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<div class="section" id="torch-normal">
<h1>torch.normal<a class="headerlink" href="#torch-normal" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.normal">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">normal</code><span class="sig-paren">(</span><em class="sig-param">mean</em>, <em class="sig-param">std</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.normal" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a tensor of random numbers drawn from separate normal distributions
whose mean and standard deviation are given.</p>
<p>The <a class="reference internal" href="torch.mean.html#torch.mean" title="torch.mean"><code class="xref py py-attr docutils literal notranslate"><span class="pre">mean</span></code></a> is a tensor with the mean of
each output element’s normal distribution</p>
<p>The <a class="reference internal" href="torch.std.html#torch.std" title="torch.std"><code class="xref py py-attr docutils literal notranslate"><span class="pre">std</span></code></a> is a tensor with the standard deviation of
each output element’s normal distribution</p>
<p>The shapes of <a class="reference internal" href="torch.mean.html#torch.mean" title="torch.mean"><code class="xref py py-attr docutils literal notranslate"><span class="pre">mean</span></code></a> and <a class="reference internal" href="torch.std.html#torch.std" title="torch.std"><code class="xref py py-attr docutils literal notranslate"><span class="pre">std</span></code></a> don’t need to match, but the
total number of elements in each tensor need to be the same.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When the shapes do not match, the shape of <a class="reference internal" href="torch.mean.html#torch.mean" title="torch.mean"><code class="xref py py-attr docutils literal notranslate"><span class="pre">mean</span></code></a>
is used as the shape for the returned output tensor</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mean</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the tensor of per-element means</p></li>
<li><p><strong>std</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the tensor of per-element standard deviations</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<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">torch</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">11.</span><span class="p">),</span> <span class="n">std</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1</span><span class="p">))</span>
<span class="go">tensor([ 1.0425, 3.5672, 2.7969, 4.2925, 4.7229, 6.2134,</span>
<span class="go"> 8.0505, 8.1408, 9.0563, 10.0566])</span>
</pre></div>
</div>
<dl class="function">
<dt>
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">normal</code><span class="sig-paren">(</span><em class="sig-param">mean=0.0</em>, <em class="sig-param">std</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor</dt>
<dd></dd></dl>
<p>Similar to the function above, but the means are shared among all drawn
elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mean</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>, </em><em>optional</em>) – the mean for all distributions</p></li>
<li><p><strong>std</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the tensor of per-element standard deviations</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><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>
</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">normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">))</span>
<span class="go">tensor([-1.2793, -1.0732, -2.0687, 5.1177, -1.2303])</span>
</pre></div>
</div>
<dl class="function">
<dt>
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">normal</code><span class="sig-paren">(</span><em class="sig-param">mean</em>, <em class="sig-param">std=1.0</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor</dt>
<dd></dd></dl>
<p>Similar to the function above, but the standard-deviations are shared among
all drawn elements.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mean</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the tensor of per-element means</p></li>
<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em>, </em><em>optional</em>) – the standard deviation for all distributions</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><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>
</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">normal</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">6.</span><span class="p">))</span>
<span class="go">tensor([ 1.1552, 2.6148, 2.6535, 5.8318, 4.2361])</span>
</pre></div>
</div>
<dl class="function">
<dt>
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">normal</code><span class="sig-paren">(</span><em class="sig-param">mean</em>, <em class="sig-param">std</em>, <em class="sig-param">size</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor</dt>
<dd></dd></dl>
<p>Similar to the function above, but the means and standard deviations are shared
among all drawn elements. The resulting tensor has size given by <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mean</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – the mean for all distributions</p></li>
<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – the standard deviation for all distributions</p></li>
<li><p><strong>size</strong> (<em>int...</em>) – a sequence of integers defining the shape of the output tensor.</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><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>
</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">normal</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="n">size</span><span class="o">=</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="go">tensor([[-1.3987, -1.9544, 3.6048, 0.7909]])</span>
</pre></div>
</div>
</dd></dl>
</div>
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