基于Caffe深度学习框架的卷积神经网络研究
发布时间：2018-07-28 07:25
【摘要】：深度学习是人工智能领域发展的一个重要组成部分,深度学习在许多领域(如图像识别、语音识别、自然语言处理)取得了突破,在传统算法不易解决的应用方面也取得了令人可喜的成就,包括自动无人驾驶汽车、自动模式识别、自动同声传译、商品图片检索、手写字符识别、车牌自动识别等。近年来,随着研究开发人员对于深度学习开发过程要求的不断提高,传统的深度学习编程方法已经不能满足当前的需要,传统的深度学习编程方法会耗费研究开发人员数月甚至几年的时间用来实现最基本的算法,与此同时,一些世界顶尖的科研机构开始寻求快速、高效的深度学习开发模式,因此就产生了包括本文研究的Caffe深度学习框架在内的多种深度学习开发框架。这些深度学习框架不仅为科研机构、相关开发人员提供了高效、快速的开发模式,并且其中一些深度学习框架还提供了多个卷积神经网络模型以便开发人员在较为先进、完善的卷积神经网络模型上进行研究、改进。本文基于Caffe深度学习框架的卷积神经网络进行了以下几项工作和研究:首先,介绍了关于深度学习在图像识别,语音识别,自然语言处理三个领域的研究现状,以及几个主流的深度学习框架,并且进行了比较,由此引出Caffe深度学习框架。之后,从人工神经网络引出卷积神经网络,对卷积神经网络的构成要素和结构进行了详细阐述,对Caffe深度学习框架的几个特性做了介绍,详细说明了Caffe环境搭建的步骤。最后,本文使用Caffe深度学习框架进行仿真实验。仿真包括三个部分:1、以CIFAR-10的神经网络为例,对Caffe框架给出的卷积神经网络示例的配置训练方法进行了说明;2、以自己构建的小型数据集为例,介绍了使用自己创建的数据集和自己搭建的卷积神经网络的训练方法;3、本文对Caffe框架下的基于MNIST手写字符集的LeNet-5网络的改进,本文对激活函数进行了改进,用ReLU函数替代了原始的Sigmoid,并在Le Net-5中加入了一层激活函数,通过对比,本文的方法使得网络的收敛速度有所提高,并且提高了网络训练的正确率。
[Abstract]:Depth learning is an important part of the development of artificial intelligence. Deep learning has made a breakthrough in many fields (such as image recognition, speech recognition, natural language processing). Gratifying achievements have been made in the application of traditional algorithms, including autonomous driverless vehicles, automatic pattern recognition, automatic simultaneous interpretation, commodity image retrieval, handwritten character recognition, license plate automatic recognition and so on. In recent years, with the increasing demands of researchers and developers on the development process of in-depth learning, the traditional in-depth learning programming method can no longer meet the current needs. Traditional deep learning programming methods can take months or even years of research and development to implement the most basic algorithms, while some of the world's top scientific institutions are looking for fast, efficient models of deep learning and development. Therefore, many kinds of deep learning development frameworks, including the Caffe depth learning framework studied in this paper, have emerged. These deep learning frameworks not only provide efficient and rapid development models for scientific research institutions and related developers, but also provide multiple convolutional neural network models for developers to be more advanced. Perfect convolution neural network model is studied and improved. In this paper, the convolution neural network based on Caffe depth learning framework is studied as follows: firstly, the research status of depth learning in image recognition, speech recognition and natural language processing is introduced. And several mainstream deep learning frameworks are compared, which leads to the Caffe deep learning framework. After that, the convolution neural network is introduced from the artificial neural network, the composing elements and structure of the convolutional neural network are described in detail, several characteristics of the Caffe deep learning framework are introduced, and the steps of setting up the Caffe environment are explained in detail. Finally, this paper uses the Caffe depth learning framework to carry on the simulation experiment. The simulation includes three parts: 1. Taking the neural network of CIFAR-10 as an example, the configuration training method of convolutional neural network example given by Caffe framework is explained, and the small data set constructed by oneself is taken as an example. This paper introduces the training method of using the data set created by oneself and the convolution neural network built by oneself. In this paper, we improve the LeNet-5 network based on MNIST handwritten character set under the Caffe framework, and improve the activation function. The ReLU function is used to replace the original Sigmoid, and a layer of activation function is added to Le Net-5. By comparison, the convergence speed of the network is improved and the correct rate of network training is improved.
【学位授予单位】：河北师范大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP18
本文编号：2149341
[Abstract]:Depth learning is an important part of the development of artificial intelligence. Deep learning has made a breakthrough in many fields (such as image recognition, speech recognition, natural language processing). Gratifying achievements have been made in the application of traditional algorithms, including autonomous driverless vehicles, automatic pattern recognition, automatic simultaneous interpretation, commodity image retrieval, handwritten character recognition, license plate automatic recognition and so on. In recent years, with the increasing demands of researchers and developers on the development process of in-depth learning, the traditional in-depth learning programming method can no longer meet the current needs. Traditional deep learning programming methods can take months or even years of research and development to implement the most basic algorithms, while some of the world's top scientific institutions are looking for fast, efficient models of deep learning and development. Therefore, many kinds of deep learning development frameworks, including the Caffe depth learning framework studied in this paper, have emerged. These deep learning frameworks not only provide efficient and rapid development models for scientific research institutions and related developers, but also provide multiple convolutional neural network models for developers to be more advanced. Perfect convolution neural network model is studied and improved. In this paper, the convolution neural network based on Caffe depth learning framework is studied as follows: firstly, the research status of depth learning in image recognition, speech recognition and natural language processing is introduced. And several mainstream deep learning frameworks are compared, which leads to the Caffe deep learning framework. After that, the convolution neural network is introduced from the artificial neural network, the composing elements and structure of the convolutional neural network are described in detail, several characteristics of the Caffe deep learning framework are introduced, and the steps of setting up the Caffe environment are explained in detail. Finally, this paper uses the Caffe depth learning framework to carry on the simulation experiment. The simulation includes three parts: 1. Taking the neural network of CIFAR-10 as an example, the configuration training method of convolutional neural network example given by Caffe framework is explained, and the small data set constructed by oneself is taken as an example. This paper introduces the training method of using the data set created by oneself and the convolution neural network built by oneself. In this paper, we improve the LeNet-5 network based on MNIST handwritten character set under the Caffe framework, and improve the activation function. The ReLU function is used to replace the original Sigmoid, and a layer of activation function is added to Le Net-5. By comparison, the convergence speed of the network is improved and the correct rate of network training is improved.
【学位授予单位】：河北师范大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP18
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