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<article id="content">
<header>
<h1 class="title">Module <code>VITAE.train</code></h1>
</header>
<section id="section-intro">
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="VITAE.train.clear_session"><code class="name flex">
<span>def <span class="ident">clear_session</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"><p>Clear Tensorflow sessions.</p></div>
</dd>
<dt id="VITAE.train.warp_dataset"><code class="name flex">
<span>def <span class="ident">warp_dataset</span></span>(<span>X_normalized, c_score, batch_size: int, X=None, scale_factor=None, conditions=None, pi_cov=None, seed=0)</span>
</code></dt>
<dd>
<div class="desc"><p>Get Tensorflow datasets.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>X_normalized</code></strong> : <code>np.array</code></dt>
<dd><span><span class="MathJax_Preview">[N, G]</span><script type="math/tex">[N, G]</script></span> The preprocessed data.</dd>
<dt><strong><code>c_score</code></strong> : <code>float</code>, optional</dt>
<dd>The normalizing constant.</dd>
<dt><strong><code>batch_size</code></strong> : <code>int</code></dt>
<dd>The batch size.</dd>
<dt><strong><code>X</code></strong> : <code>np.array</code>, optional</dt>
<dd><span><span class="MathJax_Preview">[N, G]</span><script type="math/tex">[N, G]</script></span> The raw count data.</dd>
<dt><strong><code>scale_factor</code></strong> : <code>np.array</code>, optional</dt>
<dd><span><span class="MathJax_Preview">[N, ]</span><script type="math/tex">[N, ]</script></span> The raw count data.</dd>
<dt><strong><code>seed</code></strong> : <code>int</code>, optional</dt>
<dd>The random seed for data shuffling.</dd>
<dt><strong><code>conditions</code></strong> : <code>str</code> or <code>list</code>, optional</dt>
<dd>The conditions of different cells</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>dataset</code></strong> : <code>tf.Dataset</code></dt>
<dd>The Tensorflow Dataset object.</dd>
</dl></div>
</dd>
<dt id="VITAE.train.pre_train"><code class="name flex">
<span>def <span class="ident">pre_train</span></span>(<span>train_dataset, test_dataset, vae, learning_rate: float, L: int, alpha: float, gamma: float, phi: float, num_epoch: int, num_step_per_epoch: int, es_patience: int, es_tolerance: int, es_relative: bool, verbose: bool = True)</span>
</code></dt>
<dd>
<div class="desc"><p>Pretraining.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>train_dataset</code></strong> : <code>tf.Dataset</code></dt>
<dd>The Tensorflow Dataset object.</dd>
<dt><strong><code>test_dataset</code></strong> : <code>tf.Dataset</code></dt>
<dd>The Tensorflow Dataset object.</dd>
<dt><strong><code>vae</code></strong> : <code>VariationalAutoEncoder</code></dt>
<dd>The model.</dd>
<dt><strong><code>learning_rate</code></strong> : <code>float</code></dt>
<dd>The initial learning rate for the Adam optimizer.</dd>
<dt><strong><code>L</code></strong> : <code>int</code></dt>
<dd>The number of MC samples.</dd>
<dt><strong><code>alpha</code></strong> : <code>float</code>, optional</dt>
<dd>The value of alpha in [0,1] to encourage covariate adjustment. Not used if there is no covariates.</dd>
<dt><strong><code>phi</code></strong> : <code>float</code>, optional</dt>
<dd>The weight of Jocob norm of the encoder.</dd>
<dt><strong><code>num_epoch</code></strong> : <code>int</code></dt>
<dd>The maximum number of epoches.</dd>
<dt><strong><code>num_step_per_epoch</code></strong> : <code>int</code></dt>
<dd>The number of step per epoch, it will be inferred from number of cells and batch size if it is None.</dd>
<dt><strong><code>es_patience</code></strong> : <code>int</code></dt>
<dd>The maximum number of epoches if there is no improvement.</dd>
<dt><strong><code>es_tolerance</code></strong> : <code>float</code></dt>
<dd>The minimum change of loss to be considered as an improvement.</dd>
<dt><strong><code>es_relative</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether monitor the relative change of loss or not.</dd>
<dt><strong><code>es_warmup</code></strong> : <code>int</code>, optional</dt>
<dd>The number of warmup epoches.</dd>
<dt><strong><code>conditions</code></strong> : <code>str</code> or <code>list</code></dt>
<dd>The conditions of different cells</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>vae</code></strong> : <code>VariationalAutoEncoder</code></dt>
<dd>The pretrained model.</dd>
</dl></div>
</dd>
<dt id="VITAE.train.train"><code class="name flex">
<span>def <span class="ident">train</span></span>(<span>train_dataset, test_dataset, vae, learning_rate: float, L: int, alpha: float, beta: float, gamma: float, phi: float, num_epoch: int, num_step_per_epoch: int, es_patience: int, es_tolerance: float, es_relative: bool, es_warmup: int, verbose: bool = False, pi_cov=None, **kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>Training.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>train_dataset</code></strong> : <code>tf.Dataset</code></dt>
<dd>The Tensorflow Dataset object.</dd>
<dt><strong><code>test_dataset</code></strong> : <code>tf.Dataset</code></dt>
<dd>The Tensorflow Dataset object.</dd>
<dt><strong><code>vae</code></strong> : <code>VariationalAutoEncoder</code></dt>
<dd>The model.</dd>
<dt><strong><code>learning_rate</code></strong> : <code>float</code></dt>
<dd>The initial learning rate for the Adam optimizer.</dd>
<dt><strong><code>L</code></strong> : <code>int</code></dt>
<dd>The number of MC samples.</dd>
<dt><strong><code>alpha</code></strong> : <code>float</code></dt>
<dd>The value of alpha in [0,1] to encourage covariate adjustment. Not used if there is no covariates.</dd>
<dt><strong><code>beta</code></strong> : <code>float</code></dt>
<dd>The value of beta in beta-VAE.</dd>
<dt><strong><code>gamma</code></strong> : <code>float</code></dt>
<dd>The weight of mmd_loss.</dd>
<dt><strong><code>phi</code></strong> : <code>float</code></dt>
<dd>The weight of Jacob norm of the encoder.</dd>
<dt><strong><code>num_epoch</code></strong> : <code>int</code></dt>
<dd>The maximum number of epoches.</dd>
<dt><strong><code>num_step_per_epoch</code></strong> : <code>int</code></dt>
<dd>The number of step per epoch, it will be inferred from number of cells and batch size if it is None.</dd>
<dt><strong><code>es_patience</code></strong> : <code>int</code></dt>
<dd>The maximum number of epoches if there is no improvement.</dd>
<dt><strong><code>es_tolerance</code></strong> : <code>float</code>, optional</dt>
<dd>The minimum change of loss to be considered as an improvement.</dd>
<dt><strong><code>es_relative</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether monitor the relative change of loss or not.</dd>
<dt><strong><code>es_warmup</code></strong> : <code>int</code></dt>
<dd>The number of warmup epoches.</dd>
<dt><strong><code>**kwargs</code></strong></dt>
<dd>Extra key-value arguments for dimension reduction algorithms.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>vae</code></strong> : <code>VariationalAutoEncoder</code></dt>
<dd>The trained model.</dd>
</dl></div>
</dd>
</dl>
</section>
<section>
</section>
</article>
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<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="VITAE" href="index.html">VITAE</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="VITAE.train.clear_session" href="#VITAE.train.clear_session">clear_session</a></code></li>
<li><code><a title="VITAE.train.warp_dataset" href="#VITAE.train.warp_dataset">warp_dataset</a></code></li>
<li><code><a title="VITAE.train.pre_train" href="#VITAE.train.pre_train">pre_train</a></code></li>
<li><code><a title="VITAE.train.train" href="#VITAE.train.train">train</a></code></li>
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Datasets
Models
