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authorGravatar A. Unique TensorFlower <gardener@tensorflow.org>2016-12-08 21:42:24 -0800
committerGravatar TensorFlower Gardener <gardener@tensorflow.org>2016-12-08 22:02:24 -0800
commit9e14d13c2388a49e8cfa6ef50a47f548ee04fed0 (patch)
tree17d6a3724fe1f772818f4074464931956f6525d1 /tensorflow/tools/dist_test
parentf37c468252b6bbda05dc8f3ba73d6e64b9a60696 (diff)
Automated rollback of change 141490813
Change: 141521124
Diffstat (limited to 'tensorflow/tools/dist_test')
-rw-r--r--tensorflow/tools/dist_test/python/census_widendeep.py11
1 files changed, 5 insertions, 6 deletions
diff --git a/tensorflow/tools/dist_test/python/census_widendeep.py b/tensorflow/tools/dist_test/python/census_widendeep.py
index 62ff7f8f2f..309366e467 100644
--- a/tensorflow/tools/dist_test/python/census_widendeep.py
+++ b/tensorflow/tools/dist_test/python/census_widendeep.py
@@ -217,12 +217,11 @@ class CensusDataSource(object):
for k in self.continuous_columns}
# Creates a dictionary mapping from each categorical feature column name (k)
# to the values of that column stored in a tf.SparseTensor.
- categorical_cols = {
- k: tf.SparseTensor(
- indices=[[i, 0] for i in range(df[k].size)],
- values=df[k].values,
- dense_shape=[df[k].size, 1])
- for k in self.categorical_columns}
+ categorical_cols = {k: tf.SparseTensor(
+ indices=[[i, 0] for i in range(df[k].size)],
+ values=df[k].values,
+ shape=[df[k].size, 1])
+ for k in self.categorical_columns}
# Merges the two dictionaries into one.
feature_cols = dict(continuous_cols.items() + categorical_cols.items())
# Converts the label column into a constant Tensor.