Skip to main content

Document details - TPU Vision Accuracy Performance analysis of Optimized Deep learning models using TensorFlow

Journal Volume 11, Issue 6, June 2022, Article 17483439 T. Tritva J Kiran, Dr. Pramod Pandurang Jadhav , " TPU Vision Accuracy Performance analysis of Optimized Deep learning models using TensorFlow" , International Journal of Application or Innovation in Engineering & Management (IJAIEM) , Volume 11, Issue 6, June 2022 , pp. 022-027 , ISSN 2319 - 4847.

TPU Vision Accuracy Performance analysis of Optimized Deep learning models using TensorFlow

    T. Tritva J Kiran, Dr. Pramod Pandurang Jadhav

Abstract

ABSTRACT Computer vision is the state-of-art of understanding and manipulating images and videos. Disclosure of this work relates to achieve and compare more accurate optimization by measuring the performance analysis of accuracy in vision for classification and for the predictions on TPU using TensorFlow2.0 Keras with GPU. The ability to process large number of features makes Deep Learning models very powerful when dealing with unstructured data. Previous work presented the work extension of testing the optimization growth on vision accuracy of the deep learning models on TPU of the TensorFlow. This work presenting the comparative growth results of using GPU and TPU of TensorFlow. All the results clearly showing great difference between previous works tested on GPU and with TPU the optimized growth performance of vision accuracy of deep learning models using various difficult datasets with the effect of QoS on the TPU of the TensorFlow. Keyword: TPU (Tensor Processing Unit), TensorFlow, Deep learning models, Optimization, Loss, Accuracy.

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

Cited by 0 documents

Related documents

{"topic":{"name":"Order Picking; AS/RS; Warehouses","id":5729,"uri":"Topic/5729","prominencePercentile":98.30173,"prominencePercentileString":"98.302","overallScholarlyOutput":0},"dig":"7972b85ca5bc948c1a2f0423f8150b186ec6bb8cf32afac11c4a324b8d78fb11"}