Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Convolutional Neural Network

Nowadays, deep learning can be employed to a wide ranges of fields including medicine, engineering, etc. In deep learning, Convolutional Neural Network (CNN) is extensively used in the pattern and sequence recognition, video analysis, natural language processing, spam detection, topic categorization...

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Bibliographic Details
Published in:arXiv.org 2018-11
Main Authors: Rezoana, Bente Arif, Md Abu Bakr Siddique, Mohammad Mahmudur Rahman Khan, Mahjabin Rahman Oishe
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Back propagation
Digits
Engineering education
Face recognition
Handwriting
Handwriting recognition
Image classification
Image detection
Image segmentation
Impact analysis
Machine learning
Natural language processing
Neural networks
Object recognition
Pattern recognition
Regression analysis
Robotics
Speech recognition
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Summary:Nowadays, deep learning can be employed to a wide ranges of fields including medicine, engineering, etc. In deep learning, Convolutional Neural Network (CNN) is extensively used in the pattern and sequence recognition, video analysis, natural language processing, spam detection, topic categorization, regression analysis, speech recognition, image classification, object detection, segmentation, face recognition, robotics, and control. The benefits associated with its near human level accuracies in large applications lead to the growing acceptance of CNN in recent years. The primary contribution of this paper is to analyze the impact of the pattern of the hidden layers of a CNN over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the Modified National Institute of Standards and Technology (MNIST) dataset. Also, is to observe the variations of accuracies of the network for various numbers of hidden layers and epochs and to make comparison and contrast among them. The system is trained utilizing stochastic gradient and backpropagation algorithm and tested with feedforward algorithm.
ISSN:2331-8422
DOI:10.48550/arxiv.1809.06187