Open AccessImplementation of generative adversarial networks in HPCC systems using GNN bundle
Author(s) -
Ambu Karthik,
Jyoti Shetty,
G Shobha,
Roger Dev
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i2.pp374-381
Subject(s) - mnist database , computer science , bundle , discriminator , generative grammar , generator (circuit theory) , artificial neural network , analytics , artificial intelligence , generative adversarial network , span (engineering) , machine learning , variety (cybernetics) , deep learning , data mining , telecommunications , power (physics) , materials science , physics , civil engineering , quantum mechanics , detector , engineering , composite material
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.