  The morphology of a radio galaxy is highly affected by its central active
galactic nuclei (AGN), which is studied to reveal the evolution of the super
massive black hole (SMBH). In this work, we propose a morphology generation
framework for two typical radio galaxies namely Fanaroff-Riley type-I (FRI) and
type-II (FRII) with deep neural network based autoencoder (DNNAE) and Gaussian
mixture models (GMMs). The encoder and decoder subnets in the DNNAE are
symmetric aside a fully-connected layer namely code layer hosting the extracted
feature vectors. By randomly generating the feature vectors later with a
three-component Gaussian Mixture models, new FRI or FRII radio galaxy
morphologies are simulated. Experiments were demonstrated on real radio galaxy
images, where we discussed the length of feature vectors, selection of lost
functions, and made comparisons on batch normalization and dropout techniques
for training the network. The results suggest a high efficiency and performance
of our morphology generation framework. Code is available at:
https://github.com/myinxd/dnnae-gmm.
