variational_block

This variational block consists in two dense layers which take as input the previous layer
and a sampling layer. More specifically, these layers aim to represent the mean and the
log variance of the learned distribution in a variational autoencoder.

Implementation of several unsupervised neural networks,
from building their architecture to their training and evaluation. Available
networks are auto-encoders including their main variants: sparse, contractive,
denoising, robust and variational, as described in Charte et al. (2018)
<doi:10.1016/j.inffus.2017.12.007>.

David Charte

ruta

Implementation of Unsupervised Neural Architectures

Francisco Charte

Francisco Herrera

variational_block function

<dl><dt>units</dt>
<dd>Number of units</dd>
<dt>epsilon_std</dt>
<dd>Standard deviation for the normal distribution used for sampling</dd>
<dt>seed</dt>
<dd>A seed for the random number generator. Setting a seed is required if you
want to save the model and be able to load it correctly</dd></dl>

Arguments

Create a variational block of layers — variational_block

<dl>

<dt>units</dt>
<dd>Number of units</dd>


<dt>epsilon_std</dt>
<dd>Standard deviation for the normal distribution used for sampling</dd>


<dt>seed</dt>
<dd>A seed for the random number generator. Setting a seed is required if you
want to save the model and be able to load it correctly</dd>

</dl>

variational_block: Create a variational block of layers

Description

Usage

Value

Arguments

See Also

Examples