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-rw-r--r-- | tensorflow/contrib/layers/python/layers/feature_column_ops.py | 26 |
1 files changed, 13 insertions, 13 deletions
diff --git a/tensorflow/contrib/layers/python/layers/feature_column_ops.py b/tensorflow/contrib/layers/python/layers/feature_column_ops.py index 17efe676cd..4595144092 100644 --- a/tensorflow/contrib/layers/python/layers/feature_column_ops.py +++ b/tensorflow/contrib/layers/python/layers/feature_column_ops.py @@ -810,29 +810,29 @@ class _Transformer(object): feature columns require data transformations. This class handles those transformations if they are not handled already. - Some features may be used in more than one places. For example one can use a + Some features may be used in more than one place. For example, one can use a bucketized feature by itself and a cross with it. In that case Transformer should create only one bucketization op instead of multiple ops for each feature column. To handle re-use of transformed columns, Transformer keeps all previously transformed columns. - An example usage of Transformer is as follows: + Example: - occupation = sparse_column_with_hash_bucket(column_name="occupation", - hash_bucket_size=1000) - age = real_valued_column("age") - age_buckets = bucketized_column( - source_column=age, - boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65]) - occupation_x_age = crossed_column(columns=[occupation, age_buckets], - hash_bucket_size=10000) + ```python + sparse_feature = sparse_column_with_hash_bucket(...) + real_valued_feature = real_valued_column(...) + real_valued_buckets = bucketized_column(source_column=real_valued_feature, + ...) + sparse_x_real = crossed_column( + columns=[sparse_feature, real_valued_buckets], hash_bucket_size=10000) columns_to_tensor = tf.parse_example(...) transformer = Transformer(columns_to_tensor) - occupation_x_age_tensor = transformer.transform(occupation_x_age) - occupation_tensor = transformer.transform(occupation) - age_buckets_tensor = transformer.transform(age_buckets) + sparse_x_real_tensor = transformer.transform(sparse_x_real) + sparse_tensor = transformer.transform(sparse_feature) + real_buckets_tensor = transformer.transform(real_valued_buckets) + ``` """ def __init__(self, columns_to_tensors): |