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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176
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第
9
章
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
import pathlib
tflite_model_file = pathlib.Path('model.tflite')
tflite_model_file.write_bytes(tflite_model)
我们将在第 10 章和第 11 章探索如何使用这些模型。
迁移学习是一种强大的技术，这里只是简单介绍。要了解更多相关信息，请查看
Aurelien Geron 的
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
等书籍，或 Andrew Ng 的优秀教程，例如“迁移学习”视频（
https:/ /oreil.ly/MDOEu
）。
9.4
创建语言模型
在本章中，你了解了如何以多种方式创建模型，以及如何将它们转换为 TensorFlow
Lite，以便将它们部署到移动应用程序中（你将在下一章中看到）。一个重要的细微差别
是它们都是基于图像的模型，对于其他模型类型，你可能需要与 TFLite 模型一起部署额
外的元数据，以便你的移动应用程序可以有效地使用该模型。我们不会在此处详细介绍
训练自然语言处理（NLP）模型的细节
—
这只是对为移动应用创建模型的概念的高度
概括。有关创建和训练语言模型以及 NLP 工作原理的更详细的情况，请查看我的 ...
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