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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更深入：了解
TensorFlow Lite
|
129
tflite_model_file = pathlib.Path('model.tflite')
tflite_model_file.write_bytes(tflite_model)
写入字节的过程会返回一个数字，这就是写入的字节数。对于不同版本的转换器，这可
能会发生变化，但撰写本书时的转换器写出了一个名为
model.tflite
的文件，该文件
大小为 896 字节。这封装了整个训练模型，包括架构和学习的权重。
虽然使用前面所示的 Python API 是执行模型转换的推荐方式，但 TensorFlow
团队还提供了一个命令行解释器（如果你想改用它）。你可以在
https://www.
tensorflow.org/lite/convert
上了解更多信息。
8.2.3
使用独立解释器测试模型
在 iOS 或 Android 上尝试该模型之前，你应该使用 TensorFlow Lite 的独立解释器来查看
它是否运行良好。这个解释器运行在 Python 环境中，所以它也可以用在能运行 Python
的嵌入式系统中，比如基于 Linux 的树莓派！
下一步是将模型加载到解释器中，将用于输入数据的张量分配给模型进行预测，然
后读取模型输出的预测。这就是使用 TensorFlow Lite，从程序员的角度来看，与使用
TensorFlow 有很大的不同。使用 TensorFlow，你可以只说
model.predict(
something
)
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