影像模糊方法在蝴蝶辨識神經網路中之應用

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姓名

李政瑜(Cheng-Yu Lee) 查詢紙本館藏

畢業系所

數學系

論文名稱

影像模糊方法在蝴蝶辨識神經網路中之應用
(Application of Image Blurring Method in Butterfly Identification Neural Network)

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至系統瀏覽論文 ( 永不開放)

摘要(中)

本研究旨在探討深度學習模型”多層感知神經網路”及”卷積神經網路”在圖像辨識上的訓練結果比較，也研讀當今常用的優化算法，熟知損失函數對參數更新的影響關係，兩個模型訓練將以蝴蝶圖片進行實作。
經由線上開放的圖片庫網站，取得9141張共五類蝴蝶，並自製成數據樣本集，分別帶入兩個深度模型，透過自行建立隱藏層結構，觀察兩者訓練時間及訓練準確率，在迭代結果上分析擬合情形。而後再進一步引入PCA降維方法，對數據預處理，看看對於圖片背景降維效果能否提高訓練或驗證準確度。

摘要(英)

The goal of this thesis is to explore the training results of two deep learning models "multilayer perceptual neural network" and "convolution neural network" in image recognition, and study the popular optimization algorithms. To this topic, we study the influence of loss functions on iterative parameters, and demonstrate two models with the pictures of butterflies.
There are five types of butterflies with 9141 pictures obtained through the online database website. We use these pictures for the files of data samples to two deep learning models, and observe the training time and accuracy by establishing the hidden layers. Then, we analyze the fitting situation to the iterative results. Finally, by using principle component analysis(PCA) in dimension reduction method, we preprocess the data and observe the reduction effect of the image background so that we can improve the training or verification accuracy.

關鍵字(中)

★ 神經網路

關鍵字(英)

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 v
圖目錄 vi
一、緒論 1
1-1 研究動機： 1
1-2 研究目的： 1
1-3 研究問題： 2
1-4 研究方法： 2
1-5 研究對象: 2
二、深度神經模型 3
2-1 前饋神經網路(Feedforward Neural Network) 3
2-2 CNN卷積神經網路 9
2-3 優化算法 11
三、系統環境與功能 15
3-1系統環境 15
3-2系統功能 16
四、數據處理與訓練 19
4-1 數據集製作 19
4-2 數據標準化 20
4-3 PCA數據降維 20
4-4 驗證模型準確率 24
五、結果討論與未來展望 33
5-1 結果討論 33
5-2 未來展望 35
參考文獻： 36

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指導教授

洪盟凱(Meng-Kai Hong)

審核日期

2018-10-30

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