Implementation of generative adversarial networks in HPCC systems using GNN bundle

Ambu Karthik (R. V. College of Engineering)
Jyoti Shetty (R. V. College of Engineering)
Shobha G. (R. V. College of Engineering)
Roger Dev (Lexis Nexis Risk Solutions)

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 10, No 2: June 2021

HPCC systems, an open source cluster computing platform for big data analytics consists of generalized neural network bundle with a wide variety of features which can be used for various neural network applications. To enhance the functionality of the bundle, this paper proposes the design and development of generative adversarial networks (GANs) on HPCC systems platform using ECL, a declarative language on which HPCC systems works. GANs have been developed on the HPCC platform by defining the generator and discriminator models separately, and training them by batches in the same epoch. In order to make sure that they train as adversaries, a certain weights transfer methodology was implemented. MNIST dataset which has been used to test the proposed approach has provided satisfactory results. The results obtained were unique images very similar to the MNIST dataset, as it were expected.