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
|
157
converter.optimizations = [tf.lite.Optimize.DEFAULT]
当我在没有优化的情况下创建猫与狗模型时，我有一个 8.8 MB 的模型。应用优化后，
它缩小到 2.6 MB，节省了大量空间！
你可能想知道这对准确性有什么影响。鉴于模型大小的变化，最好调查一下。
notebook 中有代码，你可以自己尝试，但是当我查看时，我发现模型的未优化版本在
Colab（使 用 GPU，因此它经过浮点运算优化）中每秒会进行大约 37 次迭代，而缩小版
本每秒进行大约 16 次迭代。没有 GPU，性能下降了大约一半，但重要的是，图像分类
的速度仍然很棒，而且在设备上对图像进行分类时，你不太可能需要这种性能！
更重要的是准确性
—
我测试了一组 100 张图像，未优化的模型有 99 个正确，而优化
的模型有 94 个。在这里，你需要做一个权衡，即你是否想以牺牲准确性为代价来优化
模型。为你自己的模型试验这个！请注意，在这种情况下，我只进行了基本的量化。还
有其他方法可以减小模型的大小，这些方法可能影响较小，所有方法都应该进行探索。
接下来让我们看看使用代表性数据。
8.5.2
使用代表性数据
前面的示例显示了通过有效地从数据中删除“空白”来将值从 float32 减少到 int8 的量
化，但该算法通常假设数据均匀分布在 0 附近，因为这是它从训练中学到的数据，如果
你的测试或真实世界的数据不是这样表示的，这可能会导致准确性的损失。我们看到 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.