January 2023
Intermediate to advanced
288 pages
6h 6m
Chinese
Content preview from 移动端AI与ML应用开发：基于iOS和Android
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181
第
10
章
在 Android 中使用自定义模型
在第 9 章中，你了解了使用 TensorFlow Lite Model Maker、Cloud AutoML Vision Edge
以及具有迁移学习功能的 TensorFlow 来创建自定义模型的各种场景。在本章中，你将探
索如何使用这些并将其集成到你的 Android 应用程序中。不幸的是，它很少像“将模型
放入应用程序就直接能工作”那么简单。处理数据通常会很复杂，因为 Android 会以不
同于 TensorFlow 的方式来表示图像和字符串等内容，而且实际上，模型的输出需要从基
于 Tensor 的输出解析为在 Android 中更具表现性的内容。 我们将首先探讨这一点，然后
进入一些关于如何在 Android 中使用图像和语言模型的示例。
10.1
将模型桥接到
Android
在创建使用机器学习模型的应用程序时，你会拥有一个扩展名为
.tflite
的二进制区块，
你将把它合并到你的应用程序中。这个二进制文件希望有张量输入（或它们的一些模拟
输入），并会输出张量。那将是第一个挑战。此外，该模型只有在有相关联的元数据时才
能很好地工作。因此，举例来说，如果你构建一个花卉分类器，如第 9 章中所述，该模
型会为你提供 5 个概率的输出，每个概率匹配特定的花卉类型。然而，该模型并没有输
出花的类型，例如玫瑰。它只是为你提供一组数字，因此你需要关联的元数据才能知道
哪个输出值与哪朵花匹配。此外，如果你使用语言模型进行文本分类，你还需要了解模
型训练所使用的单词词典。我们将在本章中探讨这一点！ ...
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