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<div class="section" id="torch-nanmean">
<h1>torch.nanmean<a class="headerlink" href="#torch-nanmean" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.nanmean">
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">nanmean</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><a class="headerlink" href="#torch.nanmean" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the mean of all <cite>non-NaN</cite> elements along the specified dimensions.</p>
<p>This function is identical to <a class="reference internal" href="torch.mean.html#torch.mean" title="torch.mean"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mean()</span></code></a> when there are no <cite>NaN</cite> values
in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor. In the presence of <cite>NaN</cite>, <a class="reference internal" href="torch.mean.html#torch.mean" title="torch.mean"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mean()</span></code></a> will
propagate the <cite>NaN</cite> to the output whereas <a class="reference internal" href="#torch.nanmean" title="torch.nanmean"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nanmean()</span></code></a> will ignore the
<cite>NaN</cite> values (<cite>torch.nanmean(a)</cite> is equivalent to <cite>torch.mean(a[~a.isnan()])</cite>).</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">keepdim</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, the output tensor is of the same size
as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> except in the dimension(s) <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> where it is of size 1.
Otherwise, <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is squeezed (see <a class="reference internal" href="torch.squeeze.html#torch.squeeze" title="torch.squeeze"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.squeeze()</span></code></a>), resulting in the
output tensor having 1 (or <code class="docutils literal notranslate"><span class="pre">len(dim)</span></code>) fewer dimension(s).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input tensor.</p></li>
<li><p><strong>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a><em> or </em><em>tuple of python:ints</em>) – the dimension or dimensions to reduce. If <cite>None</cite>, reduces all dimensions. Default is <cite>None</cite>.</p></li>
<li><p><strong>keepdim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – whether the output tensor has <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> retained or not.</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>dtype</strong> (<a class="reference internal" href="../tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
If specified, the input tensor is casted to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> before the operation
is performed. This is useful for preventing data type overflows. Default: None.</p></li>
<li><p><strong>out</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor.</p></li>
</ul>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<p><a class="reference internal" href="torch.mean.html#torch.mean" title="torch.mean"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.mean()</span></code></a> computes the mean value, propagating <cite>NaN</cite>.</p>
</div>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</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="n">torch</span><span class="o">.</span><span class="n">nan</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="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="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="go">tensor(nan)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">nanmean</span><span class="p">()</span>
<span class="go">tensor(1.8000)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">tensor([ nan, 1.5000, 2.5000])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">tensor([1.0000, 1.5000, 2.5000])</span>
<span class="go"># If all elements in the reduced dimensions are NaN then the result is NaN</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">torch</span><span class="o">.</span><span class="n">nan</span><span class="p">])</span><span class="o">.</span><span class="n">nanmean</span><span class="p">()</span>
<span class="go">tensor(nan)</span>
</pre></div>
</div>
</dd></dl>
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