_parallel</span><a class="headerlink" href="#data-parallel" title="Permalink to this heading">¶</a></h3>
<table class="autosummary longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.nn.parallel.data_parallel</span></code></p></td>
<td><p>Evaluate module(input) in parallel across the GPUs given in device_ids.</p></td>
</tr>
</tbody>
</table>
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