机器学习实践:图像分类
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练习 3:特征提取和微调
在本练习中,您将对 Google 的 Inception v3 模型应用特征提取和微调技巧,以进一步提高练习 1 和练习 2 中的猫狗分类器的准确率:
如未另行说明,那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可,并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情,请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC):2025-01-28。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["没有我需要的信息","missingTheInformationINeed","thumb-down"],["太复杂/步骤太多","tooComplicatedTooManySteps","thumb-down"],["内容需要更新","outOfDate","thumb-down"],["翻译问题","translationIssue","thumb-down"],["示例/代码问题","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-01-28。"],[[["This exercise leverages Google's Inception v3 model through feature extraction and fine-tuning to enhance the accuracy of a cat-vs-dog image classifier."],["It builds upon the previous exercises on image classification, refining the model for better performance."],["You will practically implement these techniques using a provided Google Colab notebook."]]],[]]