load_vae: Load Pretrained VAE Decoder

In vmsae: Variational Multivariate Spatial Small Area Estimation

Load Pretrained VAE Decoder

Description

Load a pretrained Variational Autoencoder (VAE) decoder from disk. This function reads the saved PyTorch model weights and corresponding GEOID list, and constructs a Decoder S4 object with the loaded parameters.

Usage

load_vae(model_name, save_dir = NULL)

Arguments

model_name	Character. The name of the trained VAE model (without .zip extensions).
save_dir	Character. The directory where the trained VAE model is saved. Defaults to the current directory if NULL.

Details

This function assumes the model was trained and saved using train_vae(), and that the decoder weights are stored in a file compatible with torch::load() (via reticulate). It extracts the decoder input/output weights and biases, along with region GEOIDs, and returns them as an S4 object of class Decoder.

Value

An object of class Decoder, containing the decoder weights and region identifiers.

Examples

## Not run: 
library(vmsae)
# this function is time consuming for the first run
install_environment()
load_environment()
decoder <- load_vae(model_name = "mo_county")

## End(Not run)

vmsae documentation built on June 21, 2025, 9:07 a.m.