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Fix errors and typos in the Estimators programmer's guide #19273

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May 21, 2018
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Fix errors and typos in the Estimators programmer's guide #19273

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19 changes: 9 additions & 10 deletions tensorflow/docs_src/programmers_guide/estimators.md
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Expand Up @@ -21,18 +21,17 @@ Note: TensorFlow also includes a deprecated `Estimator` class at

Estimators provide the following benefits:

* You can run Estimators-based models on a local host or on a
* You can run Estimator-based models on a local host or on a
distributed multi-server environment without changing your model.
Furthermore, you can run Estimators-based models on CPUs, GPUs,
Furthermore, you can run Estimator-based models on CPUs, GPUs,
or TPUs without recoding your model.
* Estimators simplify sharing implementations between model developers.
* You can develop a state of the art model with high-level intuitive code,
* You can develop a state of the art model with high-level intuitive code.
In short, it is generally much easier to create models with Estimators
than with the low-level TensorFlow APIs.
* Estimators are themselves built on tf.layers, which
* Estimators are themselves built on @{tf.layers}, which
simplifies customization.
* Estimators build the graph for you. In other words, you don't have to
build the graph.
* Estimators build the graph for you.
* Estimators provide a safe distributed training loop that controls how and
when to:
* build the graph
Expand All @@ -57,7 +56,7 @@ the "plumbing" for you. That is, pre-made Estimators create and manage
pre-made Estimators let you experiment with different model architectures by
making only minimal code changes. @{tf.estimator.DNNClassifier$`DNNClassifier`},
for example, is a pre-made Estimator class that trains classification models
through dense, feed-forward neural networks.
based on dense, feed-forward neural networks.


### Structure of a pre-made Estimators program
Expand All @@ -79,7 +78,7 @@ of the following four steps:
an input function:

def input_fn(dataset):
... # manipulate dataset, extracting feature names and the label
... # manipulate dataset, extracting the feature dict and the label
return feature_dict, label

(See @{$programmers_guide/datasets} for full details.)
Expand All @@ -96,13 +95,13 @@ of the following four steps:
population = tf.feature_column.numeric_column('population')
crime_rate = tf.feature_column.numeric_column('crime_rate')
median_education = tf.feature_column.numeric_column('median_education',
normalizer_fn='lambda x: x - global_education_mean')
normalizer_fn=lambda x: x - global_education_mean)

3. **Instantiate the relevant pre-made Estimator.** For example, here's
a sample instantiation of a pre-made Estimator named `LinearClassifier`:

# Instantiate an estimator, passing the feature columns.
estimator = tf.estimator.Estimator.LinearClassifier(
estimator = tf.estimator.LinearClassifier(
feature_columns=[population, crime_rate, median_education],
)

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