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<section id="module-deepof.models">
<span id="deepof-models"></span><h1>deepof.models<a class="headerlink" href="#module-deepof.models" title="Permalink to this heading"></a></h1>
<p>deep autoencoder models for unsupervised pose detection.</p>
<ul class="simple">
<li><p>VQ-VAE: a variational autoencoder with a vector quantization latent-space (<a class="reference external" href="https://arxiv.org/abs/1711.00937">https://arxiv.org/abs/1711.00937</a>).</p></li>
<li><p>VaDE: a variational autoencoder with a Gaussian mixture latent-space.</p></li>
<li><p>Contrastive: an embedding model consisting of a single encoder, trained using a contrastive loss.</p></li>
</ul>
<p class="rubric">Functions</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deepof.models.get_TCN_decoder.html#deepof.models.get_TCN_decoder" title="deepof.models.get_TCN_decoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_TCN_decoder</span></code></a>(input_shape, latent_dim[, ...])</p></td>
<td><p>Return a Temporal Convolutional Network (TCN) decoder.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deepof.models.get_TCN_encoder.html#deepof.models.get_TCN_encoder" title="deepof.models.get_TCN_encoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_TCN_encoder</span></code></a>(input_shape, ...[, use_gnn, ...])</p></td>
<td><p>Return a Temporal Convolutional Network (TCN) encoder.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deepof.models.get_recurrent_decoder.html#deepof.models.get_recurrent_decoder" title="deepof.models.get_recurrent_decoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_recurrent_decoder</span></code></a>(input_shape, latent_dim)</p></td>
<td><p>Return a recurrent neural decoder.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deepof.models.get_recurrent_encoder.html#deepof.models.get_recurrent_encoder" title="deepof.models.get_recurrent_encoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_recurrent_encoder</span></code></a>(input_shape, ...[, ...])</p></td>
<td><p>Return a deep recurrent neural encoder.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deepof.models.get_transformer_decoder.html#deepof.models.get_transformer_decoder" title="deepof.models.get_transformer_decoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transformer_decoder</span></code></a>(input_shape, latent_dim)</p></td>
<td><p>Build a Transformer decoder.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deepof.models.get_transformer_encoder.html#deepof.models.get_transformer_encoder" title="deepof.models.get_transformer_encoder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_transformer_encoder</span></code></a>(input_shape, ...[, ...])</p></td>
<td><p>Build a Transformer encoder.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deepof.models.get_vade.html#deepof.models.get_vade" title="deepof.models.get_vade"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_vade</span></code></a>(input_shape, edge_feature_shape, ...)</p></td>
<td><p>Build a Gaussian mixture variational autoencoder (VaDE) model, adapted to the DeepOF setting.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deepof.models.get_vqvae.html#deepof.models.get_vqvae" title="deepof.models.get_vqvae"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_vqvae</span></code></a>(input_shape, edge_feature_shape, ...)</p></td>
<td><p>Build a Vector-Quantization variational autoencoder (VQ-VAE) model, adapted to the DeepOF setting.</p></td>
</tr>
</tbody>
</table>
<p class="rubric">Classes</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="deepof.models.Classifier.html#deepof.models.Classifier" title="deepof.models.Classifier"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Classifier</span></code></a>(*args, **kwargs)</p></td>
<td><p>Classifier for supervised pose motif elucidation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deepof.models.Contrastive.html#deepof.models.Contrastive" title="deepof.models.Contrastive"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Contrastive</span></code></a>(*args, **kwargs)</p></td>
<td><p>Self-supervised contrastive embeddings.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deepof.models.GaussianMixtureLatent.html#deepof.models.GaussianMixtureLatent" title="deepof.models.GaussianMixtureLatent"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GaussianMixtureLatent</span></code></a>(*args, **kwargs)</p></td>
<td><p>Gaussian Mixture probabilistic latent space model.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deepof.models.VQVAE.html#deepof.models.VQVAE" title="deepof.models.VQVAE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VQVAE</span></code></a>(*args, **kwargs)</p></td>
<td><p>VQ-VAE model adapted to the DeepOF setting.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="deepof.models.VaDE.html#deepof.models.VaDE" title="deepof.models.VaDE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VaDE</span></code></a>(*args, **kwargs)</p></td>
<td><p>Gaussian Mixture Variational Autoencoder for pose motif elucidation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="deepof.models.VectorQuantizer.html#deepof.models.VectorQuantizer" title="deepof.models.VectorQuantizer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VectorQuantizer</span></code></a>(*args, **kwargs)</p></td>
<td><p>Vector quantizer layer.</p></td>
</tr>
</tbody>
</table>
</section>
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