dropout

Randomly sets a fraction <code>rate</code> of input units to 0 at each update during training
time, which helps prevent overfitting.

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

dropout function

<dl><dt>rate</dt>
<dd>The fraction of affected units</dd></dl>

Arguments

Dropout layer — dropout

<dl>

<dt>rate</dt>
<dd>The fraction of affected units</dd>

</dl>

dropout: Dropout layer

Description

Usage

Value

Arguments

See Also