I found the variational_layers gives the posterior log variances for the latent variables from the last encoding layer through a dense layer link. However, this dense in the variational_layers is not saved after training and thus users cannot obtain the latent posterior log variances or samples.
The example provided only uses the posterior means to decode to smiles. Can you actually encode smiles to posterior samples rather than posterior means after training?
I found the
variational_layersgives the posterior log variances for the latent variables from the last encoding layer through a dense layer link. However, this dense in thevariational_layersis not saved after training and thus users cannot obtain the latent posterior log variances or samples.The example provided only uses the posterior means to decode to smiles. Can you actually encode smiles to posterior samples rather than posterior means after training?