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<div class="section" id="mirapy-autoencoder-package">
<h1>mirapy.autoencoder package<a class="headerlink" href="#mirapy-autoencoder-package" title="Permalink to this headline">¶</a></h1>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="module-mirapy.autoencoder.models">
<span id="mirapy-autoencoder-models-module"></span><h2>mirapy.autoencoder.models module<a class="headerlink" href="#module-mirapy.autoencoder.models" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="mirapy.autoencoder.models.Autoencoder">
<em class="property">class </em><code class="descclassname">mirapy.autoencoder.models.</code><code class="descname">Autoencoder</code><a class="headerlink" href="#mirapy.autoencoder.models.Autoencoder" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<dl class="method">
<dt id="mirapy.autoencoder.models.Autoencoder.compile">
<code class="descname">compile</code><span class="sig-paren">(</span><em>optimizer</em>, <em>loss</em><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.Autoencoder.compile" title="Permalink to this definition">¶</a></dt>
<dd><p>Compile model with given configuration.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>optimizer</strong> – Instance of optimizer.</p></li>
<li><p><strong>loss</strong> – String (name of loss function) or custom function.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mirapy.autoencoder.models.Autoencoder.load_model">
<code class="descname">load_model</code><span class="sig-paren">(</span><em>model_name</em>, <em>path='models/'</em><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.Autoencoder.load_model" title="Permalink to this definition">¶</a></dt>
<dd><p>Loads a model from a H5py file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_name</strong> – File name.</p></li>
<li><p><strong>path</strong> – Pa</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mirapy.autoencoder.models.Autoencoder.plot_history">
<code class="descname">plot_history</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.Autoencoder.plot_history" title="Permalink to this definition">¶</a></dt>
<dd><p>Plots loss vs epoch graph.</p>
</dd></dl>
<dl class="method">
<dt id="mirapy.autoencoder.models.Autoencoder.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.Autoencoder.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predicts the output of the model for the given data as input.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Input data as Numpy arrays.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mirapy.autoencoder.models.Autoencoder.save_model">
<code class="descname">save_model</code><span class="sig-paren">(</span><em>model_name</em>, <em>path='models/'</em><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.Autoencoder.save_model" title="Permalink to this definition">¶</a></dt>
<dd><p>Saves a model into a H5py file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>model_name</strong> – File name.</p></li>
<li><p><strong>path</strong> – Pa</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mirapy.autoencoder.models.Autoencoder.summary">
<code class="descname">summary</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.Autoencoder.summary" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="mirapy.autoencoder.models.Autoencoder.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>batch_size=32</em>, <em>epochs=100</em>, <em>validation_data=None</em>, <em>shuffle=True</em>, <em>verbose=1</em><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.Autoencoder.train" title="Permalink to this definition">¶</a></dt>
<dd><p>Trains the model on the training data with given settings.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> – Numpy array of training data.</p></li>
<li><p><strong>y</strong> – Numpy array of target data.</p></li>
<li><p><strong>epochs</strong> – Integer. Number of epochs during training.</p></li>
<li><p><strong>batch_size</strong> – Number of samples per gradient update.</p></li>
<li><p><strong>validation_data</strong> – Numpy array of validation data.</p></li>
<li><p><strong>shuffle</strong> – Boolean. Shuffles the data before training.</p></li>
<li><p><strong>verbose</strong> – Value is 0, 1, or 2.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="mirapy.autoencoder.models.DeNoisingAutoencoder">
<em class="property">class </em><code class="descclassname">mirapy.autoencoder.models.</code><code class="descname">DeNoisingAutoencoder</code><span class="sig-paren">(</span><em>img_dim</em>, <em>activation='relu'</em>, <em>padding='same'</em><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.DeNoisingAutoencoder" title="Permalink to this definition">¶</a></dt>
<dd><p>Bases: <a class="reference internal" href="#mirapy.autoencoder.models.Autoencoder" title="mirapy.autoencoder.models.Autoencoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">mirapy.autoencoder.models.Autoencoder</span></code></a></p>
<dl class="method">
<dt id="mirapy.autoencoder.models.DeNoisingAutoencoder.compile">
<code class="descname">compile</code><span class="sig-paren">(</span><em>optimizer</em>, <em>loss</em><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.DeNoisingAutoencoder.compile" title="Permalink to this definition">¶</a></dt>
<dd><p>Compile model with given configuration.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>optimizer</strong> – Instance of optimizer.</p></li>
<li><p><strong>loss</strong> – String (name of loss function) or custom function.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mirapy.autoencoder.models.DeNoisingAutoencoder.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.DeNoisingAutoencoder.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predicts the output of the model for the given data as input.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>x</strong> – Input data as Numpy arrays.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mirapy.autoencoder.models.DeNoisingAutoencoder.show_image_pairs">
<code class="descname">show_image_pairs</code><span class="sig-paren">(</span><em>original_images</em>, <em>decoded_images</em>, <em>max_images</em><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.DeNoisingAutoencoder.show_image_pairs" title="Permalink to this definition">¶</a></dt>
<dd><p>Displays images in pair of images in grid form using Matplotlib.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>original_images</strong> – Array of original images.</p></li>
<li><p><strong>decoded_images</strong> – Array of decoded images.</p></li>
<li><p><strong>max_images</strong> – Integer. Set number of images in a row.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="mirapy.autoencoder.models.DeNoisingAutoencoder.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>x</em>, <em>y</em>, <em>batch_size=32</em>, <em>epochs=100</em>, <em>validation_data=None</em>, <em>shuffle=True</em>, <em>verbose=1</em><span class="sig-paren">)</span><a class="headerlink" href="#mirapy.autoencoder.models.DeNoisingAutoencoder.train" title="Permalink to this definition">¶</a></dt>
<dd><p>Trains the model on the training data with given settings.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>x</strong> – Numpy array of training data.</p></li>
<li><p><strong>y</strong> – Numpy array of target data.</p></li>
<li><p><strong>epochs</strong> – Integer. Number of epochs during training.</p></li>
<li><p><strong>batch_size</strong> – Number of samples per gradient update.</p></li>
<li><p><strong>validation_data</strong> – Numpy array of validation data.</p></li>
<li><p><strong>shuffle</strong> – Boolean. Shuffles the data before training.</p></li>
<li><p><strong>verbose</strong> – Value is 0, 1, or 2.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="module-mirapy.autoencoder">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-mirapy.autoencoder" title="Permalink to this headline">¶</a></h2>
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