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A Study on Reliability Analysis According to the Number of Training Data and the Number of Training

Korean Journal of Artificial Intelligence / Korean Journal of Artificial Intelligence, (E)2508-7894
2017, v.5 no.1, pp.29-37
https://doi.org/https://doi.org/10.24225/kjai.2017.5.2.29
Kim, Sung Hyeock
Oh, Sang Jin
Yoon, Geun Young
Kim, Wan
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Abstract

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the Gradient Descent Optimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

keywords
Overfitting, Deep-learning, Tensorflow, Mnist dataset, Artificial Intelligence

Korean Journal of Artificial Intelligence