loss_variational: Variational loss

In fdavidcl/ruta: Implementation of Unsupervised Neural Architectures

Variational loss

Description

Specifies an evaluation function adapted to the variational autoencoder. It combines a base reconstruction error and the Kullback-Leibler divergence between the learned distribution and the true latent posterior.

Usage

loss_variational(reconstruction_loss)

Arguments

reconstruction_loss

Another loss to be used as reconstruction error (e.g. "binary_crossentropy")

Value

A "ruta_loss" object

References

• Auto-Encoding Variational Bayes

• Under the Hood of the Variational Autoencoder (in Prose and Code)

• Keras example: Variational autoencoder

See Also

⁠\link{autoencoder_variational}⁠

Other loss functions: contraction(), correntropy()

fdavidcl/ruta documentation built on July 5, 2023, 6:32 p.m.