SOFTWARE FRAMEWORKS FOR ARTIFICIAL INTELLIGENCE: COMPARSION OF LOW-LEVEL AND HIGH-LEVEL APPROACHES

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1 Citation (Scopus)

Abstract

As nearly every artificial intelligence application is based on a framework, using the best fitting one for the task is key in developing an efficient solution quickly. Since there are two main types of frameworks, based on low and high abstraction level approaches, these two types will get compared and evaluated throughout this paper using Tensorflow and Keras as representatives. Key features of artificial intelligence frameworks for industrial applications are performance, expandability, abstraction level and therefore ease of use for rapid prototyping. All those features are major factors to keep development time and costs as low as possible, while maximizing product quality. To evaluate both approaches by these criteria a neural network classifying handwritten digits is implemented.

Original languageEnglish
Title of host publication31st European Modeling and Simulation Symposium, EMSS 2019
EditorsMichael Affenzeller, Agostino G. Bruzzone, Francesco Longo, Guilherme Pereira
Pages96-102
Number of pages7
ISBN (Electronic)9788885741263
Publication statusPublished - 2019
EventProceedings of the 31st European Modeling and Simulation Symposium EMSS2019 - Lissabon, Portugal
Duration: 17 Sep 201920 Sep 2019

Publication series

Name31st European Modeling and Simulation Symposium, EMSS 2019

Conference

ConferenceProceedings of the 31st European Modeling and Simulation Symposium EMSS2019
Country/TerritoryPortugal
CityLissabon
Period17.09.201920.09.2019

Keywords

  • Evaluation
  • Keras
  • Neural network
  • Tensorflow

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