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+<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
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+<main>
+<article id="content">
+<header>
+<h1 class="title">Package <code>multilearn</code></h1>
+</header>
+<section id="section-intro">
+</section>
+<section>
+<h2 class="section-title" id="header-submodules">Sub-modules</h2>
+<dl>
+<dt><code class="name"><a title="multilearn.datasets" href="datasets.html">multilearn.datasets</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt><code class="name"><a title="multilearn.models" href="models.html">multilearn.models</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt><code class="name"><a title="multilearn.plots" href="plots.html">multilearn.plots</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+<dt><code class="name"><a title="multilearn.utils" href="utils.html">multilearn.utils</a></code></dt>
+<dd>
+<div class="desc"></div>
+</dd>
+</dl>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3><a href="#header-submodules">Sub-modules</a></h3>
+<ul>
+<li><code><a title="multilearn.datasets" href="datasets.html">multilearn.datasets</a></code></li>
+<li><code><a title="multilearn.models" href="models.html">multilearn.models</a></code></li>
+<li><code><a title="multilearn.plots" href="plots.html">multilearn.plots</a></code></li>
+<li><code><a title="multilearn.utils" href="utils.html">multilearn.utils</a></code></li>
+</ul>
+</li>
+</ul>
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diff --git a/docs/multilearn/models.html b/docs/multilearn/models.html
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+<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
+</head>
+<body>
+<main>
+<article id="content">
+<header>
+<h1 class="title">Module <code>multilearn.models</code></h1>
+</header>
+<section id="section-intro">
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">from torch import nn
+
+
+class MultiNet(nn.Module):
+    &#39;&#39;&#39;
+    A general model for building multi-target learning NNs.
+    Each separation of layers is symmetric across input datasets.
+    &#39;&#39;&#39;
+
+    def __init__(
+                 self,
+                 input_arch={},
+                 mid_arch={64: 1, 32: 1},
+                 out_arch={},
+                 tasks=[0],
+                 ):
+
+        super(MultiNet, self).__init__()
+
+        def make_layers(arch, is_out=False):
+
+            hidden = nn.ModuleList()
+            for neurons, layers in arch.items():
+                for i in range(layers):
+                    hidden.append(nn.LazyLinear(neurons))
+                    hidden.append(nn.LeakyReLU())
+
+            if is_out:
+                hidden.append(nn.LazyLinear(1))
+
+            hidden = nn.Sequential(*hidden)
+
+            return hidden
+
+        def separate(arch, tasks, is_out=False):
+
+            separate = nn.ModuleDict()
+            for t in tasks:
+                i = make_layers(arch, is_out)
+                separate[t] = i
+
+            return separate
+
+        self.input = separate(input_arch, tasks)
+        self.mid = make_layers(mid_arch)
+        self.out = separate(out_arch, tasks, True)
+
+    def forward(self, x, prop):
+        &#39;&#39;&#39;
+        Use a model to predict.
+
+        Args:
+            x (nn.tensor): The features.
+            prop: The property to predict.
+
+        Returns:
+            torch.FloatTensor: The predicted target value.
+        &#39;&#39;&#39;
+
+        for i in self.input[prop]:
+            x = i(x)
+
+        for i in self.mid:
+            x = i(x)
+
+        for i in self.out[prop]:
+            x = i(x)
+
+        return x</code></pre>
+</details>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+</section>
+<section>
+<h2 class="section-title" id="header-classes">Classes</h2>
+<dl>
+<dt id="multilearn.models.MultiNet"><code class="flex name class">
+<span>class <span class="ident">MultiNet</span></span>
+<span>(</span><span>input_arch={}, mid_arch={64: 1, 32: 1}, out_arch={}, tasks=[0])</span>
+</code></dt>
+<dd>
+<div class="desc"><p>A general model for building multi-target learning NNs.
+Each separation of layers is symmetric across input datasets.</p>
+<p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">class MultiNet(nn.Module):
+    &#39;&#39;&#39;
+    A general model for building multi-target learning NNs.
+    Each separation of layers is symmetric across input datasets.
+    &#39;&#39;&#39;
+
+    def __init__(
+                 self,
+                 input_arch={},
+                 mid_arch={64: 1, 32: 1},
+                 out_arch={},
+                 tasks=[0],
+                 ):
+
+        super(MultiNet, self).__init__()
+
+        def make_layers(arch, is_out=False):
+
+            hidden = nn.ModuleList()
+            for neurons, layers in arch.items():
+                for i in range(layers):
+                    hidden.append(nn.LazyLinear(neurons))
+                    hidden.append(nn.LeakyReLU())
+
+            if is_out:
+                hidden.append(nn.LazyLinear(1))
+
+            hidden = nn.Sequential(*hidden)
+
+            return hidden
+
+        def separate(arch, tasks, is_out=False):
+
+            separate = nn.ModuleDict()
+            for t in tasks:
+                i = make_layers(arch, is_out)
+                separate[t] = i
+
+            return separate
+
+        self.input = separate(input_arch, tasks)
+        self.mid = make_layers(mid_arch)
+        self.out = separate(out_arch, tasks, True)
+
+    def forward(self, x, prop):
+        &#39;&#39;&#39;
+        Use a model to predict.
+
+        Args:
+            x (nn.tensor): The features.
+            prop: The property to predict.
+
+        Returns:
+            torch.FloatTensor: The predicted target value.
+        &#39;&#39;&#39;
+
+        for i in self.input[prop]:
+            x = i(x)
+
+        for i in self.mid:
+            x = i(x)
+
+        for i in self.out[prop]:
+            x = i(x)
+
+        return x</code></pre>
+</details>
+<h3>Ancestors</h3>
+<ul class="hlist">
+<li>torch.nn.modules.module.Module</li>
+</ul>
+<h3>Methods</h3>
+<dl>
+<dt id="multilearn.models.MultiNet.forward"><code class="name flex">
+<span>def <span class="ident">forward</span></span>(<span>self, x, prop) ‑> Callable[..., Any]</span>
+</code></dt>
+<dd>
+<div class="desc"><p>Use a model to predict.</p>
+<h2 id="args">Args</h2>
+<dl>
+<dt><strong><code>x</code></strong> :&ensp;<code>nn.tensor</code></dt>
+<dd>The features.</dd>
+<dt><strong><code>prop</code></strong></dt>
+<dd>The property to predict.</dd>
+</dl>
+<h2 id="returns">Returns</h2>
+<dl>
+<dt><code>torch.FloatTensor</code></dt>
+<dd>The predicted target value.</dd>
+</dl></div>
+<details class="source">
+<summary>
+<span>Expand source code</span>
+</summary>
+<pre><code class="python">def forward(self, x, prop):
+    &#39;&#39;&#39;
+    Use a model to predict.
+
+    Args:
+        x (nn.tensor): The features.
+        prop: The property to predict.
+
+    Returns:
+        torch.FloatTensor: The predicted target value.
+    &#39;&#39;&#39;
+
+    for i in self.input[prop]:
+        x = i(x)
+
+    for i in self.mid:
+        x = i(x)
+
+    for i in self.out[prop]:
+        x = i(x)
+
+    return x</code></pre>
+</details>
+</dd>
+</dl>
+</dd>
+</dl>
+</section>
+</article>
+<nav id="sidebar">
+<h1>Index</h1>
+<div class="toc">
+<ul></ul>
+</div>
+<ul id="index">
+<li><h3>Super-module</h3>
+<ul>
+<li><code><a title="multilearn" href="index.html">multilearn</a></code></li>
+</ul>
+</li>
+<li><h3><a href="#header-classes">Classes</a></h3>
+<ul>
+<li>
+<h4><code><a title="multilearn.models.MultiNet" href="#multilearn.models.MultiNet">MultiNet</a></code></h4>
+<ul class="">
+<li><code><a title="multilearn.models.MultiNet.forward" href="#multilearn.models.MultiNet.forward">forward</a></code></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</nav>
+</main>
+<footer id="footer">
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
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