| 结合Inception模块的卷积神经网络图像分类方法 |
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| 引用本文： | 齐广华,何明祥.结合Inception模块的卷积神经网络图像分类方法[J].教育技术导刊,2020,19(3):79-82. |
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| 作者姓名： | 齐广华 何明祥 |
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| 作者单位： | 山东科技大学 计算机科学与工程学院，山东 青岛 266590 |
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| 摘 要： | 针对现有卷积神经网络模型参数量大、训练时间长的问题，提出了一种结合VGG模型和Inception模块特点的网络模型。该模型通过结合两种经典模型的特点，增加网络模型的宽度和深度，使用较小的卷积核和较多的非线性激活，在减少参数量的同时增加了网络特征提取能力，同时利用全局平均池化层替代全连接层，避免全连接层参数过多容易导致的过拟合问题。在MNIST和CIFAR-10数据集上的实验结果表明，该方法在MNIST数据集上的准确率达到了99.76%，在CIFAR-10数据集上的准确率相比传统卷积神经网络模型提高了6%左右。
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| 关 键 词： | 卷积神经网络 Inception模块 全局平均池化 卷积核 图像分类 |
| 收稿时间： | 2019-11-14 |
Convolutional Neural Network Image Classification Method Combined with Inception Module |
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| QI Guang-hua,HE Ming-xiang.Convolutional Neural Network Image Classification Method Combined with Inception Module[J].Introduction of Educational Technology,2020,19(3):79-82. |
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| Authors: | QI Guang-hua HE Ming-xiang |
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| Institution: | College of Computer Science and Engineering， Shandong University of Science and Technology， Qingdao 266590， China |
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| Abstract: | Aiming at the problem that the existing convolutional neural network model has large parameters and long training time， we propose a network model combining VGG model and inception module. By combining the characteristics of the two classical models， the model increases the width and depth of the network model， uses a smaller convolution kernel and more nonlinear activation， and increases the network feature extraction ability while reducing the parameter quantity. The average pooling layer replaces the fully connected layer， avoiding the over-fitting problem that is easily caused by too many parameters of the full-connected layer. Experimental results on the MNIST and CIFAR-10 datasets show that the accuracy of this method on the MNIST dataset is 99.76%. The accuracy on the CIFAR-10 dataset is about 6% higher than the traditional convolutional neural network model. |
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| Keywords: | convolutional neural network inception module global average pooling convolution kernel image classification |
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