September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
Content preview from 基于AWS的数据科学实践
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260
｜
第
7
章
我们设置
shuffle=True
以打乱数据集，并配置
epochs=5
以在数据集上运行五次。
epochs
（读作
eh-puhks
）的值是可配置并微调的。我们将在下一章探索模型微调。
7.8.4
保存模型
现在，让我们将模型保存为预测应用使用的
TensorFlow
SavedModel
格式：
model.save('./tensorflow/', save_format='tf')
在第
9
章，我们会使用保存于
./tensorflow/
的模型和
TensorFlow Serving
来部署模
型，并通过
SageMaker
批量转换（离线，批量）和
SageMaker
端点（在线，实时）
来规模化运行评价分类预测。
7.9
从
SageMaker
笔记本启动训练脚本
让我们展示从
SageMaker
笔记本运行训练脚本所需的步骤。晚些时候，我们将从自
动化流水线运行相同的脚本。现在，我们从笔记本运行脚本。首先，我们设置监控
训练任务所需的指标。其次，我们将配置算法特定的超参数。接下来，我们将选择
实例类型和集群中的实例数量。最后，我们将启动训练任务。
7.9.1
定义用于捕捉和监控的指标
我们可以从训练脚本打印或记录到控制台的任何信息创建指标。让我们假定我们的
TensorFlow
模型输出包含训练损失和训练准确性的如下日志（
loss, accuracy
）以
及验证损失和验证准确性（
val_loss, val_accuracy
）。
5000/10000 [>....................] - loss: 0.1420 ...
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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.