大数据驱动的深度模型在图像分类中的应用(VGG16+VGG19图像分类,源码结果都可运行)

上传者: 45508265 | 上传时间: 2022-05-17 12:06:09 | 文件大小: 80.85MB | 文件类型: ZIP
简述VGG模型,说明其中的结构(描述模型的结构,哪一层是卷积、那一层是池化、那一层是全连接?),并使用VGG模型完成下面图像分类的实验(建议使用Python语言,Pytorch 框架)。图像分类数据集:CIFAR-10,由10个类的60000个32x32彩色图像组成,每个类有6000个图像;有50000个训练样本(训练集)和10000个测试样本(测试集) 分别使用数据集中训练集的1%、10%、50%、80%样本进行训练模型,使用测试样本进行测试,简述步骤并对比使用不同比例的训练样本对于训练结果的影响(即模型训练完成后,使用测试样本输入模型得到的准确率)。随着数据量的增大,观察每一次模型迭代(模型每完成一次迭代,即所有训练样本输入到模型中进行训练更新)所需的计算时间、内存消耗变化,并做比较。分析试验结果,回答下面问题: A. 说明你实验的硬件环境 B. 说明自己程序中使用的是哪种梯度下降算法(随机、批量、全部)? C. 训练过程中你调整了哪些参数,谈谈你的调参过程和调参技巧 D. 当数据量逐渐变大时,你的训练测试过程有没遇到实质性困难?

文件下载

资源详情

[{"title":"( 45 个子文件 80.85MB ) 大数据驱动的深度模型在图像分类中的应用(VGG16+VGG19图像分类,源码结果都可运行)","children":[{"title":"VGG16_train_split_0.1(72 %).ipynb <span style='color:#111;'> 3.23MB </span>","children":null,"spread":false},{"title":"VGG的探索之CIFAR10分类.pdf <span style='color:#111;'> 3.91MB </span>","children":null,"spread":false},{"title":"VGG_19_whole 2.ipynb <span style='color:#111;'> 761.47KB </span>","children":null,"spread":false},{"title":"VGG16_improve_acc_last(无全连接) - 副本.ipynb <span style='color:#111;'> 3.16MB </span>","children":null,"spread":false},{"title":"img","children":[{"title":"Loss_split_0.1.png <span style='color:#111;'> 34.37KB </span>","children":null,"spread":false},{"title":"Lr_split_0.8.png <span style='color:#111;'> 9.91KB </span>","children":null,"spread":false},{"title":"Loss_split_0.01.png <span style='color:#111;'> 26.84KB </span>","children":null,"spread":false},{"title":"Acc_split_0.01.png <span style='color:#111;'> 27.16KB </span>","children":null,"spread":false},{"title":"Loss_split_0.8.png <span style='color:#111;'> 24.46KB </span>","children":null,"spread":false},{"title":"Lr_split_0.01.png <span style='color:#111;'> 10.14KB </span>","children":null,"spread":false},{"title":"Acc_split_0.8.png <span style='color:#111;'> 22.04KB </span>","children":null,"spread":false},{"title":"Acc_split_0.5.png <span style='color:#111;'> 21.81KB </span>","children":null,"spread":false},{"title":"Acc_split_1.0.png <span style='color:#111;'> 21.00KB </span>","children":null,"spread":false},{"title":"Acc_improve.png <span style='color:#111;'> 23.45KB </span>","children":null,"spread":false},{"title":"Lr_split_0.5.png <span style='color:#111;'> 9.91KB </span>","children":null,"spread":false},{"title":"Loss_split_1.0.png <span style='color:#111;'> 22.84KB </span>","children":null,"spread":false},{"title":"Lr_improve.png <span style='color:#111;'> 10.60KB </span>","children":null,"spread":false},{"title":"Loss_improve.png <span style='color:#111;'> 26.09KB </span>","children":null,"spread":false},{"title":"Lr_split_1.0.png <span style='color:#111;'> 10.23KB </span>","children":null,"spread":false},{"title":"Loss_split_0.5.png <span style='color:#111;'> 25.25KB </span>","children":null,"spread":false},{"title":"Acc_split_0.1.png <span style='color:#111;'> 30.14KB </span>","children":null,"spread":false},{"title":"Lr_split_0.1.png <span style='color:#111;'> 9.89KB </span>","children":null,"spread":false}],"spread":false},{"title":"VGG16_improve_acc_last(91.82 %).ipynb <span style='color:#111;'> 3.29MB </span>","children":null,"spread":false},{"title":"VGG16_train_split_0.01(36%).ipynb <span style='color:#111;'> 1.86MB </span>","children":null,"spread":false},{"title":"history","children":[{"title":"history_train_split_0.8.txt <span style='color:#111;'> 9.38KB </span>","children":null,"spread":false},{"title":"history_train_split_0.01.txt <span style='color:#111;'> 9.43KB </span>","children":null,"spread":false},{"title":"history_train_split_improve_last2.txt <span style='color:#111;'> 14.08KB </span>","children":null,"spread":false},{"title":"history_train_split_improve_last.txt <span style='color:#111;'> 13.09KB </span>","children":null,"spread":false},{"title":"history_train_split_1.0.txt <span style='color:#111;'> 7.03KB </span>","children":null,"spread":false},{"title":"history_train_split_improve2.txt <span style='color:#111;'> 13.09KB </span>","children":null,"spread":false},{"title":"history_vgg19_best.txt <span style='color:#111;'> 14.12KB </span>","children":null,"spread":false},{"title":"history_train_split_0.1.txt <span style='color:#111;'> 9.38KB </span>","children":null,"spread":false},{"title":"history_vgg19.txt <span style='color:#111;'> 4.69KB </span>","children":null,"spread":false},{"title":"history.txt <span style='color:#111;'> 6.08KB </span>","children":null,"spread":false},{"title":"history_train_split_improve.txt <span style='color:#111;'> 7.03KB </span>","children":null,"spread":false},{"title":"history_train_split_0.5.txt <span style='color:#111;'> 9.38KB </span>","children":null,"spread":false}],"spread":false},{"title":"VggNet.py <span style='color:#111;'> 2.47KB </span>","children":null,"spread":false},{"title":"vgg_19.ipynb <span style='color:#111;'> 13.63KB </span>","children":null,"spread":false},{"title":"VGG16_train_split_0.8(91.13 %).ipynb <span style='color:#111;'> 3.20MB </span>","children":null,"spread":false},{"title":"VGG16_improve_acc(92.58 %).ipynb <span style='color:#111;'> 2.52MB </span>","children":null,"spread":false},{"title":"VGG16_train_split_0.5(88 %).ipynb <span style='color:#111;'> 3.20MB </span>","children":null,"spread":false},{"title":"VGG16_train_split_1.0(92.08 %).ipynb <span style='color:#111;'> 3.18MB </span>","children":null,"spread":false},{"title":"VGG16_improve_acc2.ipynb <span style='color:#111;'> 2.71MB </span>","children":null,"spread":false},{"title":"vgg16_best.pkl <span style='color:#111;'> 58.26MB </span>","children":null,"spread":false},{"title":"VGG16_improve_acc_last2(92.68%).ipynb <span style='color:#111;'> 3.26MB </span>","children":null,"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明