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创建定制化 Estimator #53

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merged 7 commits into from
Apr 16, 2018
Merged

创建定制化 Estimator #53

merged 7 commits into from
Apr 16, 2018

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Starriers
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@Starriers Starriers commented Apr 5, 2018 •
edited by leviding
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翻译完成 #3

@leviding leviding mentioned this pull request Apr 6, 2018
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leviding commented Apr 9, 2018

@Starriers 失效的图，需要你去 TensorFlow 官方找图源进行替换

@leviding leviding mentioned this pull request Apr 11, 2018
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校对认领

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@luochen1992 ok

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luochen1992 approved these changes Apr 15, 2018
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get_started/custom_estimators.md Outdated
Let's see how to solve the Iris problem with a custom Estimator. A quick
reminder--here's the organization of the Iris model that we're trying to mimic:
让我们看看如何使用定制化 Estimator 解决 Iris 问题。
快速提醒 -- 这是我们尝试模仿虹膜模型的组织结构：
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『 -- 』=>『 —— 』
多打了一个空格

get_started/custom_estimators.md Outdated
本文档介绍定制化 Estimators。特别是，
本文档将演示如何创建定制化 @{tf.estimator.Estimator$Estimator} ，
@{tf.estimator.DNNClassifier$`DNNClassifier`} 在解决 Iris 问题的行为中，
它可以模拟预制的 Estimator。
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『 Iris 』=>『虹膜 』

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@Starriers @leviding 校对完成

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@Starriers 调整一下吧，该是一行的就调整到一行。然后我再 Review 一遍就行了。

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@Starriers @luochen1992 有空校对下新的那个 PR 哈

@Starriers Starriers changed the title 创建定制化 Estimators 创建定制化 Estimator Apr 16, 2018
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校对修改建议完成

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@leviding @luochen1992

leviding
leviding reviewed Apr 16, 2018
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@Starriers 问题太多了！！

@luochen1992 校对的时候，格式问题也要指出的。

get_started/custom_estimators.md Outdated
@{tf.estimator.DNNClassifier$`DNNClassifier`} in solving the Iris problem. See
the @{$get_started/premade_estimators$Pre-Made Estimators chapter} for details
on the Iris problem.
本文档介绍定制化 Estimator。特别是，本文档将演示如何创建定制化 @{tf.estimator.Estimator$Estimator} ，@{tf.estimator.DNNClassifier$`DNNClassifier`} 在解决虹膜问题的行为中，它可以模拟预制的 Estimator。有关虹膜问题的详细信息，请参阅 @{$get_started/premade_estimators$Pre-Made Estimators chapter} 。
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段尾多了空格

get_started/custom_estimators.md Outdated
</div>
<div style="text-align: center">
Pre-made and custom Estimators are all Estimators.
预制和定制化 Estimator 都是 Estimator.
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句号

get_started/custom_estimators.md Outdated
dataset = dataset.shuffle(1000).repeat().batch(batch_size)

# Return the read end of the pipeline.
# 返回管道读取的结束端
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句号

get_started/custom_estimators.md Outdated
the following lines create the estimator and set the params to configure the
model. This configuration step is similar to how we configured the @{tf.estimator.DNNClassifier} in
@{$get_started/premade_estimators}.
调用者可以将 `params` 传递给 Estimator 的构造函数。任何传递给构造函数的`params` 接着都会传递给 `model_fn`。在 [`custom_estimator.py`](https://github.com/tensorflow/models/blob/master/samples/core/get_started/custom_estimator.py) 中以下几行会创建 estimator 并设置参数来配置该参数模型。此配置步骤与我们在 @{$get_started/premade_estimators} 中如何配置 @{tf.estimator.DNNClassifier} 相似。
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空格少了

get_started/custom_estimators.md Outdated
* [Evaluate](#evaluate)
* [Train](#train)
* [定义模型](#define_the_model)
* 为每种[三种不同模式](#modes):
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多了空格

get_started/custom_estimators.md Outdated
@{tf.train.AdagradOptimizer} because we're mimicking the `DNNClassifier`, which
also uses `Adagrad` by default. The `tf.train` package provides many other
optimizers—feel free to experiment with them.
构建训练操作将需要一个优化器。我们将使用 @{tf.tra.AdagradOptimizer}，因为我们正在模`DNNClassifier`，默认情况下也使用 `Adagrad`。 `tf.train` 包提供了许多其他优化器--可以随意使用它们。
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空格多了

get_started/custom_estimators.md Outdated

```python
# Build 2 hidden layer DNN with 10, 10 units respectively.
# 分别用 10 个单元和 10 个单元 建立 2 个隐藏层 DNN。
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空格问题 + 用两个 10 单元建立有 2 个隐藏层的 DNN

get_started/custom_estimators.md Outdated
Here the `params` dictionary serves the same purpose as the key-word
arguments of `DNNClassifier`; that is, the `params` dictionary lets you
configure your Estimator without modifying the code in the `model_fn`.
在这里 `params` 字典的作用与关键字相同。`DNNClassifier` 的参数; 也就是说， `params` 字典允许您在不修改 `model_fn` 中代码的情况下配置您的 Estimator。
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空格多了一个

get_started/custom_estimators.md Outdated

* loss: The loss reported.
* 损失：损失报告。
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格式乱码

get_started/custom_estimators.md Outdated
These tensorboard graphs are one of the main reasons it's important to pass a
`global_step` to your optimizer's `minimize` method. The model can't record
the x-coordinate for these graphs without it.
这些 tensorboard 是向优化的`minimize` 方法传递 `global_step` 的主要原因之一。没有它，模型就不能记录这些图的 x 坐标。
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空格少了

leviding
leviding reviewed Apr 16, 2018
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get_started/custom_estimators.md

## Implement training, evaluation, and prediction {#modes}
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这是锚点啊，怎么能翻译呢

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leviding commented Apr 16, 2018 •
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@Starriers @luochen1992 看看我的 修改，下次注意哈，还有什么问题吗？

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格式问题 确实挺多的…… 一次一定注意！

@leviding leviding merged commit 516007c into xitu:zh-hans Apr 16, 2018
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