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<div class="section" id="torch-bernoulli">
<h1>torch.bernoulli<a class="headerlink" href="#torch-bernoulli" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.bernoulli">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">bernoulli</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">*</em>, <em class="sig-param">generator=None</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.bernoulli" title="Permalink to this definition">¶</a></dt>
<dd><p>Draws binary random numbers (0 or 1) from a Bernoulli distribution.</p>
<p>The <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor should be a tensor containing probabilities
to be used for drawing the binary random number.
Hence, all values in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> have to be in the range:
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>0</mn><mo>≤</mo><msub><mtext>input</mtext><mi>i</mi></msub><mo>≤</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">0 \leq \text{input}_i \leq 1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.78041em;vertical-align:-0.13597em;"></span><span class="mord">0</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:0.912em;vertical-align:-0.24414em;"></span><span class="mord"><span class="mord text"><span class="mord">input</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.21752399999999997em;"><span style="top:-2.4558600000000004em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.24414em;"><span></span></span></span></span></span></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:0.64444em;vertical-align:0em;"></span><span class="mord">1</span></span></span></span>
</span>.</p>
<p>The <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mtext>i</mtext><mrow><mi>t</mi><mi>h</mi></mrow></msup></mrow><annotation encoding="application/x-tex">\text{i}^{th}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.906868em;vertical-align:0em;"></span><span class="mord"><span class="mord text"><span class="mord">i</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.906868em;"><span style="top:-3.12076em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">t</span><span class="mord mathnormal mtight">h</span></span></span></span></span></span></span></span></span></span></span></span>
</span> element of the output tensor will draw a
value <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>1</mn></mrow><annotation encoding="application/x-tex">1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">1</span></span></span></span>
</span> according to the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mtext>i</mtext><mrow><mi>t</mi><mi>h</mi></mrow></msup></mrow><annotation encoding="application/x-tex">\text{i}^{th}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.906868em;vertical-align:0em;"></span><span class="mord"><span class="mord text"><span class="mord">i</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.906868em;"><span style="top:-3.12076em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">t</span><span class="mord mathnormal mtight">h</span></span></span></span></span></span></span></span></span></span></span></span>
</span> probability value given
in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p>
<div class="math">
<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mtext>out</mtext><mi>i</mi></msub><mo>∼</mo><mrow><mi mathvariant="normal">B</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">n</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">u</mi><mi mathvariant="normal">l</mi><mi mathvariant="normal">l</mi><mi mathvariant="normal">i</mi></mrow><mo stretchy="false">(</mo><mi>p</mi><mo>=</mo><msub><mtext>input</mtext><mi>i</mi></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i})
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.76508em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord text"><span class="mord">out</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.31166399999999994em;"><span style="top:-2.5500000000000003em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">i</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></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="mord"><span class="mord mathrm">B</span><span class="mord mathrm">e</span><span class="mord mathrm">r</span><span class="mord mathrm">n</span><span class="mord mathrm">o</span><span class="mord mathrm">u</span><span class="mord mathrm">l</span><span class="mord mathrm">l</span><span class="mord mathrm">i</span></span><span class="mopen">(</span><span class="mord mathnormal">p</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="mord"><span class="mord text"><span class="mord">input</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.21752399999999997em;"><span style="top:-2.4558600000000004em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">i</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.24414em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>
</div><p>The returned <code class="xref py py-attr docutils literal notranslate"><span class="pre">out</span></code> tensor only has values 0 or 1 and is of the same
shape as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p>
<p><code class="xref py py-attr docutils literal notranslate"><span class="pre">out</span></code> can have integral <code class="docutils literal notranslate"><span class="pre">dtype</span></code>, but <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> must have floating
point <code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</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 of probability values for the Bernoulli distribution</p></li>
<li><p><strong>generator</strong> (<a class="reference internal" href="torch.Generator.html#torch.Generator" title="torch.Generator"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Generator</span></code></a>, optional) – a pseudorandom number generator for sampling</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>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</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="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># generate a uniform random matrix with range [0, 1]</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[ 0.1737, 0.0950, 0.3609],</span>
<span class="go"> [ 0.7148, 0.0289, 0.2676],</span>
<span class="go"> [ 0.9456, 0.8937, 0.7202]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">bernoulli</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor([[ 1., 0., 0.],</span>
<span class="go"> [ 0., 0., 0.],</span>
<span class="go"> [ 1., 1., 1.]])</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</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="c1"># probability of drawing "1" is 1</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">bernoulli</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor([[ 1., 1., 1.],</span>
<span class="go"> [ 1., 1., 1.],</span>
<span class="go"> [ 1., 1., 1.]])</span>
<span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</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="c1"># probability of drawing "1" is 0</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">bernoulli</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor([[ 0., 0., 0.],</span>
<span class="go"> [ 0., 0., 0.],</span>
<span class="go"> [ 0., 0., 0.]])</span>
</pre></div>
</div>
</dd></dl>
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