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Performance Comparison Analysis of AI Supervised Learning Methods of Tensorflow and Scikit-Learn in the Writing Digit Data

필기숫자 데이터에 대한 텐서플로우와 사이킷런의 인공지능 지도학습 방식의 성능비교 분석

  • Jo, Jun-Mo (Dept. Electronic Engineering, TongMyong University)
  • 조준모 (동명대학교 전자공학과)
  • Received : 2019.05.09
  • Accepted : 2019.08.15
  • Published : 2019.08.31

Abstract

The advent of the AI(: Artificial Intelligence) has applied to many industrial and general applications have havingact on our lives these days. Various types of machine learning methods are supported in this field. The supervised learning method of the machine learning has features and targets as an input in the learning process. There are many supervised learning methods as well and their performance varies depends on the characteristics and states of the big data type as an input data. Therefore, in this paper, in order to compare the performance of the various supervised learning method with a specific big data set, the supervised learning methods supported in the Tensorflow and the Sckit-Learn are simulated and analyzed in the Jupyter Notebook environment with python.

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Fig. 1 Architecture of KSOM controller[3]

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Fig. 2 Example of the handwritten digit data

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Fig. 3 Comparing training methods with different quantity of input data

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Fig. 4 Comparing kneighbor with other methods

Table 1. Contents of the dataset

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Table 1. Euclidean distance method

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Table 2. Result of the training methods

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Acknowledgement

Supported by : Tongmyong University

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