Anonymize docstring for create_feature_spec_for_parsing

Change: 139087061

1 files changed, 16 insertions, 12 deletions

diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py
index ec94f7ace9..f6338f9ec0 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column.py
@@ -68,6 +68,7 @@ Typical usage example:
       dnn_feature_columns=my_deep_features,
       dnn_hidden_units=[500, 250, 50])
   estimator.train(...)
+  ```
 
   See feature_column_ops_test for more examples.
 """
@@ -1802,22 +1803,25 @@ def create_feature_spec_for_parsing(feature_columns):
 
   ```python
   # Define features and transformations
-  country = sparse_column_with_vocabulary_file("country", VOCAB_FILE)
-  age = real_valued_column("age")
-  click_bucket = bucketized_column(real_valued_column("historical_click_ratio"),
-                                   boundaries=[i/10. for i in range(10)])
-  country_x_click = crossed_column([country, click_bucket], 10)
-
-  feature_columns = set([age, click_bucket, country_x_click])
+  feature_a = sparse_column_with_vocabulary_file(...)
+  feature_b = real_valued_column(...)
+  feature_c_bucketized = bucketized_column(real_valued_column("feature_c"), ...)
+  feature_a_x_feature_c = crossed_column(
+    columns=[feature_a, feature_c_bucketized], ...)
+
+  feature_columns = set(
+    [feature_b, feature_c_bucketized, feature_a_x_feature_c])
   batch_examples = tf.parse_example(
-      serialized_examples,
-      create_feature_spec_for_parsing(feature_columns))
+      serialized=serialized_examples,
+      features=create_feature_spec_for_parsing(feature_columns))
   ```
 
   For the above example, create_feature_spec_for_parsing would return the dict:
-  {"age": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
-   "historical_click_ratio": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
-   "country": parsing_ops.VarLenFeature(tf.string)}
+  {
+    "feature_a": parsing_ops.VarLenFeature(tf.string),
+    "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
+    "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
+  }
 
   Args:
     feature_columns: An iterable containing all the feature columns. All items