本文選題：深度學習 + 圖像分類　； 參考：《東華理工大學》2017年碩士論文

【摘要】：近幾年以來,隨著科學計算機網絡及人工智能領域的發展,圖形圖像數據量逐漸增多,于是,如何從大量的自然圖像中快速提取到視覺特征已經成了機器智能學習中的熱點研究課題,進而對自然圖像的分類必然成為獲取自然圖像信息的研究重點。卷積神經網絡是深度學習在圖像處理方面的一個重要應用。它相比于其它機器學習算法如SVM等,其優點是能夠直接對圖像像素進行卷積并提取特征,也能夠利用海量的圖像數據將網絡參數訓練充分,以達到更好的分類效果。本文對基于深度學習的自然圖像分類方法展開研究,主要工作及創新點如下:1)基于tensorflow深度學習框架平臺設計一個用于識別圖像的淺層卷積神經網絡,并分別用單GPU和多GPU訓練加速來對比該網絡性能,其中多GPU訓練該網絡的所用的時間比單GPU縮短了25分鐘。該項工作的設計旨在建立一個較好的網絡結構進行訓練和評估,并為工作3中建立更加復雜的網絡模型做鋪墊。2)本文圍繞卷積神經網絡的網絡結構和多參數分別進行了改進和優化。研究實驗表明,對batch值、dropout、momentum動量值、數據集擴增等的優化,能夠有效地提高深層卷積神經網絡模型的識別率。因此,合理的增加網絡層數,優化訓練參數提高訓練效率,以達到最佳的分類效果是圖像分類應用研究中非常重要的目的。3)基于tensorflow深度學習框架平臺,并用GPU訓練加速來進行卷積神經網絡的網絡結構改進設計和參數優化。首先,設計一個具有9層結構的深層卷積神經網絡。其次,用該網絡結構分別對cifar-10和cifar-100等復雜圖像數據庫進行訓練、測試和優化參數。結果表明,該網絡結構相比之前研究者的網絡模型(Conv-KN)對這兩種復雜的圖像庫的分類準確率分別提高了9.26%和3.55%。tensorflow框架平臺下的深層卷積神經網絡的分類效果要明顯好于其它平臺,并且tensorflow框架平臺下的訓練時間上也得到了極大的提高。
[Abstract]:In recent years, with the development of scientific computer network and artificial intelligence, the amount of graphic and image data is increasing gradually. So how to quickly extract visual features from a large number of natural images has become a hot topic in machine intelligent learning, and then the classification of natural images will inevitably become the acquisition of natural image information. Research emphasis. Convolution neural network is an important application of deep learning in image processing. Compared with other machine learning algorithms, such as SVM, it has the advantage that it can convolution the image pixels directly and extract the features, and can also use massive image data to train the network parameters fully in order to achieve better classification results. This paper studies the classification method of natural image based on deep learning. The main work and innovation are as follows: 1) based on the tensorflow deep learning framework platform, a shallow convolution neural network for identifying images is designed, and the performance of the network is compared with single GPU and multiple GPU training respectively, in which the multi GPU is used to train the network. The time is 25 minutes shorter than the single GPU. The design of the work is designed to build a better network structure for training and evaluation, and to build a more complex network model for work 3. This paper improves and optimizes the network structure and multi parameters of the convolution neural network respectively. The research experiments show that the batch value, D The optimization of ropout, momentum momentum, and data set amplification can effectively improve the recognition rate of the deep convolution neural network model. Therefore, it is very important for the image classification application to increase the number of network layers, optimize the training parameters and improve the training efficiency, and to achieve the best classification effect in the study of the image classification application. Based on the depth of tensorflow, the.3 Study frame platform and accelerate the network structure improvement design and parameter optimization of convolution neural network with GPU training. First, a deep convolution neural network with 9 layers structure is designed. Secondly, the network structure is used to train, test and optimize the parameters of complex image databases such as cifar-10 and cifar-100, respectively. Compared with the previous researchers' network model (Conv-KN), the classification accuracy of the two complex image bases is improved by 9.26% and the deep convolution neural network under the 3.55%.tensorflow framework platform is better than the other platforms, and the training time under the tensorflow frame platform is also greatly improved. Improve.
【學位授予單位】：東華理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP18

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本文編號：1915953