activation_glu

The GLU activation function is defined as:
<code>glu(x) = a * sigmoid(b)</code>,
where <code>x</code> is split into two equal parts <code>a</code> and <code>b</code> along the given axis.

Interface to 'Keras' <https://keras.io>, a high-level neural
networks API. 'Keras' was developed with a focus on enabling fast experimentation,
supports both convolution based networks and recurrent networks (as well as
combinations of the two), and runs seamlessly on both CPU and GPU devices.

Tomasz Kalinowski

keras3

R Interface to 'Keras'

Daniel Falbel

JJ Allaire

François Chollet

Posit Software, PBC 

Google 

Yuan Tang

Wouter Van Der Bijl

Martin Studer

Sigrid Keydana

activation_glu function

<dl><dt>x</dt>
<dd>Input tensor.</dd>
<dt>axis</dt>
<dd>The axis along which to split the input tensor. Defaults to <code>-1</code>.</dd></dl>

Arguments

<ul>
<li><a href="https://arxiv.org/abs/1612.08083">Dauphin et al., 2017</a></li>
</ul>

Reference

Gated Linear Unit (GLU) activation function. — activation_glu

<dl>

<dt>x</dt>
<dd>Input tensor.</dd>


<dt>axis</dt>
<dd>The axis along which to split the input tensor. Defaults to <code>-1</code>.</dd>

</dl>

<ul>
<li><a href='https://arxiv.org/abs/1612.08083'>Dauphin et al., 2017</a></li>
</ul>

activation_glu: Gated Linear Unit (GLU) activation function.

Description

Usage

Value

Arguments

Reference

See Also