The VAE model is designed to sample the latent embeddings from a probability distribution. The initial VAE training process of the encoder-decoder pair was conducted in two phases in analogy with : first training the deterministic autoencoder network and later tuning the autoencoder model by adding a penalty term involving Kullback-Leibler divergence between latent vectors and standard normal distribution with a fixed penalty coefficient. As in the work of , our experiments indicate this approach as the optimal way of training.