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)
Network architecture as a
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?
A construct of class