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机器学习实战:基于Scikit-Learn、Keras 和TensorFlow (原书第2 版)
book

机器学习实战:基于Scikit-Learn、Keras 和TensorFlow (原书第2 版)

by Aurélien Géron
October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
China Machine Press
Content preview from 机器学习实战:基于Scikit-Learn、Keras 和TensorFlow (原书第2 版)
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可以在创建它时将它设置为 trainable = True 来更改它,以便你可以针对你的任务
来进行微调。
并非所有 TF Hub 模块都支持 TensorFlow 2,因此请确保选择一个支持的
模块。
接下来,我们只需加载 IMDb 评论数据集即可
—
无须对其进行预处理(除了批处理和
预取)并直接训练模型:
data sets, info = tfds.load("imdb_reviews", as_supervised=True, with_
info=True)
train_size = info.splits["train"].num_examples
batch_size = 32
train_set = datasets["train"].batch(batch_size).prefetch(1)
history = model.fit(train_set, epochs=5)
请注意,TF Hub 模块 URL 的最后一部分指定了我们想要模型的版本 1。此版本控制可
确保如果发布了新的模块版本,不会破坏我们的模型。方便的是,如果你在网络浏览器
中输入此 URL,你会得到此模块的文档。默认情况下,TF Hub 会将下载的文件缓存到
本地系统的临时目录中。你可能希望把它们下载到一个永久目录中,以避免在每次系统
清理后都必须再次下载它们。为此,请将 TFHUB_CACHE_DIR 环境变量设置为你选择
的目录(例如 os.environ["TFHUB_CACHE_DIR"] = "./my_tfhub_cache" ...
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Publisher Resources

ISBN: 9787111665977