1. Evrişimli Sinir Ağı Kullanarak El yazısı Rakamların Tanımasında Hiper Parametre Analizi.
- Author
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Yiğit, Tuncay, Atmaca, Şerafettin, Gürfidan, Remzi, and Çolak, Recep
- Abstract
Recognition of handwritten digits has recently gained importance and attracted the attention of many scientists, as it is used in many machine learning, deep learning and computer vision applications. Hyperparameter optimization involves determining a set of values aimed at increasing accuracy in both classification and prediction. It is also aimed to optimize the performance in feature selection by regulating the parameters selected by the algorithms more accurately. In this study, a convolutional neural network was used to recognize handwritten digits using the MNIST dataset. There are many open source hyperparameter libraries that deep learning developers can use to determine hyperparameters. In the developed model, hyperparameter optimization techniques were applied using Optuna, HyperOpt and Scikitoptimize libraries and the results were evaluated. Optimization times for hyperparameter libraries and the change in the success rate in recognizing handwritten digits were analyzed. The model trained with randomly given parameters achieved 78.45%, 97.13%, 75.62%, 76.95%, 97.46% and 97.27% accuracy, while the model trained with optimized hyperparameters achieved 99.26% accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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