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models.html
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models.html
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<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.models</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .imports import *
def bn_drop_lin(inp, n_out, bn=True, p=0.0, actn=None):
out = inp
if bn:
out = keras.layers.BatchNormalization()(out)
if p > 0:
out = keras.layers.Dropout(p)(out)
use_bias = False if bn else True
out = keras.layers.Dense(n_out, activation=actn, use_bias=use_bias)(out)
return out</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.models.bn_drop_lin"><code class="name flex">
<span>def <span class="ident">bn_drop_lin</span></span>(<span>inp, n_out, bn=True, p=0.0, actn=None)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def bn_drop_lin(inp, n_out, bn=True, p=0.0, actn=None):
out = inp
if bn:
out = keras.layers.BatchNormalization()(out)
if p > 0:
out = keras.layers.Dropout(p)(out)
use_bias = False if bn else True
out = keras.layers.Dense(n_out, activation=actn, use_bias=use_bias)(out)
return out</code></pre>
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</dd>
</dl>
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<section>
</section>
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<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain" href="index.html">ktrain</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="ktrain.models.bn_drop_lin" href="#ktrain.models.bn_drop_lin">bn_drop_lin</a></code></li>
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