深度学习图像分类研究：基于CNN的CIFAR-10数据集实验

深度学习图像分类研究：基于CNN的CIFAR-10数据集实验

摘要：

深度学习近年来在图像处理领域中得到了广泛应用。本文基于深度学习算法，探究了图像分类问题。首先介绍了传统图像分类方法的不足，然后详细介绍了卷积神经网络（CNN）及其变种模型。接着，通过TensorFlow框架实现了一个基于CNN的图像分类模型，并利用CIFAR-10数据集进行了实验。实验结果表明，基于CNN的图像分类模型能够有效地提高图像分类的准确性。

**关键词：**深度学习；图像分类；卷积神经网络；TensorFlow

Abstract:

Deep learning has been widely used in image processing in recent years. Based on deep learning algorithms, this paper explores the problem of image classification. Firstly, the shortcomings of traditional image classification methods are introduced, and then the convolutional neural network (CNN) and its variant models are explained in detail. Next, a CNN-based image classification model is implemented using the TensorFlow framework, and experiments are conducted using the CIFAR-10 dataset. The experimental results show that the CNN-based image classification model can effectively improve the accuracy of image classification.

Keywords: Deep learning; Image classification; Convolutional neural network; TensorFlow

目录

1. 引言
2. 传统图像分类方法的不足
3. 卷积神经网络及其变种模型
4. 基于CNN的图像分类模型 4.1 数据预处理 4.2 网络结构设计 4.3 训练模型
5. 实验结果分析
6. 总结

参考文献

本文的具体内容见于笔者的博客：https://www.cnblogs.com/cjld/p/10977216.html