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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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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 headline">¶</a></h1>
<dl class="py function">
<dt id="torch.nn.init.calculate_gain">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">calculate_gain</span></code><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.
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:0.64444em;vertical-align:0em;"></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:0.64444em;vertical-align:0em;"></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:0.64444em;vertical-align:0em;"></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:1.190108em;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:0.845108em;"><span style="top:-2.6550000000000002em;"><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:-0.13278em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.90722em;"><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:-2.86722em;"><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:-0.72661em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.11339em;"><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:0.845108em;"><span style="top:-2.6286720000000003em;"><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:0.8018971428571429em;"><span style="top:-2.841582857142857em;margin-right:0.07142857142857144em;"><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:0.588328em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span><span style="top:-3.07339em;"><span class="pstrut" style="height:3.8em;"></span><span class="hide-tail" style="min-width:1.02em;height:1.8800000000000001em;"><svg xmlns="http://www.w3.org/2000/svg" width='400em' height='1.8800000000000001em' 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:1.190108em;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:0.845108em;"><span style="top:-2.6550000000000002em;"><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 normalisation
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 id="torch.nn.init.uniform_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">uniform_</span></code><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><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>Fills the input Tensor with values drawn from the uniform
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">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:0.16666666666666666em;"></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> – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>a</strong> – the lower bound of the uniform distribution</p></li>
<li><p><strong>b</strong> – the upper bound of the uniform distribution</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">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 id="torch.nn.init.normal_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">normal_</span></code><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><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>Fills the input Tensor with values 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:1.148448em;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:0.16666666666666666em;"></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:0.8984479999999999em;"><span style="top:-3.1473400000000002em;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> – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>mean</strong> – the mean of the normal distribution</p></li>
<li><p><strong>std</strong> – the standard deviation of the normal distribution</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">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 id="torch.nn.init.constant_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">constant_</span></code><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>Fills 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:0.69444em;vertical-align:0em;"></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> – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>val</strong> – the value to fill the tensor with</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">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 id="torch.nn.init.ones_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">ones_</span></code><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>Fills 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> – an n-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">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 id="torch.nn.init.zeros_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">zeros_</span></code><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>Fills 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> – an n-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">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 id="torch.nn.init.eye_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">eye_</span></code><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>Fills the 2-dimensional input <cite>Tensor</cite> with the identity
matrix. 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 id="torch.nn.init.dirac_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">dirac_</span></code><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>Fills the {3, 4, 5}-dimensional input <cite>Tensor</cite> with the Dirac
delta function. 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> (<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 id="torch.nn.init.xavier_uniform_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">xavier_uniform_</span></code><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><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>Fills the input <cite>Tensor</cite> with values according to the method
described in <cite>Understanding the difficulty of training deep feedforward
neural networks</cite> - Glorot, X. & Bengio, Y. (2010), using a uniform
distribution. 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:0.16666666666666666em;"></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:0.43056em;vertical-align:0em;"></span><span class="mord mathnormal">a</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:0.8623000000000001em;vertical-align:-0.19444em;"></span><span class="mord text"><span class="mord">gain</span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:3.04em;vertical-align:-1.243405em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.796595em;"><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:1.32144em;"><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:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></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:-3.756595em;"><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:1.243405em;"><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> – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>gain</strong> – an optional scaling factor</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">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>
</dd></dl>
<dl class="py function">
<dt id="torch.nn.init.xavier_normal_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">xavier_normal_</span></code><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><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>Fills the input <cite>Tensor</cite> with values according to the method
described in <cite>Understanding the difficulty of training deep feedforward
neural networks</cite> - Glorot, X. & Bengio, Y. (2010), using a normal
distribution. 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:1.148448em;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:0.16666666666666666em;"></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:0.8984479999999999em;"><span style="top:-3.1473400000000002em;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:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">std</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:0.8623000000000001em;vertical-align:-0.19444em;"></span><span class="mord text"><span class="mord">gain</span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:3.04em;vertical-align:-1.243405em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.796595em;"><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:1.32144em;"><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:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></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:-3.756595em;"><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:1.243405em;"><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> – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>gain</strong> – an optional scaling factor</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">xavier_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 id="torch.nn.init.kaiming_uniform_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">kaiming_uniform_</span></code><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><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>Fills the input <cite>Tensor</cite> with values according to the method
described in <cite>Delving deep into rectifiers: Surpassing human-level
performance on ImageNet classification</cite> - He, K. et al. (2015), using a
uniform distribution. 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:0.16666666666666666em;"></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:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">bound</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:0.8623000000000001em;vertical-align:-0.19444em;"></span><span class="mord text"><span class="mord">gain</span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:3.04em;vertical-align:-1.243405em;"></span><span class="mord sqrt"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.796595em;"><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:1.32144em;"><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:-3.756595em;"><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:1.243405em;"><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> – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>a</strong> – 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> – 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> – 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>
</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>
</dd></dl>
<dl class="py function">
<dt id="torch.nn.init.kaiming_normal_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">kaiming_normal_</span></code><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><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>Fills the input <cite>Tensor</cite> with values according to the method
described in <cite>Delving deep into rectifiers: Surpassing human-level
performance on ImageNet classification</cite> - He, K. et al. (2015), using a
normal distribution. 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:1.148448em;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:0.16666666666666666em;"></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:0.8984479999999999em;"><span style="top:-3.1473400000000002em;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:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">std</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:2.47486em;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:1.3448600000000002em;"><span style="top:-2.23278em;"><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:0.8772199999999999em;"><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:-2.8372200000000003em;"><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
c34,79.3,68.167,158.7,102.5,238c34.3,79.3,51.8,119.3,52.5,120
c340,-704.7,510.7,-1060.3,512,-1067
l0 -0
c4.7,-7.3,11,-11,19,-11
H40000v40H1012.3
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c-22.3,46.7,-33.8,70.3,-34.5,71c-4.7,4.7,-12.3,7,-23,7s-12,-1,-12,-1
s-109,-253,-109,-253c-72.7,-168,-109.3,-252,-110,-252c-10.7,8,-22,16.7,-34,26
c-22,17.3,-33.3,26,-34,26s-26,-26,-26,-26s76,-59,76,-59s76,-60,76,-60z
M1001 80h400000v40h-400000z'/></svg></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.3627800000000001em;"><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:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord text"><span class="mord">gain</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:1.13em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></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> – an n-dimensional <cite>torch.Tensor</cite></p></li>
<li><p><strong>a</strong> – 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> – 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> – 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>
</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>
</dd></dl>
<dl class="py function">
<dt id="torch.nn.init.orthogonal_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">orthogonal_</span></code><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><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>Fills the input <cite>Tensor</cite> with a (semi) orthogonal matrix, as
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:0.7719400000000001em;vertical-align:-0.13597em;"></span><span class="mord mathnormal">n</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">2</span></span></span></span></span></p></li>
<li><p><strong>gain</strong> – optional scaling factor</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">orthogonal_</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt id="torch.nn.init.sparse_">
<code class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></code><code class="sig-name descname"><span class="pre">sparse_</span></code><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">sparsity</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">0.01</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#sparse_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.sparse_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fills the 2D input <cite>Tensor</cite> as a sparse matrix, where the
non-zero elements will be 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><mn>0</mn><mo separator="true">,</mo><mn>0.01</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(0, 0.01)</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:0.14736em;">N</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">0.01</span><span class="mclose">)</span></span></span></span></span>, as described in <cite>Deep learning via
Hessian-free optimization</cite> - Martens, J. (2010).</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></p></li>
<li><p><strong>sparsity</strong> – The fraction of elements in each column to be set to zero</p></li>
<li><p><strong>std</strong> – the standard deviation of the normal distribution used to generate
the non-zero values</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">sparse_</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">sparsity</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
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