基于深度卷积神经网络的图像分类

SHANGHAI JIAO TONG UNIVERSITY

论文题目：基于卷积神经网络的自然图像分类技术研

究

姓名: 高小宁

专业：控制科学与工程

基于卷积神经网络的自然图像分类技术研究

摘要：卷积神经网络已在图像分类领域取得了很好的效果，但其网络结构及参数的选择对图像分类的效果和效率有较大的影响。为改善卷积网络的图像分类性能，本文对卷积神经网络模型进行了详细的理论分析，并通过大量的对比实验，得出了影响卷积网络性能的因素。结合理论分析及对比实验，本文设计了一个卷积层数为8层的深度卷积网络，并结合Batch Normalization、dropout等方法，在CIFAR-10数据集上取得了88.1%的分类精度，有效地提高了卷积神经网络的分类效果。

关键词：卷积神经网络，图像分类，Batch Normalization，Dropout

Research on Natural Image Classification Based on

Convolution Neural Network

Abstract: Convolution neural network has achieved very good results in image classification, but its network structure and the choice of parameters have a greater impact on image classification efficiency and efficiency. In order to improve the image classification performance of the convolution network, a convolutional neural network model is analyzed in detail, and a large number of contrastive experiments are conducted to get the factors that influence the performance of the convolution network. Combining the theory analysis and contrast experiment, a convolution layer depth convolution network with 8 layers is designed. Combined with Batch Normalization and dropout, 88.1% classification accuracy is achieved on CIFAR-10 dataset. Which improves the classification effect of convolution neural network.

Key Words: Convolution neural network(CNN), image classification, Batch Normalization, Dropout

目录

基于卷积神经网络的自然图像分类技术研究....................................................... - 1 -1引言.. (3)

2卷积神经网络的模型分析 (4)

2.1网络基本拓扑结构..................................................................................... - 4 -

2.2卷积和池化................................................................................................. - 5 -

2.3激活函数..................................................................................................... - 6 -

2.4 Softmax分类器与代价函数...................................................................... - 7 -

2.5学习算法..................................................................................................... - 8 -

2.6 Dropout ..................................................................................................... - 10 -

2.7 Batch Normalization ................................................................................. - 11 -3模型设计与实验分析.. (12)

3.1 CIFAR-10数据集..................................................................................... - 12 -

3.2 模型设计.................................................................................................. - 13 -

3.3 实验结果与分析...................................................................................... - 15 -4结论 (22)

参考文献 (23)