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Document details - Analysis of Non-Linearity Accuracy for a Deep Learning model usingGPU on TensorFlow

Journal Volume 10, Issue 4, April 2021, Article 16383215 T. Tritva Jyothi Kiran , " Analysis of Non-Linearity Accuracy for a Deep Learning model usingGPU on TensorFlow" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 10, Issue 4, April 2021 , pp. 001-007 , ISSN 2319 - 4847.

Analysis of Non-Linearity Accuracy for a Deep Learning model usingGPU on TensorFlow

    T. Tritva Jyothi Kiran

Abstract

ABSTRACT The ability to process large number of features makes deep learning very powerful when dealing with unstructured data. Still, Deep Learning algorithms can be excess for more complex problems because they require access to a vast amount of data to be effective. So many researchers analysed performance of Deep Learning models implemented using Python andin previous researchers work Linearity resulted less accuracy. To overcome these issues of accuracy, loss rate and linearity,in this paper I am showing the performance analysis ofDeep Learning modelon TensorFlow with Keras. TensorFlow emerged on top of the Pythonlibraries implemented by the Google. My model trained to classify the image on few thousand images and used Non-Linearity in network for images, and tested on TensorFlow weather the model is able to predicted accurately and successfullycompared the human brain to artificial neural networks in terms of image recognition and compared the performance analysis for accuracy of the model prediction on TensorFlow, and also compared the result with Python.Finally, TensorFlow with Non- Linearity showed high accuracy 88% on Intel® Core™ i3-7100U CPU. Keywords:TensorFlow, CNN (Convolution Neural Network), Deep Learning, Python, RELU (Rectified linear unit), Max Pooling, Flattening

  • ISSN: 23194847
  • Source Type: Journal
  • Original language: English

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