evaluation_metric

Must be either a metric function defined by Keras (e.g.
<code>keras::metric_binary_crossentropy</code>) or a valid function for Keras to
create a performance metric (see <code><a rd-options="keras" href="/link/metric_binary_accuracy?package=ruta&version=1.0.2&to=keras" data-mini-rdoc="keras::metric_binary_accuracy">metric_binary_accuracy</a></code>
for details)

evaluate_f

Create a different evaluation metric from a valid Keras metric

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

evaluation_metric function

Must be either a metric function defined by Keras (e.g.
<code>keras::metric_binary_crossentropy</code>) or a valid function for Keras to
create a performance metric (see <code><a rd-options='keras' href='metric_binary_accuracy'>metric_binary_accuracy</a></code>
for details)

evaluation_metric: Custom evaluation metrics

Description

Usage

Arguments

Value

See Also