Reconstruction accuracy and clustering of test set embeddings are both useful metrics to ensure that model training is proceeding as expected; however, a more important question for a generative model is whether it is capable of accurately recapitulating the underlying distribution of the data and whether the resultant overall distribution associated with the latent space is appropriate for novel data generation. In this section, we demonstrate the capacity of the model to generate unique and diverse AMP sequences in localized portions of the latent space.