message: Minor corrections in feature_columns doc
diff: @@ -146,10 +146,10 @@ single input number into a four-element vector. Therefore, the model now can
  learn _four individual weights_ rather than just one; four weights creates a
  richer model than one weight. More importantly, bucketizing enables the model
  to clearly distinguish between different year categories since only one of the
  -elements is set (1) and the other three elements are cleared (0). When we just
  -use a single number (a year) as input, the model can only learn a linear
  -relationship. So, bucketing provides the model with additional flexibility that
  -the model can use to learn.
  +elements is set (1) and the other three elements are cleared (0). For example,
  +when we just use a single number (a year) as input, a linear model can only
  +learn a linear relationship. So, bucketing provides the model with additional
  +flexibility that the model can use to learn.
  The following code demonstrates how to create a bucketized feature:
  @@ -242,7 +242,7 @@ on an explicit vocabulary list. For example:
  # the elements in the vocabulary list.
  vocabulary_feature_column =
  tf.feature_column.categorical_column_with_vocabulary_list(
  - key="a feature returned by input_fn()",
  + key=feature_name_from_input_fn,
  vocabulary_list=["kitchenware", "electronics", "sports"])
  ```
  @@ -259,7 +259,7 @@ you place the vocabulary words in a separate file. For example:
  # the elements in the vocabulary file
  vocabulary_feature_column =
  tf.feature_column.categorical_column_with_vocabulary_file(
  - key="a feature returned by input_fn()",
  + key=feature_name_from_input_fn,
  vocabulary_file="product_class.txt",
  vocabulary_size=3)
  ```
  