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A COMPARATIVE STUDY OF FFN AND CNN WITHIN IMAGE RECOGNITION: The effects of training and accuracy of different artificial neural network designs
University of Skövde, School of Informatics.
University of Skövde, School of Informatics.
2019 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Image recognition and -classification is becoming more important as the need to be able to process large amounts of images is becoming more common. The aim of this thesis is to compare two types of artificial neural networks, FeedForward Network and Convolutional Neural Network, to see how these compare when performing the task of image recognition.

Six models of each type of neural network was created that differed in terms of width, depth and which activation function they used in order to learn. This enabled the experiment to also see if these parameters had any effect on the rate which a network learn and how the network design affected the validation accuracy of the models.

The models were implemented using the API Keras, and trained and tested using the dataset CIFAR-10. The results showed that within the scope of this experiment the CNN models were always preferable as they achieved a statistically higher validation accuracy compared to their FFN counterparts.

Place, publisher, year, edition, pages
2019. , p. 59
Keywords [en]
Machine learning, Supervised learning, FeedForward Network, Convolutional Neural Network, CIFAR-10, Keras, Activation Function
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:his:diva-17214OAI: oai:DiVA.org:his-17214DiVA, id: diva2:1327696
Educational program
Computer Science - Specialization in Systems Development
Supervisors
Examiners
Available from: 2019-06-20 Created: 2019-06-19 Last updated: 2019-06-20Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf