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<div class="section" id="torch-nn-init">
<span id="nn-init-doc"></span><h1>torch.nn.init<a class="headerlink" href="#torch-nn-init" title="Permalink to this heading">¶</a></h1>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>All the functions in this module are intended to be used to initialize neural network
parameters, so they all run in <a class="reference internal" href="generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.no_grad()</span></code></a> mode and will not be taken into
account by autograd.</p>
</div>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.calculate_gain">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">calculate_gain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</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="_modules/torch/nn/init.html#calculate_gain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.calculate_gain" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the recommended gain value for the given nonlinearity function.</p>
<p>The values are as follows:</p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>nonlinearity</p></th>
<th class="head"><p>gain</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>Linear / Identity</p></td>
<td><p><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:0.6444em;"></span><span class="mord">1</span></span></span></span></span></p></td>
</tr>
<tr class="row-odd"><td><p>Conv{1,2,3}D</p></td>
<td><p><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:0.6444em;"></span><span class="mord">1</span></span></span></span></span></p></td>
</tr>
<tr class="row-even"><td><p>Sigmoid</p></td>
<td><p><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:0.6444em;"></span><span class="mord">1</span></span></span></span></span></p></td>
</tr>
<tr class="row-odd"><td><p>Tanh</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mfrac><mn>5</mn><mn>3</mn></mfrac></mrow><annotation encoding="application/x-tex">\frac{5}{3}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1901em;vertical-align:-0.345em;"></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:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">3</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:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">5</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></span></span></span></p></td>
</tr>
<tr class="row-even"><td><p>ReLU</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msqrt><mn>2</mn></msqrt></mrow><annotation encoding="application/x-tex">\sqrt{2}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.04em;vertical-align:-0.1328em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.9072em;"><span class="svg-align" style="top:-3em;"><span class="pstrut" style="height:3em;"></span><span class="mord" style="padding-left:0.833em;"><span class="mord">2</span></span></span><span style="top:-2.8672em;"><span class="pstrut" style="height:3em;"></span><span class="hide-tail" style="min-width:0.853em;height:1.08em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="1.08em" viewBox="0 0 400000 1080" preserveAspectRatio="xMinYMin slice"><path d="M95,702
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</tr>
<tr class="row-odd"><td><p>Leaky Relu</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msqrt><mfrac><mn>2</mn><mrow><mn>1</mn><mo>+</mo><msup><mtext>negative_slope</mtext><mn>2</mn></msup></mrow></mfrac></msqrt></mrow><annotation encoding="application/x-tex">\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.84em;vertical-align:-0.7266em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.1134em;"><span class="svg-align" style="top:-3.8em;"><span class="pstrut" style="height:3.8em;"></span><span class="mord" style="padding-left:1em;"><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:0.8451em;"><span style="top:-2.6287em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">negative_slope</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8019em;"><span style="top:-2.8416em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mtight">2</span></span></span></span></span></span></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:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">2</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.5883em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span><span style="top:-3.0734em;"><span class="pstrut" style="height:3.8em;"></span><span class="hide-tail" style="min-width:1.02em;height:1.88em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="1.88em" viewBox="0 0 400000 1944" preserveAspectRatio="xMinYMin slice"><path d="M983 90
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</tr>
<tr class="row-even"><td><p>SELU</p></td>
<td><p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mfrac><mn>3</mn><mn>4</mn></mfrac></mrow><annotation encoding="application/x-tex">\frac{3}{4}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1901em;vertical-align:-0.345em;"></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:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">4</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:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">3</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></span></span></span></p></td>
</tr>
</tbody>
</table>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>In order to implement <a class="reference external" href="https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html">Self-Normalizing Neural Networks</a> ,
you should use <code class="docutils literal notranslate"><span class="pre">nonlinearity='linear'</span></code> instead of <code class="docutils literal notranslate"><span class="pre">nonlinearity='selu'</span></code>.
This gives the initial weights a variance of <code class="docutils literal notranslate"><span class="pre">1</span> <span class="pre">/</span> <span class="pre">N</span></code>,
which is necessary to induce a stable fixed point in the forward pass.
In contrast, the default gain for <code class="docutils literal notranslate"><span class="pre">SELU</span></code> sacrifices the normalization
effect for more stable gradient flow in rectangular layers.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nonlinearity</strong> – the non-linear function (<cite>nn.functional</cite> name)</p></li>
<li><p><strong>param</strong> – optional parameter for the non-linear function</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">gain</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">calculate_gain</span><span class="p">(</span><span class="s1">'leaky_relu'</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">)</span> <span class="c1"># leaky_relu with negative_slope=0.2</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.uniform_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</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="_modules/torch/nn/init.html#uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.uniform_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input Tensor with values drawn from the uniform distribution.</p>
<p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">U</mi><mo stretchy="false">(</mo><mi>a</mi><mo separator="true">,</mo><mi>b</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{U}(a, b)</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 mathcal" style="margin-right:0.09931em;">U</span><span class="mopen">(</span><span class="mord mathnormal">a</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">b</span><span class="mclose">)</span></span></span></span></span>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>a</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the lower bound of the uniform distribution</p></li>
<li><p><strong>b</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the upper bound of the uniform distribution</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.normal_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</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="_modules/torch/nn/init.html#normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.normal_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input Tensor with values drawn from the normal distribution.</p>
<p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mtext>mean</mtext><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(\text{mean}, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord text"><span class="mord">mean</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>mean</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the mean of the normal distribution</p></li>
<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the standard deviation of the normal distribution</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.constant_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">constant_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#constant_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.constant_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input Tensor with the value <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>val</mtext></mrow><annotation encoding="application/x-tex">\text{val}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord text"><span class="mord">val</span></span></span></span></span></span>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>val</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the value to fill the tensor with</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.ones_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">ones_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#ones_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.ones_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input Tensor with the scalar value <cite>1</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">ones_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.zeros_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">zeros_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#zeros_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.zeros_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input Tensor with the scalar value <cite>0</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">zeros_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.eye_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">eye_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#eye_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.eye_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the 2-dimensional input <cite>Tensor</cite> with the identity matrix.</p>
<p>Preserves the identity of the inputs in <cite>Linear</cite> layers, where as
many inputs are preserved as possible.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> – a 2-dimensional <cite>torch.Tensor</cite></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">eye_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.dirac_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">dirac_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#dirac_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.dirac_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the {3, 4, 5}-dimensional input <cite>Tensor</cite> with the Dirac delta function.</p>
<p>Preserves the identity of the inputs in <cite>Convolutional</cite>
layers, where as many input channels are preserved as possible. In case
of groups>1, each group of channels preserves identity</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> – a {3, 4, 5}-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>groups</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>, </em><em>optional</em>) – number of groups in the conv layer (default: 1)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">16</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">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">dirac_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">w</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">24</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">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">dirac_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.xavier_uniform_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</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="_modules/torch/nn/init.html#xavier_uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_uniform_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input <cite>Tensor</cite> with values using a Xavier uniform distribution.</p>
<p>The method is described in <cite>Understanding the difficulty of training
deep feedforward neural networks</cite> - Glorot, X. & Bengio, Y. (2010).
The resulting tensor will have values sampled from
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">U</mi><mo stretchy="false">(</mo><mo>−</mo><mi>a</mi><mo separator="true">,</mo><mi>a</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{U}(-a, a)</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 mathcal" style="margin-right:0.09931em;">U</span><span class="mopen">(</span><span class="mord">−</span><span class="mord mathnormal">a</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">a</span><span class="mclose">)</span></span></span></span></span> where</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><mi>a</mi><mo>=</mo><mtext>gain</mtext><mo>×</mo><msqrt><mfrac><mn>6</mn><mrow><mtext>fan_in</mtext><mo>+</mo><mtext>fan_out</mtext></mrow></mfrac></msqrt></mrow><annotation encoding="application/x-tex">a = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}}
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">a</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8623em;vertical-align:-0.1944em;"></span><span class="mord text"><span class="mord">gain</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:3.04em;vertical-align:-1.2434em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.7966em;"><span class="svg-align" style="top:-5em;"><span class="pstrut" style="height:5em;"></span><span class="mord" style="padding-left:1em;"><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:1.3214em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord text"><span class="mord">fan_in</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord text"><span class="mord">fan_out</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:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord">6</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.996em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span><span style="top:-3.7566em;"><span class="pstrut" style="height:5em;"></span><span class="hide-tail" style="min-width:1.02em;height:3.08em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="3.08em" viewBox="0 0 400000 3240" preserveAspectRatio="xMinYMin slice"><path d="M473,2793
c339.3,-1799.3,509.3,-2700,510,-2702 l0 -0
c3.3,-7.3,9.3,-11,18,-11 H400000v40H1017.7
s-90.5,478,-276.2,1466c-185.7,988,-279.5,1483,-281.5,1485c-2,6,-10,9,-24,9
c-8,0,-12,-0.7,-12,-2c0,-1.3,-5.3,-32,-16,-92c-50.7,-293.3,-119.7,-693.3,-207,-1200
c0,-1.3,-5.3,8.7,-16,30c-10.7,21.3,-21.3,42.7,-32,64s-16,33,-16,33s-26,-26,-26,-26
s76,-153,76,-153s77,-151,77,-151c0.7,0.7,35.7,202,105,604c67.3,400.7,102,602.7,104,
606zM1001 80h400000v40H1017.7z"/></svg></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:1.2434em;"><span></span></span></span></span></span></span></span></span></span></div><p>Also known as Glorot initialization.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>gain</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – an optional scaling factor</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">xavier_uniform_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">calculate_gain</span><span class="p">(</span><span class="s1">'relu'</span><span class="p">))</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Be aware that <code class="docutils literal notranslate"><span class="pre">fan_in</span></code> and <code class="docutils literal notranslate"><span class="pre">fan_out</span></code> are calculated assuming
that the weight matrix is used in a transposed manner,
(i.e., <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">@</span> <span class="pre">w.T</span></code> in <code class="docutils literal notranslate"><span class="pre">Linear</span></code> layers, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_out,</span> <span class="pre">fan_in]</span></code>).
This is important for correct initialization.
If you plan to use <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">@</span> <span class="pre">w</span></code>, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_in,</span> <span class="pre">fan_out]</span></code>,
pass in a transposed weight matrix, i.e. <code class="docutils literal notranslate"><span class="pre">nn.init.xavier_uniform_(w.T,</span> <span class="pre">...)</span></code>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.xavier_normal_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</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="_modules/torch/nn/init.html#xavier_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_normal_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input <cite>Tensor</cite> with values using a Xavier normal distribution.</p>
<p>The method is described in <cite>Understanding the difficulty of training deep feedforward
neural networks</cite> - Glorot, X. & Bengio, Y. (2010). The resulting tensor
will have values sampled from <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(0, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> where</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><mtext>std</mtext><mo>=</mo><mtext>gain</mtext><mo>×</mo><msqrt><mfrac><mn>2</mn><mrow><mtext>fan_in</mtext><mo>+</mo><mtext>fan_out</mtext></mrow></mfrac></msqrt></mrow><annotation encoding="application/x-tex">\text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan\_in} + \text{fan\_out}}}
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord text"><span class="mord">std</span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8623em;vertical-align:-0.1944em;"></span><span class="mord text"><span class="mord">gain</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:3.04em;vertical-align:-1.2434em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.7966em;"><span class="svg-align" style="top:-5em;"><span class="pstrut" style="height:5em;"></span><span class="mord" style="padding-left:1em;"><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:1.3214em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord text"><span class="mord">fan_in</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mord text"><span class="mord">fan_out</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:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord">2</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.996em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span><span style="top:-3.7566em;"><span class="pstrut" style="height:5em;"></span><span class="hide-tail" style="min-width:1.02em;height:3.08em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="3.08em" viewBox="0 0 400000 3240" preserveAspectRatio="xMinYMin slice"><path d="M473,2793
c339.3,-1799.3,509.3,-2700,510,-2702 l0 -0
c3.3,-7.3,9.3,-11,18,-11 H400000v40H1017.7
s-90.5,478,-276.2,1466c-185.7,988,-279.5,1483,-281.5,1485c-2,6,-10,9,-24,9
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606zM1001 80h400000v40H1017.7z"/></svg></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:1.2434em;"><span></span></span></span></span></span></span></span></span></span></div><p>Also known as Glorot initialization.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>gain</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – an optional scaling factor</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">xavier_normal_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Be aware that <code class="docutils literal notranslate"><span class="pre">fan_in</span></code> and <code class="docutils literal notranslate"><span class="pre">fan_out</span></code> are calculated assuming
that the weight matrix is used in a transposed manner,
(i.e., <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">@</span> <span class="pre">w.T</span></code> in <code class="docutils literal notranslate"><span class="pre">Linear</span></code> layers, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_out,</span> <span class="pre">fan_in]</span></code>).
This is important for correct initialization.
If you plan to use <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">@</span> <span class="pre">w</span></code>, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_in,</span> <span class="pre">fan_out]</span></code>,
pass in a transposed weight matrix, i.e. <code class="docutils literal notranslate"><span class="pre">nn.init.xavier_normal_(w.T,</span> <span class="pre">...)</span></code>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.kaiming_uniform_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</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="_modules/torch/nn/init.html#kaiming_uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_uniform_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input <cite>Tensor</cite> with values using a Kaiming uniform distribution.</p>
<p>The method is described in <cite>Delving deep into rectifiers: Surpassing
human-level performance on ImageNet classification</cite> - He, K. et al. (2015).
The resulting tensor will have values sampled from
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">U</mi><mo stretchy="false">(</mo><mo>−</mo><mtext>bound</mtext><mo separator="true">,</mo><mtext>bound</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{U}(-\text{bound}, \text{bound})</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 mathcal" style="margin-right:0.09931em;">U</span><span class="mopen">(</span><span class="mord">−</span><span class="mord text"><span class="mord">bound</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord text"><span class="mord">bound</span></span><span class="mclose">)</span></span></span></span></span> where</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><mtext>bound</mtext><mo>=</mo><mtext>gain</mtext><mo>×</mo><msqrt><mfrac><mn>3</mn><mtext>fan_mode</mtext></mfrac></msqrt></mrow><annotation encoding="application/x-tex">\text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord text"><span class="mord">bound</span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8623em;vertical-align:-0.1944em;"></span><span class="mord text"><span class="mord">gain</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:3.04em;vertical-align:-1.2434em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.7966em;"><span class="svg-align" style="top:-5em;"><span class="pstrut" style="height:5em;"></span><span class="mord" style="padding-left:1em;"><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:1.3214em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord text"><span class="mord">fan_mode</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:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord">3</span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.996em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span><span style="top:-3.7566em;"><span class="pstrut" style="height:5em;"></span><span class="hide-tail" style="min-width:1.02em;height:3.08em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="3.08em" viewBox="0 0 400000 3240" preserveAspectRatio="xMinYMin slice"><path d="M473,2793
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s76,-153,76,-153s77,-151,77,-151c0.7,0.7,35.7,202,105,604c67.3,400.7,102,602.7,104,
606zM1001 80h400000v40H1017.7z"/></svg></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:1.2434em;"><span></span></span></span></span></span></span></span></span></span></div><p>Also known as He initialization.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>a</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the negative slope of the rectifier used after this layer (only
used with <code class="docutils literal notranslate"><span class="pre">'leaky_relu'</span></code>)</p></li>
<li><p><strong>mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – either <code class="docutils literal notranslate"><span class="pre">'fan_in'</span></code> (default) or <code class="docutils literal notranslate"><span class="pre">'fan_out'</span></code>. Choosing <code class="docutils literal notranslate"><span class="pre">'fan_in'</span></code>
preserves the magnitude of the variance of the weights in the
forward pass. Choosing <code class="docutils literal notranslate"><span class="pre">'fan_out'</span></code> preserves the magnitudes in the
backwards pass.</p></li>
<li><p><strong>nonlinearity</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – the non-linear function (<cite>nn.functional</cite> name),
recommended to use only with <code class="docutils literal notranslate"><span class="pre">'relu'</span></code> or <code class="docutils literal notranslate"><span class="pre">'leaky_relu'</span></code> (default).</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_uniform_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'fan_in'</span><span class="p">,</span> <span class="n">nonlinearity</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Be aware that <code class="docutils literal notranslate"><span class="pre">fan_in</span></code> and <code class="docutils literal notranslate"><span class="pre">fan_out</span></code> are calculated assuming
that the weight matrix is used in a transposed manner,
(i.e., <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">@</span> <span class="pre">w.T</span></code> in <code class="docutils literal notranslate"><span class="pre">Linear</span></code> layers, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_out,</span> <span class="pre">fan_in]</span></code>).
This is important for correct initialization.
If you plan to use <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">@</span> <span class="pre">w</span></code>, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_in,</span> <span class="pre">fan_out]</span></code>,
pass in a transposed weight matrix, i.e. <code class="docutils literal notranslate"><span class="pre">nn.init.kaiming_uniform_(w.T,</span> <span class="pre">...)</span></code>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.kaiming_normal_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</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="_modules/torch/nn/init.html#kaiming_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_normal_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input <cite>Tensor</cite> with values using a Kaiming normal distribution.</p>
<p>The method is described in <cite>Delving deep into rectifiers: Surpassing
human-level performance on ImageNet classification</cite> - He, K. et al. (2015).
The resulting tensor will have values sampled from
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(0, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> where</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><mtext>std</mtext><mo>=</mo><mfrac><mtext>gain</mtext><msqrt><mtext>fan_mode</mtext></msqrt></mfrac></mrow><annotation encoding="application/x-tex">\text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord text"><span class="mord">std</span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:2.4749em;vertical-align:-1.13em;"></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:1.3449em;"><span style="top:-2.2328em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8772em;"><span class="svg-align" style="top:-3.2em;"><span class="pstrut" style="height:3.2em;"></span><span class="mord" style="padding-left:1em;"><span class="mord text"><span class="mord">fan_mode</span></span></span></span><span style="top:-2.8372em;"><span class="pstrut" style="height:3.2em;"></span><span class="hide-tail" style="min-width:1.02em;height:1.28em;"><svg xmlns="http://www.w3.org/2000/svg" width="400em" height="1.28em" viewBox="0 0 400000 1296" preserveAspectRatio="xMinYMin slice"><path d="M263,681c0.7,0,18,39.7,52,119
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>a</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the negative slope of the rectifier used after this layer (only
used with <code class="docutils literal notranslate"><span class="pre">'leaky_relu'</span></code>)</p></li>
<li><p><strong>mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – either <code class="docutils literal notranslate"><span class="pre">'fan_in'</span></code> (default) or <code class="docutils literal notranslate"><span class="pre">'fan_out'</span></code>. Choosing <code class="docutils literal notranslate"><span class="pre">'fan_in'</span></code>
preserves the magnitude of the variance of the weights in the
forward pass. Choosing <code class="docutils literal notranslate"><span class="pre">'fan_out'</span></code> preserves the magnitudes in the
backwards pass.</p></li>
<li><p><strong>nonlinearity</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – the non-linear function (<cite>nn.functional</cite> name),
recommended to use only with <code class="docutils literal notranslate"><span class="pre">'relu'</span></code> or <code class="docutils literal notranslate"><span class="pre">'leaky_relu'</span></code> (default).</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'fan_out'</span><span class="p">,</span> <span class="n">nonlinearity</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Be aware that <code class="docutils literal notranslate"><span class="pre">fan_in</span></code> and <code class="docutils literal notranslate"><span class="pre">fan_out</span></code> are calculated assuming
that the weight matrix is used in a transposed manner,
(i.e., <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">@</span> <span class="pre">w.T</span></code> in <code class="docutils literal notranslate"><span class="pre">Linear</span></code> layers, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_out,</span> <span class="pre">fan_in]</span></code>).
This is important for correct initialization.
If you plan to use <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">@</span> <span class="pre">w</span></code>, where <code class="docutils literal notranslate"><span class="pre">w.shape</span> <span class="pre">=</span> <span class="pre">[fan_in,</span> <span class="pre">fan_out]</span></code>,
pass in a transposed weight matrix, i.e. <code class="docutils literal notranslate"><span class="pre">nn.init.kaiming_normal_(w.T,</span> <span class="pre">...)</span></code>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.trunc_normal_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">trunc_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</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="_modules/torch/nn/init.html#trunc_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.trunc_normal_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input Tensor with values drawn from a truncated normal distribution.</p>
<p>The values are effectively drawn from the
normal distribution <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mtext>mean</mtext><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(\text{mean}, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord text"><span class="mord">mean</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>
with values outside <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">[</mo><mi>a</mi><mo separator="true">,</mo><mi>b</mi><mo stretchy="false">]</mo></mrow><annotation encoding="application/x-tex">[a, b]</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="mopen">[</span><span class="mord mathnormal">a</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">b</span><span class="mclose">]</span></span></span></span></span> redrawn until they are within
the bounds. The method used for generating the random values works
best when <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>a</mi><mo>≤</mo><mtext>mean</mtext><mo>≤</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">a \leq \text{mean} \leq b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7719em;vertical-align:-0.136em;"></span><span class="mord mathnormal">a</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">≤</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7719em;vertical-align:-0.136em;"></span><span class="mord text"><span class="mord">mean</span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">≤</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>mean</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the mean of the normal distribution</p></li>
<li><p><strong>std</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the standard deviation of the normal distribution</p></li>
<li><p><strong>a</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the minimum cutoff value</p></li>
<li><p><strong>b</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – the maximum cutoff value</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">trunc_normal_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.nn.init.orthogonal_">
<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">orthogonal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</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="_modules/torch/nn/init.html#orthogonal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.orthogonal_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fill the input <cite>Tensor</cite> with a (semi) orthogonal matrix.</p>
<p>Described in <cite>Exact solutions to the nonlinear dynamics of learning in deep
linear neural networks</cite> - Saxe, A. et al. (2013). The input tensor must have
at least 2 dimensions, and for tensors with more than 2 dimensions the
trailing dimensions are flattened.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> – an n-dimensional <cite>torch.Tensor</cite>, where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi><mo>≥</mo><mn>2</mn></mrow><annotation encoding="application/x-tex">n \geq 2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7719em;vertical-align:-0.136em;"></span><span class="mord mathnormal">n</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">≥</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">2</span></span></span></span></span></p></li>
<li><p><strong>gain</strong> – optional scaling factor</p></li>
<li><p><strong>generator</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a><em>[</em><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch._C.Generator"><em>Generator</em></a><em>]</em>) – the torch Generator to sample from (default: None)</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">w</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">5</span><span class="p">)</span>