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  <div class="section" id="module-torchlayers.activations">
<span id="torchlayers-activations-module"></span><h1>torchlayers.activations module<a class="headerlink" href="#module-torchlayers.activations" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="torchlayers.activations.HardSigmoid">
<em class="property">class </em><code class="sig-prename descclassname">torchlayers.activations.</code><code class="sig-name descname">HardSigmoid</code><a class="reference internal" href="../_modules/torchlayers/activations.html#HardSigmoid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchlayers.activations.HardSigmoid" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies HardSigmoid function element-wise.</p>
<p>Uses <a class="reference external" href="https://pytorch.org/docs/stable/nn.functional.html#torch.nn.functional.hardtanh" title="(in PyTorch vmaster (1.4.0a0+919fcbb ))"><code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.nn.functional.hardtanh</span></code></a> internally with <code class="xref py py-obj docutils literal notranslate"><span class="pre">0</span></code> and <code class="xref py py-obj docutils literal notranslate"><span class="pre">1</span></code> ranges.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch vmaster (1.4.0a0+919fcbb ))"><em>torch.Tensor</em></a>) – Tensor activated element-wise</p>
</dd>
</dl>
<dl class="method">
<dt id="torchlayers.activations.HardSigmoid.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">tensor: torch.Tensor</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchlayers/activations.html#HardSigmoid.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchlayers.activations.HardSigmoid.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div>
</dd></dl>

</dd></dl>

<dl class="class">
<dt id="torchlayers.activations.HardSwish">
<em class="property">class </em><code class="sig-prename descclassname">torchlayers.activations.</code><code class="sig-name descname">HardSwish</code><a class="reference internal" href="../_modules/torchlayers/activations.html#HardSwish"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchlayers.activations.HardSwish" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies HardSwish function element-wise.</p>
<div class="math">
<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>H</mi><mi>a</mi><mi>r</mi><mi>d</mi><mi>S</mi><mi>w</mi><mi>i</mi><mi>s</mi><mi>h</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi><mo>∗</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo>+</mo><mn>3</mn><mo stretchy="false">)</mo><mo separator="true">,</mo><mn>6</mn><mo stretchy="false">)</mo><mi mathvariant="normal">/</mi><mn>6</mn></mrow><annotation encoding="application/x-tex">HardSwish(x) = x * \min(\max(0,x + 3), 6) / 6</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 mathdefault" style="margin-right:0.08125em;">H</span><span class="mord mathdefault">a</span><span class="mord mathdefault" style="margin-right:0.02778em;">r</span><span class="mord mathdefault">d</span><span class="mord mathdefault" style="margin-right:0.05764em;">S</span><span class="mord mathdefault" style="margin-right:0.02691em;">w</span><span class="mord mathdefault">i</span><span class="mord mathdefault">s</span><span class="mord mathdefault">h</span><span class="mopen">(</span><span class="mord mathdefault">x</span><span class="mclose">)</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:0.46528em;vertical-align:0em;"></span><span class="mord mathdefault">x</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:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mop">max</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 mathdefault">x</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:1em;vertical-align:-0.25em;"></span><span class="mord">3</span><span class="mclose">)</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">6</span><span class="mclose">)</span><span class="mord">/</span><span class="mord">6</span></span></span></span></span>

</div><p>While similar in effect to <a class="reference internal" href="#torchlayers.activations.Swish" title="torchlayers.activations.Swish"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Swish</span></code></a> should be more CPU-efficient.
Above formula proposed by in Andrew Howard et. al in <a class="reference external" href="https://arxiv.org/pdf/1905.02244.pdf">Searching for MobileNetV3</a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch vmaster (1.4.0a0+919fcbb ))"><em>torch.Tensor</em></a>) – Tensor activated element-wise</p>
</dd>
</dl>
<dl class="method">
<dt id="torchlayers.activations.HardSwish.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">tensor: torch.Tensor</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchlayers/activations.html#HardSwish.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchlayers.activations.HardSwish.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div>
</dd></dl>

</dd></dl>

<dl class="class">
<dt id="torchlayers.activations.Swish">
<em class="property">class </em><code class="sig-prename descclassname">torchlayers.activations.</code><code class="sig-name descname">Swish</code><span class="sig-paren">(</span><em class="sig-param">beta: float = 1.0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchlayers/activations.html#Swish"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchlayers.activations.Swish" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies Swish function element-wise.</p>
<div class="math">
<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>S</mi><mi>w</mi><mi>i</mi><mi>s</mi><mi>h</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi><mi mathvariant="normal">/</mi><mo stretchy="false">(</mo><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>b</mi><mi>e</mi><mi>t</mi><mi>a</mi><mo>∗</mo><mi>x</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">Swish(x) = x / (1 + \exp(-beta * x))</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 mathdefault" style="margin-right:0.05764em;">S</span><span class="mord mathdefault" style="margin-right:0.02691em;">w</span><span class="mord mathdefault">i</span><span class="mord mathdefault">s</span><span class="mord mathdefault">h</span><span class="mopen">(</span><span class="mord mathdefault">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathdefault">x</span><span class="mord">/</span><span class="mopen">(</span><span class="mord">1</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:1em;vertical-align:-0.25em;"></span><span class="mop">exp</span><span class="mopen">(</span><span class="mord">−</span><span class="mord mathdefault">b</span><span class="mord mathdefault">e</span><span class="mord mathdefault">t</span><span class="mord mathdefault">a</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:1em;vertical-align:-0.25em;"></span><span class="mord mathdefault">x</span><span class="mclose">)</span><span class="mclose">)</span></span></span></span></span>

</div><p>This form was originally proposed by Prajit Ramachandran et. al in
<a class="reference external" href="https://arxiv.org/pdf/1710.05941.pdf">Searching for Activation Functions</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>beta</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.8)"><em>float</em></a><em>, </em><em>optional</em>) – Multiplier used for sigmoid. Default: 1.0 (no multiplier)</p>
</dd>
</dl>
<dl class="method">
<dt id="torchlayers.activations.Swish.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">tensor: torch.Tensor</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchlayers/activations.html#Swish.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchlayers.activations.Swish.forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div>
</dd></dl>

</dd></dl>

<dl class="function">
<dt id="torchlayers.activations.hard_sigmoid">
<code class="sig-prename descclassname">torchlayers.activations.</code><code class="sig-name descname">hard_sigmoid</code><span class="sig-paren">(</span><em class="sig-param">tensor: torch.Tensor</em>, <em class="sig-param">inplace: bool = False</em><span class="sig-paren">)</span> &#x2192; torch.Tensor<a class="reference internal" href="../_modules/torchlayers/activations.html#hard_sigmoid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchlayers.activations.hard_sigmoid" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies HardSigmoid function element-wise.</p>
<p>See <a class="reference internal" href="#torchlayers.activations.HardSigmoid" title="torchlayers.activations.HardSigmoid"><code class="xref py py-class docutils literal notranslate"><span class="pre">torchlayers.activations.HardSigmoid</span></code></a> for more details.</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 external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch vmaster (1.4.0a0+919fcbb ))"><em>torch.Tensor</em></a>) – Tensor activated element-wise</p></li>
<li><p><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a><em>, </em><em>optional</em>) – Whether operation should be performed <code class="xref py py-obj docutils literal notranslate"><span class="pre">in-place</span></code>. Default: <a class="reference external" href="https://docs.python.org/3/library/constants.html#False" title="(in Python v3.8)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">False</span></code></a></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch vmaster (1.4.0a0+919fcbb ))">torch.Tensor</a></p>
</dd>
</dl>
</dd></dl>

<dl class="function">
<dt id="torchlayers.activations.hard_swish">
<code class="sig-prename descclassname">torchlayers.activations.</code><code class="sig-name descname">hard_swish</code><span class="sig-paren">(</span><em class="sig-param">tensor: torch.Tensor</em><span class="sig-paren">)</span> &#x2192; torch.Tensor<a class="reference internal" href="../_modules/torchlayers/activations.html#hard_swish"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchlayers.activations.hard_swish" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies HardSwish function element-wise.</p>
<p>See <a class="reference internal" href="#torchlayers.activations.HardSwish" title="torchlayers.activations.HardSwish"><code class="xref py py-class docutils literal notranslate"><span class="pre">torchlayers.activations.HardSwish</span></code></a> for more details.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>tensor</strong> (<a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch vmaster (1.4.0a0+919fcbb ))"><em>torch.Tensor</em></a>) – Tensor activated element-wise</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch vmaster (1.4.0a0+919fcbb ))">torch.Tensor</a></p>
</dd>
</dl>
</dd></dl>

<dl class="function">
<dt id="torchlayers.activations.swish">
<code class="sig-prename descclassname">torchlayers.activations.</code><code class="sig-name descname">swish</code><span class="sig-paren">(</span><em class="sig-param">tensor: torch.Tensor</em>, <em class="sig-param">beta: float = 1.0</em><span class="sig-paren">)</span> &#x2192; torch.Tensor<a class="reference internal" href="../_modules/torchlayers/activations.html#swish"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchlayers.activations.swish" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies Swish function element-wise.</p>
<p>See <a class="reference internal" href="#torchlayers.activations.Swish" title="torchlayers.activations.Swish"><code class="xref py py-class docutils literal notranslate"><span class="pre">torchlayers.activations.Swish</span></code></a> for more details.</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 external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch vmaster (1.4.0a0+919fcbb ))"><em>torch.Tensor</em></a>) – Tensor activated element-wise</p></li>
<li><p><strong>beta</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.8)"><em>float</em></a><em>, </em><em>optional</em>) – Multiplier used for sigmoid. Default: 1.0 (no multiplier)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://pytorch.org/docs/stable/tensors.html#torch.Tensor" title="(in PyTorch vmaster (1.4.0a0+919fcbb ))">torch.Tensor</a></p>
</dd>
</dl>
</dd></dl>

</div>


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