首页 开发技术 其它     /    训练了一层卷积层的神经网络模型来预测CFIR10数据中的图像-源码

训练了一层卷积层的神经网络模型来预测CFIR10数据中的图像-源码

上传者: weixin_42131785 | 上传时间:2024/6/17 3:49:35 | 文件大小:1005KB | 文件类型:ZIP
训练了一层卷积层的神经网络模型来预测CFIR10数据中的图像-源码
训练了一层卷积层的神经网络模型来预测CFIR10数据中的图像

文件下载

资源详情

[{"title":"(35个子文件1005KB)训练了一层卷积层的神经网络模型来预测CFIR10数据中的图像-源码","children":[{"title":"A-layer-convolution-layers-neural-network-model-trained-to-predict-the-images-from-the-CFIR10-datas-master","children":[{"title":"Question3_lossWithRelu.PNG <span style='color:#111;'>7.45KB</span>","children":null,"spread":false},{"title":"Question3_Accuracy_withRelu.PNG <span style='color:#111;'>19.59KB</span>","children":null,"spread":false},{"title":"Question5_batch1_Accuracy.PNG <span style='color:#111;'>19.39KB</span>","children":null,"spread":false},{"title":"Question8_loss.PNG <span style='color:#111;'>4.73KB</span>","children":null,"spread":false},{"title":"Question6_10_accuracy.PNG <span style='color:#111;'>7.90KB</span>","children":null,"spread":false},{"title":"Question3_Loss.PNG <span style='color:#111;'>12.38KB</span>","children":null,"spread":false},{"title":"Question6_10_loss.PNG <span style='color:#111;'>4.20KB</span>","children":null,"spread":false},{"title":"Question6_0.1_Accuracy.PNG <span style='color:#111;'>8.43KB</span>","children":null,"spread":false},{"title":"Question6_0.001_Accuracy.PNG <span style='color:#111;'>15.57KB</span>","children":null,"spread":false},{"title":"Question2_1.PNG <span style='color:#111;'>13.12KB</span>","children":null,"spread":false},{"title":"Question6_0.01_loss.PNG <span style='color:#111;'>14.49KB</span>","children":null,"spread":false},{"title":"question6.py <span style='color:#111;'>7.65KB</span>","children":null,"spread":false},{"title":"Question5_batch4_Accuracy.PNG <span style='color:#111;'>20.49KB</span>","children":null,"spread":false},{"title":"MachineLearningAssignment3-converted.pdf <span style='color:#111;'>597.80KB</span>","children":null,"spread":false},{"title":"Question6_0.001_loss.PNG <span style='color:#111;'>7.95KB</span>","children":null,"spread":false},{"title":"Question5_batch4_Loss.PNG <span style='color:#111;'>7.39KB</span>","children":null,"spread":false},{"title":"Questipn5_batch1000_Accuracy.PNG <span style='color:#111;'>19.48KB</span>","children":null,"spread":false},{"title":"Question5_batch1000_loss.PNG <span style='color:#111;'>7.43KB</span>","children":null,"spread":false},{"title":"question3.py <span style='color:#111;'>8.22KB</span>","children":null,"spread":false},{"title":"Question5_batch1_Loss.PNG <span style='color:#111;'>7.28KB</span>","children":null,"spread":false},{"title":"Question4_Accuracy.PNG <span style='color:#111;'>18.63KB</span>","children":null,"spread":false},{"title":"Question6_0.01Accuracy.PNG <span style='color:#111;'>29.46KB</span>","children":null,"spread":false},{"title":"Question6_0.1_loss.PNG <span style='color:#111;'>21.54KB</span>","children":null,"spread":false},{"title":"question7n.py <span style='color:#111;'>8.24KB</span>","children":null,"spread":false},{"title":"Image_classification.ipynb <span style='color:#111;'>60.37KB</span>","children":null,"spread":false},{"title":"question4.py <span style='color:#111;'>7.61KB</span>","children":null,"spread":false},{"title":"Question4_loss.PNG <span style='color:#111;'>7.33KB</span>","children":null,"spread":false},{"title":"Question3_Accuracy_withourRelu.PNG <span style='color:#111;'>33.19KB</span>","children":null,"spread":false},{"title":"question5.py <span style='color:#111;'>7.98KB</span>","children":null,"spread":false},{"title":"question8.py <span style='color:#111;'>8.11KB</span>","children":null,"spread":false},{"title":"Question2_2.PNG <span style='color:#111;'>40.76KB</span>","children":null,"spread":false},{"title":"question2.py <span style='color:#111;'>7.22KB</span>","children":null,"spread":false},{"title":"Question7_Accuracy.PNG <span style='color:#111;'>19.15KB</span>","children":null,"spread":false},{"title":"Question8_accuracy.PNG <span style='color:#111;'>15.13KB</span>","children":null,"spread":false},{"title":"Question7_loss.PNG <span style='color:#111;'>7.42KB</span>","children":null,"spread":false}],"spread":false}],"spread":true}]

评论信息

免责申明

【好快吧下载】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,【好快吧下载】 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 【好快吧下载】 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,8686821#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