The loss function consists of the reconstruction loss, which is modelled by mean squared error and the KL-divergence loss. Both these losses are scaled with different parameters. The choice of these functions is standard for a VAE. We note that the dice loss can also be used for parts of our matrix given the binary nature of our input. The difference in performance was negligible. MSE is scaled by a factor of 28.8 and KLD is scaled by a factor of 1.13. ADAM optimizer was used with an initial learning rate of 0.0005 and the inbuilt scheduler ReduceLROnPlateau with patience = 3 was also used. We train our model for 200 epochs.