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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 headline">¶</a></h1>
<div class="section" id="convolution-functions">
<h2>Convolution functions<a class="headerlink" href="#convolution-functions" title="Permalink to this headline">¶</a></h2>
<div class="section" id="conv1d">
<h3><span class="hidden-section">conv1d</span><a class="headerlink" href="#conv1d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.conv1d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">conv1d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">groups=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.conv1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 1D convolution over an input signal composed of several input
planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.Conv1d" title="torch.nn.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a> for details and output shape.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In some circumstances when using the CUDA backend with CuDNN, this operator
may select a nondeterministic algorithm to increase performance. If this is
undesirable, you can try to make the operation deterministic (potentially at
a performance cost) by setting <code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn.deterministic</span> <span class="pre">=</span>
<span class="pre">True</span></code>.
Please see the notes on <a class="reference internal" href="notes/randomness.html"><span class="doc">Reproducibility</span></a> for background.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – input tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>weight</strong> – filters of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo separator="true">,</mo><mfrac><mtext>in_channels</mtext><mtext>groups</mtext></mfrac><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.4942159999999998em;vertical-align:-0.481108em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></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:1.013108em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">groups</span></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:-3.527em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">in_channels</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>bias</strong> – optional bias of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mclose">)</span></span></span></span>
</span>. Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></p></li>
<li><p><strong>stride</strong> – the stride of the convolving kernel. Can be a single number or
a one-element tuple <cite>(sW,)</cite>. Default: 1</p></li>
<li><p><strong>padding</strong> – implicit paddings on both sides of the input. Can be a
single number or a one-element tuple <cite>(padW,)</cite>. Default: 0</p></li>
<li><p><strong>dilation</strong> – the spacing between kernel elements. Can be a single number or
a one-element tuple <cite>(dW,)</cite>. Default: 1</p></li>
<li><p><strong>groups</strong> – split input into groups, <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mtext>in_channels</mtext></mrow><annotation encoding="application/x-tex">\text{in\_channels}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.00444em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">in_channels</span></span></span></span></span>
</span> should be divisible by
the number of groups. Default: 1</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">filters</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">33</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">F</span><span class="o">.</span><span class="n">conv1d</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">filters</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="conv2d">
<h3><span class="hidden-section">conv2d</span><a class="headerlink" href="#conv2d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.conv2d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">conv2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">groups=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.conv2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D convolution over an input image composed of several input
planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.Conv2d" title="torch.nn.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a> for details and output shape.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In some circumstances when using the CUDA backend with CuDNN, this operator
may select a nondeterministic algorithm to increase performance. If this is
undesirable, you can try to make the operation deterministic (potentially at
a performance cost) by setting <code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn.deterministic</span> <span class="pre">=</span>
<span class="pre">True</span></code>.
Please see the notes on <a class="reference internal" href="notes/randomness.html"><span class="doc">Reproducibility</span></a> for background.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – input tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>H</mi><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iH , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>weight</strong> – filters of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo separator="true">,</mo><mfrac><mtext>in_channels</mtext><mtext>groups</mtext></mfrac><mo separator="true">,</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.4942159999999998em;vertical-align:-0.481108em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></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:1.013108em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">groups</span></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:-3.527em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">in_channels</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>bias</strong> – optional bias tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mclose">)</span></span></span></span>
</span>. Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></p></li>
<li><p><strong>stride</strong> – the stride of the convolving kernel. Can be a single number or a
tuple <cite>(sH, sW)</cite>. Default: 1</p></li>
<li><p><strong>padding</strong> – implicit paddings on both sides of the input. Can be a
single number or a tuple <cite>(padH, padW)</cite>. Default: 0</p></li>
<li><p><strong>dilation</strong> – the spacing between kernel elements. Can be a single number or
a tuple <cite>(dH, dW)</cite>. Default: 1</p></li>
<li><p><strong>groups</strong> – split input into groups, <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mtext>in_channels</mtext></mrow><annotation encoding="application/x-tex">\text{in\_channels}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.00444em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">in_channels</span></span></span></span></span>
</span> should be divisible by the
number of groups. Default: 1</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># With square kernels and equal stride</span>
<span class="gp">>>> </span><span class="n">filters</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">F</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="conv3d">
<h3><span class="hidden-section">conv3d</span><a class="headerlink" href="#conv3d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.conv3d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">conv3d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">groups=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.conv3d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 3D convolution over an input image composed of several input
planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.Conv3d" title="torch.nn.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv3d</span></code></a> for details and output shape.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In some circumstances when using the CUDA backend with CuDNN, this operator
may select a nondeterministic algorithm to increase performance. If this is
undesirable, you can try to make the operation deterministic (potentially at
a performance cost) by setting <code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn.deterministic</span> <span class="pre">=</span>
<span class="pre">True</span></code>.
Please see the notes on <a class="reference internal" href="notes/randomness.html"><span class="doc">Reproducibility</span></a> for background.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – input tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>T</mi><mo separator="true">,</mo><mi>i</mi><mi>H</mi><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iT , iH , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>weight</strong> – filters of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo separator="true">,</mo><mfrac><mtext>in_channels</mtext><mtext>groups</mtext></mfrac><mo separator="true">,</mo><mi>k</mi><mi>T</mi><mo separator="true">,</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kT , kH , kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.4942159999999998em;vertical-align:-0.481108em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></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:1.013108em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">groups</span></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:-3.527em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">in_channels</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>bias</strong> – optional bias tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mclose">)</span></span></span></span>
</span>. Default: None</p></li>
<li><p><strong>stride</strong> – the stride of the convolving kernel. Can be a single number or a
tuple <cite>(sT, sH, sW)</cite>. Default: 1</p></li>
<li><p><strong>padding</strong> – implicit paddings on both sides of the input. Can be a
single number or a tuple <cite>(padT, padH, padW)</cite>. Default: 0</p></li>
<li><p><strong>dilation</strong> – the spacing between kernel elements. Can be a single number or
a tuple <cite>(dT, dH, dW)</cite>. Default: 1</p></li>
<li><p><strong>groups</strong> – split input into groups, <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mtext>in_channels</mtext></mrow><annotation encoding="application/x-tex">\text{in\_channels}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.00444em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">in_channels</span></span></span></span></span>
</span> should be divisible by
the number of groups. Default: 1</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">filters</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">33</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">F</span><span class="o">.</span><span class="n">conv3d</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">filters</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="conv-transpose1d">
<h3><span class="hidden-section">conv_transpose1d</span><a class="headerlink" href="#conv-transpose1d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.conv_transpose1d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">conv_transpose1d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">output_padding=0</em>, <em class="sig-param">groups=1</em>, <em class="sig-param">dilation=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.conv_transpose1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 1D transposed convolution operator over an input signal
composed of several input planes, sometimes also called “deconvolution”.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.ConvTranspose1d" title="torch.nn.ConvTranspose1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose1d</span></code></a> for details and output shape.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In some circumstances when using the CUDA backend with CuDNN, this operator
may select a nondeterministic algorithm to increase performance. If this is
undesirable, you can try to make the operation deterministic (potentially at
a performance cost) by setting <code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn.deterministic</span> <span class="pre">=</span>
<span class="pre">True</span></code>.
Please see the notes on <a class="reference internal" href="notes/randomness.html"><span class="doc">Reproducibility</span></a> for background.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – input tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>weight</strong> – filters of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mfrac><mtext>out_channels</mtext><mtext>groups</mtext></mfrac><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.4942159999999998em;vertical-align:-0.481108em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></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:1.013108em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">groups</span></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:-3.527em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">out_channels</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>bias</strong> – optional bias of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mclose">)</span></span></span></span>
</span>. Default: None</p></li>
<li><p><strong>stride</strong> – the stride of the convolving kernel. Can be a single number or a
tuple <code class="docutils literal notranslate"><span class="pre">(sW,)</span></code>. Default: 1</p></li>
<li><p><strong>padding</strong> – <code class="docutils literal notranslate"><span class="pre">dilation</span> <span class="pre">*</span> <span class="pre">(kernel_size</span> <span class="pre">-</span> <span class="pre">1)</span> <span class="pre">-</span> <span class="pre">padding</span></code> zero-padding will be added to both
sides of each dimension in the input. Can be a single number or a tuple
<code class="docutils literal notranslate"><span class="pre">(padW,)</span></code>. Default: 0</p></li>
<li><p><strong>output_padding</strong> – additional size added to one side of each dimension in the
output shape. Can be a single number or a tuple <code class="docutils literal notranslate"><span class="pre">(out_padW)</span></code>. Default: 0</p></li>
<li><p><strong>groups</strong> – split input into groups, <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mtext>in_channels</mtext></mrow><annotation encoding="application/x-tex">\text{in\_channels}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.00444em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">in_channels</span></span></span></span></span>
</span> should be divisible by the
number of groups. Default: 1</p></li>
<li><p><strong>dilation</strong> – the spacing between kernel elements. Can be a single number or
a tuple <code class="docutils literal notranslate"><span class="pre">(dW,)</span></code>. Default: 1</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">33</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">F</span><span class="o">.</span><span class="n">conv_transpose1d</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="conv-transpose2d">
<h3><span class="hidden-section">conv_transpose2d</span><a class="headerlink" href="#conv-transpose2d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.conv_transpose2d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">conv_transpose2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">output_padding=0</em>, <em class="sig-param">groups=1</em>, <em class="sig-param">dilation=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.conv_transpose2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D transposed convolution operator over an input image
composed of several input planes, sometimes also called “deconvolution”.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.ConvTranspose2d" title="torch.nn.ConvTranspose2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose2d</span></code></a> for details and output shape.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In some circumstances when using the CUDA backend with CuDNN, this operator
may select a nondeterministic algorithm to increase performance. If this is
undesirable, you can try to make the operation deterministic (potentially at
a performance cost) by setting <code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn.deterministic</span> <span class="pre">=</span>
<span class="pre">True</span></code>.
Please see the notes on <a class="reference internal" href="notes/randomness.html"><span class="doc">Reproducibility</span></a> for background.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – input tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>H</mi><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iH , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>weight</strong> – filters of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mfrac><mtext>out_channels</mtext><mtext>groups</mtext></mfrac><mo separator="true">,</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kH , kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.4942159999999998em;vertical-align:-0.481108em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></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:1.013108em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">groups</span></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:-3.527em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">out_channels</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>bias</strong> – optional bias of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mclose">)</span></span></span></span>
</span>. Default: None</p></li>
<li><p><strong>stride</strong> – the stride of the convolving kernel. Can be a single number or a
tuple <code class="docutils literal notranslate"><span class="pre">(sH,</span> <span class="pre">sW)</span></code>. Default: 1</p></li>
<li><p><strong>padding</strong> – <code class="docutils literal notranslate"><span class="pre">dilation</span> <span class="pre">*</span> <span class="pre">(kernel_size</span> <span class="pre">-</span> <span class="pre">1)</span> <span class="pre">-</span> <span class="pre">padding</span></code> zero-padding will be added to both
sides of each dimension in the input. Can be a single number or a tuple
<code class="docutils literal notranslate"><span class="pre">(padH,</span> <span class="pre">padW)</span></code>. Default: 0</p></li>
<li><p><strong>output_padding</strong> – additional size added to one side of each dimension in the
output shape. Can be a single number or a tuple <code class="docutils literal notranslate"><span class="pre">(out_padH,</span> <span class="pre">out_padW)</span></code>.
Default: 0</p></li>
<li><p><strong>groups</strong> – split input into groups, <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mtext>in_channels</mtext></mrow><annotation encoding="application/x-tex">\text{in\_channels}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.00444em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">in_channels</span></span></span></span></span>
</span> should be divisible by the
number of groups. Default: 1</p></li>
<li><p><strong>dilation</strong> – the spacing between kernel elements. Can be a single number or
a tuple <code class="docutils literal notranslate"><span class="pre">(dH,</span> <span class="pre">dW)</span></code>. Default: 1</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># With square kernels and equal stride</span>
<span class="gp">>>> </span><span class="n">inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">F</span><span class="o">.</span><span class="n">conv_transpose2d</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">weights</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="conv-transpose3d">
<h3><span class="hidden-section">conv_transpose3d</span><a class="headerlink" href="#conv-transpose3d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.conv_transpose3d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">conv_transpose3d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">output_padding=0</em>, <em class="sig-param">groups=1</em>, <em class="sig-param">dilation=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.conv_transpose3d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 3D transposed convolution operator over an input image
composed of several input planes, sometimes also called “deconvolution”</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.ConvTranspose3d" title="torch.nn.ConvTranspose3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose3d</span></code></a> for details and output shape.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In some circumstances when using the CUDA backend with CuDNN, this operator
may select a nondeterministic algorithm to increase performance. If this is
undesirable, you can try to make the operation deterministic (potentially at
a performance cost) by setting <code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn.deterministic</span> <span class="pre">=</span>
<span class="pre">True</span></code>.
Please see the notes on <a class="reference internal" href="notes/randomness.html"><span class="doc">Reproducibility</span></a> for background.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – input tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>T</mi><mo separator="true">,</mo><mi>i</mi><mi>H</mi><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iT , iH , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>weight</strong> – filters of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mfrac><mtext>out_channels</mtext><mtext>groups</mtext></mfrac><mo separator="true">,</mo><mi>k</mi><mi>T</mi><mo separator="true">,</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{in\_channels} , \frac{\text{out\_channels}}{\text{groups}} , kT , kH , kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.4942159999999998em;vertical-align:-0.481108em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></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:1.013108em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">groups</span></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:-3.527em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">out_channels</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>bias</strong> – optional bias of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>out_channels</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{out\_channels})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">out_channels</span></span><span class="mclose">)</span></span></span></span>
</span>. Default: None</p></li>
<li><p><strong>stride</strong> – the stride of the convolving kernel. Can be a single number or a
tuple <code class="docutils literal notranslate"><span class="pre">(sT,</span> <span class="pre">sH,</span> <span class="pre">sW)</span></code>. Default: 1</p></li>
<li><p><strong>padding</strong> – <code class="docutils literal notranslate"><span class="pre">dilation</span> <span class="pre">*</span> <span class="pre">(kernel_size</span> <span class="pre">-</span> <span class="pre">1)</span> <span class="pre">-</span> <span class="pre">padding</span></code> zero-padding will be added to both
sides of each dimension in the input. Can be a single number or a tuple
<code class="docutils literal notranslate"><span class="pre">(padT,</span> <span class="pre">padH,</span> <span class="pre">padW)</span></code>. Default: 0</p></li>
<li><p><strong>output_padding</strong> – additional size added to one side of each dimension in the
output shape. Can be a single number or a tuple
<code class="docutils literal notranslate"><span class="pre">(out_padT,</span> <span class="pre">out_padH,</span> <span class="pre">out_padW)</span></code>. Default: 0</p></li>
<li><p><strong>groups</strong> – split input into groups, <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mtext>in_channels</mtext></mrow><annotation encoding="application/x-tex">\text{in\_channels}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.00444em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">in_channels</span></span></span></span></span>
</span> should be divisible by the
number of groups. Default: 1</p></li>
<li><p><strong>dilation</strong> – the spacing between kernel elements. Can be a single number or
a tuple <cite>(dT, dH, dW)</cite>. Default: 1</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">50</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">33</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">F</span><span class="o">.</span><span class="n">conv_transpose3d</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">weights</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="unfold">
<h3><span class="hidden-section">unfold</span><a class="headerlink" href="#unfold" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.unfold">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">unfold</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">stride=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/functional.html#unfold"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.unfold" title="Permalink to this definition">¶</a></dt>
<dd><p>Extracts sliding local blocks from an batched input tensor.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Currently, only 4-D input tensors (batched image-like tensors) are
supported.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>More than one element of the unfolded tensor may refer to a single
memory location. As a result, in-place operations (especially ones that
are vectorized) may result in incorrect behavior. If you need to write
to the tensor, please clone it first.</p>
</div>
<p>See <a class="reference internal" href="nn.html#torch.nn.Unfold" title="torch.nn.Unfold"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Unfold</span></code></a> for details</p>
</dd></dl>
</div>
<div class="section" id="fold">
<h3><span class="hidden-section">fold</span><a class="headerlink" href="#fold" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.fold">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">fold</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">output_size</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">stride=1</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/functional.html#fold"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.fold" title="Permalink to this definition">¶</a></dt>
<dd><p>Combines an array of sliding local blocks into a large containing
tensor.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Currently, only 4-D output tensors (batched image-like tensors) are
supported.</p>
</div>
<p>See <a class="reference internal" href="nn.html#torch.nn.Fold" title="torch.nn.Fold"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Fold</span></code></a> for details</p>
</dd></dl>
</div>
</div>
<div class="section" id="pooling-functions">
<h2>Pooling functions<a class="headerlink" href="#pooling-functions" title="Permalink to this headline">¶</a></h2>
<div class="section" id="avg-pool1d">
<h3><span class="hidden-section">avg_pool1d</span><a class="headerlink" href="#avg-pool1d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.avg_pool1d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">avg_pool1d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=None</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">ceil_mode=False</em>, <em class="sig-param">count_include_pad=True</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.avg_pool1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 1D average pooling over an input signal composed of several
input planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.AvgPool1d" title="torch.nn.AvgPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AvgPool1d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – input tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>kernel_size</strong> – the size of the window. Can be a single number or a
tuple <cite>(kW,)</cite></p></li>
<li><p><strong>stride</strong> – the stride of the window. Can be a single number or a tuple
<cite>(sW,)</cite>. Default: <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code></p></li>
<li><p><strong>padding</strong> – implicit zero paddings on both sides of the input. Can be a
single number or a tuple <cite>(padW,)</cite>. Default: 0</p></li>
<li><p><strong>ceil_mode</strong> – when True, will use <cite>ceil</cite> instead of <cite>floor</cite> to compute the
output shape. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
<li><p><strong>count_include_pad</strong> – when True, will include the zero-padding in the
averaging calculation. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># pool of square window of size=3, stride=2</span>
<span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">F</span><span class="o">.</span><span class="n">avg_pool1d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="go">tensor([[[ 2., 4., 6.]]])</span>
</pre></div>
</div>
</dd></dl>
</div>
<div class="section" id="avg-pool2d">
<h3><span class="hidden-section">avg_pool2d</span><a class="headerlink" href="#avg-pool2d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.avg_pool2d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">avg_pool2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=None</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">ceil_mode=False</em>, <em class="sig-param">count_include_pad=True</em>, <em class="sig-param">divisor_override=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.avg_pool2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies 2D average-pooling operation in <span class="math"><span class="katex"><span class="katex-mathml"><math><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:0.77777em;vertical-align:-0.08333em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span></span></span></span>
</span> regions by step size
<span class="math"><span class="katex"><span class="katex-mathml"><math><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:0.76666em;vertical-align:-0.08333em;"></span><span class="mord mathdefault">s</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathdefault">s</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span></span></span></span>
</span> steps. The number of output features is equal to the number of
input planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.AvgPool2d" title="torch.nn.AvgPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AvgPool2d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – input tensor <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>H</mi><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iH , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>kernel_size</strong> – size of the pooling region. Can be a single number or a
tuple <cite>(kH, kW)</cite></p></li>
<li><p><strong>stride</strong> – stride of the pooling operation. Can be a single number or a
tuple <cite>(sH, sW)</cite>. Default: <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code></p></li>
<li><p><strong>padding</strong> – implicit zero paddings on both sides of the input. Can be a
single number or a tuple <cite>(padH, padW)</cite>. Default: 0</p></li>
<li><p><strong>ceil_mode</strong> – when True, will use <cite>ceil</cite> instead of <cite>floor</cite> in the formula
to compute the output shape. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
<li><p><strong>count_include_pad</strong> – when True, will include the zero-padding in the
averaging calculation. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
<li><p><strong>divisor_override</strong> – if specified, it will be used as divisor, otherwise
size of the pooling region will be used. Default: None</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="avg-pool3d">
<h3><span class="hidden-section">avg_pool3d</span><a class="headerlink" href="#avg-pool3d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.avg_pool3d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">avg_pool3d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=None</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">ceil_mode=False</em>, <em class="sig-param">count_include_pad=True</em>, <em class="sig-param">divisor_override=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.avg_pool3d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies 3D average-pooling operation in <span class="math"><span class="katex"><span class="katex-mathml"><math><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:0.77777em;vertical-align:-0.08333em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.77777em;vertical-align:-0.08333em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord mathdefault" style="margin-right:0.03148em;">k</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span></span></span></span>
</span> regions by step
size <span class="math"><span class="katex"><span class="katex-mathml"><math><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:0.76666em;vertical-align:-0.08333em;"></span><span class="mord mathdefault">s</span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.76666em;vertical-align:-0.08333em;"></span><span class="mord mathdefault">s</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathdefault">s</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span></span></span></span>
</span> steps. The number of output features is equal to
<span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">⌊</mo><mfrac><mtext>input planes</mtext><mrow><mi>s</mi><mi>T</mi></mrow></mfrac><mo stretchy="false">⌋</mo></mrow><annotation encoding="application/x-tex">\lfloor\frac{\text{input planes}}{sT}\rfloor</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.277216em;vertical-align:-0.345em;"></span><span class="mopen">⌊</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:0.9322159999999999em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathdefault mtight">s</span><span class="mord mathdefault mtight" style="margin-right:0.13889em;">T</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:-3.446108em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">input planes</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.345em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mclose">⌋</span></span></span></span>
</span>.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.AvgPool3d" title="torch.nn.AvgPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AvgPool3d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – input tensor <span class="math"><span class="katex"><span class="katex-mathml"><math><semantics><mrow><mo stretchy="false">(</mo><mtext>minibatch</mtext><mo separator="true">,</mo><mtext>in_channels</mtext><mo separator="true">,</mo><mi>i</mi><mi>T</mi><mo>×</mo><mi>i</mi><mi>H</mi><mo separator="true">,</mo><mi>i</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(\text{minibatch} , \text{in\_channels} , iT \times iH , iW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mopen">(</span><span class="mord text"><span class="mord">minibatch</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord text"><span class="mord">in_channels</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">i</span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p></li>
<li><p><strong>kernel_size</strong> – size of the pooling region. Can be a single number or a
tuple <cite>(kT, kH, kW)</cite></p></li>
<li><p><strong>stride</strong> – stride of the pooling operation. Can be a single number or a
tuple <cite>(sT, sH, sW)</cite>. Default: <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code></p></li>
<li><p><strong>padding</strong> – implicit zero paddings on both sides of the input. Can be a
single number or a tuple <cite>(padT, padH, padW)</cite>, Default: 0</p></li>
<li><p><strong>ceil_mode</strong> – when True, will use <cite>ceil</cite> instead of <cite>floor</cite> in the formula
to compute the output shape</p></li>
<li><p><strong>count_include_pad</strong> – when True, will include the zero-padding in the
averaging calculation</p></li>
<li><p><strong>divisor_override</strong> – if specified, it will be used as divisor, otherwise
size of the pooling region will be used. Default: None</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="max-pool1d">
<h3><span class="hidden-section">max_pool1d</span><a class="headerlink" href="#max-pool1d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.max_pool1d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">max_pool1d</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.nn.functional.max_pool1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 1D max pooling over an input signal composed of several input
planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.MaxPool1d" title="torch.nn.MaxPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a> for details.</p>
</dd></dl>
</div>
<div class="section" id="max-pool2d">
<h3><span class="hidden-section">max_pool2d</span><a class="headerlink" href="#max-pool2d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.max_pool2d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">max_pool2d</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.nn.functional.max_pool2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D max pooling over an input signal composed of several input
planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.MaxPool2d" title="torch.nn.MaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a> for details.</p>
</dd></dl>
</div>
<div class="section" id="max-pool3d">
<h3><span class="hidden-section">max_pool3d</span><a class="headerlink" href="#max-pool3d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.max_pool3d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">max_pool3d</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.nn.functional.max_pool3d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 3D max pooling over an input signal composed of several input
planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.MaxPool3d" title="torch.nn.MaxPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code></a> for details.</p>
</dd></dl>
</div>
<div class="section" id="max-unpool1d">
<h3><span class="hidden-section">max_unpool1d</span><a class="headerlink" href="#max-unpool1d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.max_unpool1d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">max_unpool1d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">indices</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=None</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">output_size=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/functional.html#max_unpool1d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.max_unpool1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code>.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.MaxUnpool1d" title="torch.nn.MaxUnpool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool1d</span></code></a> for details.</p>
</dd></dl>
</div>
<div class="section" id="max-unpool2d">
<h3><span class="hidden-section">max_unpool2d</span><a class="headerlink" href="#max-unpool2d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.max_unpool2d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">max_unpool2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">indices</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=None</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">output_size=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/functional.html#max_unpool2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.max_unpool2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code>.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.MaxUnpool2d" title="torch.nn.MaxUnpool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool2d</span></code></a> for details.</p>
</dd></dl>
</div>
<div class="section" id="max-unpool3d">
<h3><span class="hidden-section">max_unpool3d</span><a class="headerlink" href="#max-unpool3d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.max_unpool3d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">max_unpool3d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">indices</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=None</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">output_size=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/functional.html#max_unpool3d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.max_unpool3d" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code>.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.MaxUnpool3d" title="torch.nn.MaxUnpool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool3d</span></code></a> for details.</p>
</dd></dl>
</div>
<div class="section" id="lp-pool1d">
<h3><span class="hidden-section">lp_pool1d</span><a class="headerlink" href="#lp-pool1d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.lp_pool1d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">lp_pool1d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">norm_type</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=None</em>, <em class="sig-param">ceil_mode=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/functional.html#lp_pool1d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.lp_pool1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 1D power-average pooling over an input signal composed of
several input planes. If the sum of all inputs to the power of <cite>p</cite> is
zero, the gradient is set to zero as well.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.LPPool1d" title="torch.nn.LPPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">LPPool1d</span></code></a> for details.</p>
</dd></dl>
</div>
<div class="section" id="lp-pool2d">
<h3><span class="hidden-section">lp_pool2d</span><a class="headerlink" href="#lp-pool2d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.lp_pool2d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">lp_pool2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">norm_type</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=None</em>, <em class="sig-param">ceil_mode=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/functional.html#lp_pool2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.lp_pool2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D power-average pooling over an input signal composed of
several input planes. If the sum of all inputs to the power of <cite>p</cite> is
zero, the gradient is set to zero as well.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.LPPool2d" title="torch.nn.LPPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">LPPool2d</span></code></a> for details.</p>
</dd></dl>
</div>
<div class="section" id="adaptive-max-pool1d">
<h3><span class="hidden-section">adaptive_max_pool1d</span><a class="headerlink" href="#adaptive-max-pool1d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.adaptive_max_pool1d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">adaptive_max_pool1d</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 1D adaptive max pooling over an input signal composed of
several input planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.AdaptiveMaxPool1d" title="torch.nn.AdaptiveMaxPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveMaxPool1d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>output_size</strong> – the target output size (single integer)</p></li>
<li><p><strong>return_indices</strong> – whether to return pooling indices. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="adaptive-max-pool2d">
<h3><span class="hidden-section">adaptive_max_pool2d</span><a class="headerlink" href="#adaptive-max-pool2d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.adaptive_max_pool2d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">adaptive_max_pool2d</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D adaptive max pooling over an input signal composed of
several input planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.AdaptiveMaxPool2d" title="torch.nn.AdaptiveMaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveMaxPool2d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>output_size</strong> – the target output size (single integer or
double-integer tuple)</p></li>
<li><p><strong>return_indices</strong> – whether to return pooling indices. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="adaptive-max-pool3d">
<h3><span class="hidden-section">adaptive_max_pool3d</span><a class="headerlink" href="#adaptive-max-pool3d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.adaptive_max_pool3d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">adaptive_max_pool3d</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool3d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 3D adaptive max pooling over an input signal composed of
several input planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.AdaptiveMaxPool3d" title="torch.nn.AdaptiveMaxPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveMaxPool3d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>output_size</strong> – the target output size (single integer or
triple-integer tuple)</p></li>
<li><p><strong>return_indices</strong> – whether to return pooling indices. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="adaptive-avg-pool1d">
<h3><span class="hidden-section">adaptive_avg_pool1d</span><a class="headerlink" href="#adaptive-avg-pool1d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.adaptive_avg_pool1d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">adaptive_avg_pool1d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">output_size</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool1d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 1D adaptive average pooling over an input signal composed of
several input planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.AdaptiveAvgPool1d" title="torch.nn.AdaptiveAvgPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveAvgPool1d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>output_size</strong> – the target output size (single integer)</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="adaptive-avg-pool2d">
<h3><span class="hidden-section">adaptive_avg_pool2d</span><a class="headerlink" href="#adaptive-avg-pool2d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.adaptive_avg_pool2d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">adaptive_avg_pool2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">output_size</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/functional.html#adaptive_avg_pool2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 2D adaptive average pooling over an input signal composed of
several input planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.AdaptiveAvgPool2d" title="torch.nn.AdaptiveAvgPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveAvgPool2d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>output_size</strong> – the target output size (single integer or
double-integer tuple)</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="adaptive-avg-pool3d">
<h3><span class="hidden-section">adaptive_avg_pool3d</span><a class="headerlink" href="#adaptive-avg-pool3d" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.adaptive_avg_pool3d">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">adaptive_avg_pool3d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">output_size</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/functional.html#adaptive_avg_pool3d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool3d" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies a 3D adaptive average pooling over an input signal composed of
several input planes.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.AdaptiveAvgPool3d" title="torch.nn.AdaptiveAvgPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveAvgPool3d</span></code></a> for details and output shape.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>output_size</strong> – the target output size (single integer or
triple-integer tuple)</p>
</dd>
</dl>
</dd></dl>
</div>
</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 headline">¶</a></h2>
<div class="section" id="threshold">
<h3><span class="hidden-section">threshold</span><a class="headerlink" href="#threshold" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.threshold">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">threshold</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">threshold</em>, <em class="sig-param">value</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/functional.html#threshold"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.threshold" title="Permalink to this definition">¶</a></dt>
<dd><p>Thresholds each element of the input Tensor.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.Threshold" title="torch.nn.Threshold"><code class="xref py py-class docutils literal notranslate"><span class="pre">Threshold</span></code></a> for more details.</p>
</dd></dl>
<dl class="function">
<dt id="torch.nn.functional.threshold_">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">threshold_</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">threshold</em>, <em class="sig-param">value</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.threshold_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#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>
</dd></dl>
</div>
<div class="section" id="relu">
<h3><span class="hidden-section">relu</span><a class="headerlink" href="#relu" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.relu">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">relu</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span> → Tensor<a class="reference internal" href="_modules/torch/nn/functional.html#relu"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.relu" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies the rectified linear unit function element-wise. See
<a class="reference internal" href="nn.html#torch.nn.ReLU" title="torch.nn.ReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">ReLU</span></code></a> for more details.</p>
</dd></dl>
<dl class="function">
<dt id="torch.nn.functional.relu_">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">relu_</code><span class="sig-paren">(</span><em class="sig-param">input</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.relu_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#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>
</dd></dl>
</div>
<div class="section" id="hardtanh">
<h3><span class="hidden-section">hardtanh</span><a class="headerlink" href="#hardtanh" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.hardtanh">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">hardtanh</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">min_val=-1.</em>, <em class="sig-param">max_val=1.</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span> → Tensor<a class="reference internal" href="_modules/torch/nn/functional.html#hardtanh"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.hardtanh" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies the HardTanh function element-wise. See <a class="reference internal" href="nn.html#torch.nn.Hardtanh" title="torch.nn.Hardtanh"><code class="xref py py-class docutils literal notranslate"><span class="pre">Hardtanh</span></code></a> for more
details.</p>
</dd></dl>
<dl class="function">
<dt id="torch.nn.functional.hardtanh_">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">hardtanh_</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">min_val=-1.</em>, <em class="sig-param">max_val=1.</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.nn.functional.hardtanh_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#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>
</dd></dl>
</div>
<div class="section" id="relu6">
<h3><span class="hidden-section">relu6</span><a class="headerlink" href="#relu6" title="Permalink to this headline">¶</a></h3>
<dl class="function">
<dt id="torch.nn.functional.relu6">
<code class="sig-prename descclassname">torch.nn.functional.</code><code class="sig-name descname">relu6</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span> → Tensor<a class="reference internal" href="_modules/torch/nn/functional.html#relu6"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.functional.relu6" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies the element-wise function <span class="math"><span class="katex"><span class="katex-mathml"><math><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 mathdefault">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height: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:0.16666666666666666em;"></span><span class="mord mathdefault">x</span><span class="mclose">)</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">6</span><span class="mclose">)</span></span></span></span>
</span>.</p>
<p>See <a class="reference internal" href="nn.html#torch.nn.ReLU6" title="torch.nn.ReLU6"><code class="xref py py-class docutils literal notranslate"><span class="pre">ReLU6</span></code></a> for more details.</p>
</dd></dl>