vae_loss_independent

the encoder model of the VAE, used to obtain z_mean and z_log_var from inputs

encoder

kl_weight

the number of nodes in the input/output of the VAE

rec_dim

A custom loss function for a VAE learning a standard normal distribution

Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required.

vae_loss_independent: A custom loss function for a VAE learning a standard normal distribution

Description

Usage

Arguments

Value