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<div class="section" id="torch-histogram">
<h1>torch.histogram<a class="headerlink" href="#torch-histogram" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.histogram">
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">histogram</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">bins</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">range</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">weight</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">density</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="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="headerlink" href="#torch.histogram" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes a histogram of the values in a tensor.</p>
<p><code class="xref py py-attr docutils literal notranslate"><span class="pre">bins</span></code> can be an integer or a 1D tensor.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">bins</span></code> is an int, it specifies the number of equal-width bins.
By default, the lower and upper range of the bins is determined by the
minimum and maximum elements of the input tensor. The <a class="reference internal" href="torch.range.html#torch.range" title="torch.range"><code class="xref py py-attr docutils literal notranslate"><span class="pre">range</span></code></a>
argument can be provided to specify a range for the bins.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">bins</span></code> is a 1D tensor, it specifies the sequence of bin edges
including the rightmost edge. It should contain at least 2 elements
and its elements should be increasing.</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>bins</strong> – int or 1D Tensor. If int, defines the number of equal-width bins. If tensor,
defines the sequence of bin edges including the rightmost edge.</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>range</strong> (<em>tuple of python:float</em>) – Defines the range of the bins.</p></li>
<li><p><strong>weight</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – If provided, weight should have the same shape as input. Each value in
input contributes its associated weight towards its bin’s result.</p></li>
<li><p><strong>density</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><em>bool</em></a>) – If False, the result will contain the count (or total weight) in each bin.
If True, the result is the value of the probability density function over the bins,
normalized such that the integral over the range of the bins is 1.</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. (tuple, optional): The result tuple of two output tensors (hist, bin_edges).</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>1D Tensor containing the values of the histogram.
bin_edges(Tensor): 1D Tensor containing the edges of the histogram bins.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>hist (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a>)</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span> <span class="n">bins</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">),</span> <span class="n">weight</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="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]))</span>
<span class="go">(tensor([ 0., 5., 2., 0.]), tensor([0., 0.75, 1.5, 2.25, 3.]))</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mf">1.</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span> <span class="n">bins</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">3.</span><span class="p">),</span> <span class="n">weight</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="mf">1.</span><span class="p">,</span> <span class="mf">2.</span><span class="p">,</span> <span class="mf">4.</span><span class="p">]),</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">(tensor([ 0., 0.9524, 0.3810, 0.]), tensor([0., 0.75, 1.5, 2.25, 3.]))</span>
</pre></div>
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