A variational autoencoder assumes that a latent, unobserved random variable produces the observed data and attempts to approximate its distribution. This function constructs a wrapper for a variational autoencoder using a Gaussian distribution as the prior of the latent space.

autoencoder_variational(network, loss = "binary_crossentropy",
auto_transform_network = TRUE)

## Arguments

network Network architecture as a "ruta_network" object (or coercible) Reconstruction error to be combined with KL divergence in order to compute the variational loss Boolean: convert the encoding layer into a variational block if none is found?

## Value

A construct of class "ruta_autoencoder"

## References

Other autoencoder variants: autoencoder_contractive, autoencoder_denoising, autoencoder_robust, autoencoder_sparse, autoencoder
network <-
learner <- autoencoder_variational(network, loss = "binary_crossentropy")