博碩士論文 106522091 詳細資訊




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姓名 劉文誠(Wen-Cheng Liu)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 可用於自動訓練深度學習網路的網頁服務
(A Web Service for Automatic Deep Learning Model Generation)
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摘要(中) 隨著科技的進步,深度學習改變了許多工業的生產方式,例如用於檢測缺陷、辨識物件等。其中位於最核心的網路模型,是演算法的核心,也是經過大數據訓練之後的精華。但是,對於大多數業者來說,如何從零開始訓練出一個可用的模型,是在生產線上導入人工智慧的一大難關。如何快速且簡單的完成一個可用的深度學習模型,就成了一個大多數非資工領域的作業人員想要了解的議題。
通常要訓練一個準確度高的深度學習模型,除了需要大量的資料外,也需要複雜的網路架構,並經過無數次的微調後,才能完成。資料的取得對於產線作業人員而言相對容易,網路架構則需要花時間了解細節,並不是一時半刻可以完成的,也使得入門的門檻相對提高許多,不利於各類工廠進行產業升級。
本研究結合了深度模型套件Keras及客戶端與伺服器端的網頁語言,提供一個可以快速訓練的深度學習模型的網頁端人工智慧系統。本系統可以讓使用者透過圖形介面設定參數並上傳訓練資料,讓非資工領域的使用者不需要花太多時間了解深度學習的細節也能快速訓練出所需的模型。
摘要(英) As technology advances, deep learning has changed the way many industries produce, such as detecting defects, identifying objects, and so on. The core network model is the core of the algorithm and the essence of the training after big data. However, for most operators, how to train a usable model from scratch is a major difficulty in introducing artificial intelligence on the production line. How to quickly and easily complete a usable deep learning model becomes an issue that most non-employed workers want to know.
Usually, training a highly accurate deep learning model requires a complex network architecture in addition to a large amount of data, and can be completed after numerous fine-tuning. The acquisition of data is relatively easy for the production line operators, and the network architecture needs to take time to understand the details. It is not completed in a moment and a half, and the threshold for entry is relatively improved, which is not conducive to industrial upgrading of various factories.
This study combines the deep learning model suite Keras with the web language of the client and server side to provide a web-based artificial intelligence system that can quickly train deep learning models. The system allows users to set parameters and upload training data through the graphical interface, so that users in non-employed fields can quickly train the required models without spending too much time on the details of deep learning.
關鍵字(中) ★ 深度學習
★ 程式碼生成
★ 網頁應用程式
關鍵字(英)
論文目次 中文摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
圖目錄 ix
表目錄 xii
一、 緒論 1
1-1 前言 1
1-2 研究目的 1
1-3 研究成果 2
1-4 論文架構 2
二、 研究背景及文獻探討 3
2-1 網頁前端 3
2-1-1 HTML 3
2-1-2 PHP 3
2-1-3 JavaScript 3
2-1-4 Apache HTTP伺服器 3
2-2 訓練後端 4
2-2-1 NASNet 4
2-2-2 DenseNet 5
2-2-3 Xception 6
2-2-4 ResNet 7
2-3 背景探討 8
2-3-1 視覺化程式編輯 8
2-3-2 利用模塊生成網路 9
2-3-3 動態網頁生成 10
2-3-4 基於流的訓練系統 11
三、 方法說明 12
3-1 系統架構 12
3-2 模塊設定 12
3-2-1 卷積層 13
3-2-2 池化層 14
3-2-3 扁平層 15
3-2-4 全連接層 16
3-2-5 Dropout層 16
3-3 資料傳遞 17
3-4 網路架構生成 19
3-5 深度學習網路訓練 22
3-6 測試及取得模型 23
四、 實驗 25
4-1 實驗配備 25
4-2 系統可靠性驗證 25
4-3 實驗資料集介紹 27
4-3-1 MNIST手寫資料集 27
4-3-2 工業焊點資料集 27
4-3-3 一維與二維條碼資料集 28
4-4 實驗結果 29
4-4-1 MNIST手寫資料集 29
4-4-2 工業焊點資料集 30
4-4-3 一維與二維條碼資料集 31
4-5 實驗驗證 32
4-6 實驗結論 33
4-7 使用者分析 33
4-7-1 本科系學生 33
4-7-2 非本科系學生 34
五、 結論與未來展望 36
5-1 結論 36
5-2 未來展望 36
六、 參考文獻 37
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[5] G. Huang, Z. Liu, K. Q. Weinberger, and L. Maaten. Densely connected convolutional networks. In CVPR, 2017.
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[19] Francois Chollet et al. Keras. Retrieved June 16, 2019, from https://keras.io/
[20] A. Krizhevsky. Learning multiple layers of features from tiny images. Tech Report, 2009.
[21] The CIFAR-10 dataset. Retrieved June 16, 2019, from https://www.cs.toronto.edu/~kriz/cifar.html
[22] Spyder Website. Retrieved June 16, 2019, from https://www.spyder-ide.org/
[23] Node.js. Retrieved June 16, 2019, from https://nodejs.org/

[24] Mohorovicic, S. "Implementing responsive web design for enhanced web presence." Information & Communication Technology Electronics & Microelectronics (MIPRO), 2013 36th International Convention on. IEEE, 2013.
[25] Myers, Brad A. "Taxonomies of visual programming and program visualization." Journal of Visual Languages & Computing 1.1 (1990): 97-123.
[26] Weigert, Thomas, et al. "Automated code generation for industrial-strength systems." Computer Software and Applications, 2008. COMPSAC′′08. 32nd Annual IEEE International. IEEE, 2008.
指導教授 栗永徽 審核日期 2019-8-12
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