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<section id="layers-package">
<h1>layers Package<a class="headerlink" href="#layers-package" title="Permalink to this headline"></a></h1>
<section id="id1">
<h2><code class="xref py py-mod docutils literal notranslate"><span class="pre">layers</span></code> Package<a class="headerlink" href="#id1" title="Permalink to this headline"></a></h2>
<span class="target" id="module-sde_calibration.dnn_estimators.layers"></span><p>Package provides custom layers that are used for some of the models used in the
estimators.</p>
</section>
<section id="module-sde_calibration.dnn_estimators.layers.dense_mixture">
<span id="dense-mixture-module"></span><h2><code class="xref py py-mod docutils literal notranslate"><span class="pre">dense_mixture</span></code> Module<a class="headerlink" href="#module-sde_calibration.dnn_estimators.layers.dense_mixture" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="sde_calibration.dnn_estimators.layers.dense_mixture.DenseMixture">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">sde_calibration.dnn_estimators.layers.dense_mixture.</span></span><span class="sig-name descname"><span class="pre">DenseMixture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_dimensionality</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="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sde_calibration.dnn_estimators.layers.dense_mixture.DenseMixture" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">tensorflow.python.keras.engine.base_layer.Layer</span></code></p>
<p>Class providing a dense layer that is setup to be used for a mixture density
network (MDN)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>num_components</strong> (<em>int</em><em>, </em><em>optional</em>) – <p>Gives the number of components in the Gaussian mixture
model (GMM).</p>
<blockquote>
<div><p><a href="#id12"><span class="problematic" id="id13">|default|</span></a> <code class="code docutils literal notranslate"><span class="pre">10</span></code></p>
</div></blockquote>
</p></li>
<li><p><strong>label_dimensionality</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of dimensions for the response variables.
<a href="#id14"><span class="problematic" id="id15">|default|</span></a> <code class="code docutils literal notranslate"><span class="pre">1</span></code></p></li>
<li><p><strong>**kwargs</strong> – <p>Additional keyword arguments passed to the base class
<code class="xref py py-class docutils literal notranslate"><span class="pre">tensorflow.python.keras.engine.base_layer.Layer</span></code>. The documentation
can be found <a class="reference external" href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer">here</a>.</p>
</p></li>
</ul>
</dd>
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="sde_calibration.dnn_estimators.layers.dense_mixture.DenseMixture.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sde_calibration.dnn_estimators.layers.dense_mixture.DenseMixture.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives.</p>
<p>Note here that <cite>call()</cite> method in <cite>tf.keras</cite> is little bit different
from <cite>keras</cite> API. In <cite>keras</cite> API, you can pass support masking for
layers as additional arguments. Whereas <cite>tf.keras</cite> has <cite>compute_mask()</cite>
method to support masking.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>inputs: Input tensor, or list/tuple of input tensors.
<a href="#id4"><span class="problematic" id="id5">*</span></a>args: Additional positional arguments. Currently unused.
<a href="#id6"><span class="problematic" id="id7">**</span></a>kwargs: Additional keyword arguments. Currently unused.</p>
</dd>
<dt>Returns:</dt><dd><p>A tensor or list/tuple of tensors.</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="sde_calibration.dnn_estimators.layers.dense_mixture.DenseMixture.get_config">
<span class="sig-name descname"><span class="pre">get_config</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#sde_calibration.dnn_estimators.layers.dense_mixture.DenseMixture.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Returns the config of the layer.</p>
<p>A layer config is a Python dictionary (serializable)
containing the configuration of a layer.
The same layer can be reinstantiated later
(without its trained weights) from this configuration.</p>
<p>The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by <cite>Network</cite> (one layer of abstraction above).</p>
<p>Note that <cite>get_config()</cite> does not guarantee to return a fresh copy of dict
every time it is called. The callers should make a copy of the returned dict
if they want to modify it.</p>
<dl class="simple">
<dt>Returns:</dt><dd><p>Python dictionary.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
<section id="module-sde_calibration.dnn_estimators.layers.mc_dropout">
<span id="mc-dropout-module"></span><h2><code class="xref py py-mod docutils literal notranslate"><span class="pre">mc_dropout</span></code> Module<a class="headerlink" href="#module-sde_calibration.dnn_estimators.layers.mc_dropout" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="sde_calibration.dnn_estimators.layers.mc_dropout.MCDropout">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">sde_calibration.dnn_estimators.layers.mc_dropout.</span></span><span class="sig-name descname"><span class="pre">MCDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sde_calibration.dnn_estimators.layers.mc_dropout.MCDropout" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">tensorflow.python.keras.layers.core.Dropout</span></code></p>
<dl class="py method">
<dt class="sig sig-object py" id="sde_calibration.dnn_estimators.layers.mc_dropout.MCDropout.call">
<span class="sig-name descname"><span class="pre">call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sde_calibration.dnn_estimators.layers.mc_dropout.MCDropout.call" title="Permalink to this definition"></a></dt>
<dd><p>This is where the layer’s logic lives.</p>
<p>Note here that <cite>call()</cite> method in <cite>tf.keras</cite> is little bit different
from <cite>keras</cite> API. In <cite>keras</cite> API, you can pass support masking for
layers as additional arguments. Whereas <cite>tf.keras</cite> has <cite>compute_mask()</cite>
method to support masking.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>inputs: Input tensor, or list/tuple of input tensors.
<a href="#id8"><span class="problematic" id="id9">*</span></a>args: Additional positional arguments. Currently unused.
<a href="#id10"><span class="problematic" id="id11">**</span></a>kwargs: Additional keyword arguments. Currently unused.</p>
</dd>
<dt>Returns:</dt><dd><p>A tensor or list/tuple of tensors.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</section>
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