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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第
9
章
12/12 [=======================] - 5s 123ms/step - loss: 0.5813 - accuracy: 0.9292
这里的准确率表明，在测试集上
—
对于网络以前没有见过的图像
—
我们的准确率
约为 93%。我们有一个非常好的网络，它不会过拟合训练数据！这只是一个高层次的
样子，当你更详细地构建模型时，你会想要探索更详细的指标，例如探索模型对每一
类图像的准确度（通常称为“混淆矩阵”），因此可以确定你是否对某些类别没有过度
补偿。这超出了本书的范围，但请查看 O
’
Reilly 的其他书籍（例如我的
AI and Machine
Learning for Coders
）以了解有关创建模型的更多详细信息。
现在我们需要做的就是导出训练好的模型。此时我们有一个在 Python 环境中执行的
TensorFlow 模型。我们想要一个可以在移动设备上使用的 TensorFlow Lite 模型。为此，
Model Maker 工具为我们提供了一个可以使用的导出：
model.export(export_dir='/mm_flowers/')
这会将模型转换为 TFLite 格式，类似于我们在第 8 章中看到的，但也会将元数据和标签
信息编码到模型中。这为我们提供了一个单一文件的解决方案，使 Android 开发人员可
以更轻松地将模型导入 Android Studio 并生成正确的标签详细信息。我们将在第 10 章看
到这一点。不幸的是，这是 Android ...
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