Final breaking change of SparseTensor.shape -> SparseTensor.dense_shape rename.

Removing shape property from SparseTensor.
Change: 141489556

tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_benchmark_test.py

@@ -98,7 +98,7 @@ class DNNLinearCombinedClassifierBenchmark(tf.test.Benchmark):
       features['dummy_sparse_column'] = tf.SparseTensor(
           values=('en', 'fr', 'zh'),
           indices=((0, 0), (0, 1), (60, 0)),
-          shape=(len(iris.target), 2))
+          dense_shape=(len(iris.target), 2))
       labels = tf.reshape(tf.constant(iris.target, dtype=tf.int32), (-1, 1))
       return features, labels
 

tensorflow/contrib/learn/python/learn/estimators/dnn_test.py

@@ -57,7 +57,7 @@ class EmbeddingMultiplierTest(tf.test.TestCase):
             tf.SparseTensor(
                 values=['en', 'fr', 'zh'],
                 indices=[[0, 0], [1, 0], [2, 0]],
-                shape=[3, 1]),
+                dense_shape=[3, 1]),
     }
     labels = tf.constant([[0], [0], [0]], dtype=tf.int32)
     with self.assertRaisesRegexp(
@@ -87,12 +87,12 @@ class EmbeddingMultiplierTest(tf.test.TestCase):
             tf.SparseTensor(
                 values=['en', 'fr', 'zh'],
                 indices=[[0, 0], [1, 0], [2, 0]],
-                shape=[3, 1]),
+                dense_shape=[3, 1]),
         'wire':
             tf.SparseTensor(
                 values=['omar', 'stringer', 'marlo'],
                 indices=[[0, 0], [1, 0], [2, 0]],
-                shape=[3, 1]),
+                dense_shape=[3, 1]),
     }
     labels = tf.constant([[0], [0], [0]], dtype=tf.int32)
     model_ops = dnn._dnn_model_fn(features, labels,
@@ -193,7 +193,7 @@ class DNNClassifierTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant([[1], [0], [0]], dtype=tf.int32)
 
@@ -230,7 +230,7 @@ class DNNClassifierTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       labels = tf.constant([[0.8], [0.], [0.2]], dtype=tf.float32)
       return features, labels
@@ -415,7 +415,7 @@ class DNNClassifierTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant([[1], [0], [0]], dtype=tf.int32)
 
@@ -453,7 +453,7 @@ class DNNClassifierTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant([[1], [0], [0]], dtype=tf.int32)
 
@@ -552,7 +552,7 @@ class DNNClassifierTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant([[1], [0], [0]], dtype=tf.int32)
 
@@ -594,7 +594,7 @@ class DNNClassifierTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant([[1], [0], [0]], dtype=tf.int32)
 
@@ -760,7 +760,7 @@ class DNNRegressorTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant([1., 0., 0.2], dtype=tf.float32)
 
@@ -878,7 +878,7 @@ class DNNRegressorTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant(labels, dtype=tf.float32)
 
@@ -912,7 +912,7 @@ class DNNRegressorTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant(labels, dtype=tf.float32)
 
@@ -1051,7 +1051,7 @@ class DNNRegressorTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant([1., 0., 0.2], dtype=tf.float32)
 
@@ -1092,7 +1092,7 @@ class DNNRegressorTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant([1., 0., 0.2], dtype=tf.float32)
 
@@ -1138,7 +1138,7 @@ class DNNRegressorTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant([1., 0., 0.2], dtype=tf.float32)
 
@@ -1171,7 +1171,7 @@ class DNNRegressorTest(tf.test.TestCase):
               values=tf.train.limit_epochs(
                   ['en', 'fr', 'zh'], num_epochs=num_epochs),
               indices=[[0, 0], [0, 1], [2, 0]],
-              shape=[3, 2])
+              dense_shape=[3, 2])
       }
       return features, tf.constant([1., 0., 0.2], dtype=tf.float32)
 

tensorflow/contrib/learn/python/learn/estimators/dynamic_rnn_estimator_test.py

@@ -109,7 +109,7 @@ class DynamicRnnEstimatorTest(tf.test.TestCase):
         'location': tf.SparseTensor(
             indices=[[0, 0], [1, 0], [2, 0]],
             values=['west_side', 'west_side', 'nyc'],
-            shape=[3, 1]),
+            dense_shape=[3, 1]),
         'wire_cast': tf.SparseTensor(
             indices=[[0, 0, 0], [0, 1, 0],
                      [1, 0, 0], [1, 1, 0], [1, 1, 1],
@@ -117,7 +117,7 @@ class DynamicRnnEstimatorTest(tf.test.TestCase):
             values=[b'marlo', b'stringer',
                     b'omar', b'stringer', b'marlo',
                     b'marlo'],
-            shape=[3, 2, 2]),
+            dense_shape=[3, 2, 2]),
         'measurements': tf.random_uniform([3, 2, 2], seed=4711)}
 
   def GetClassificationTargetsOrNone(self, mode):

tensorflow/contrib/learn/python/learn/estimators/linear_test.py

@@ -1253,7 +1253,7 @@ class LinearRegressorTest(tf.test.TestCase):
           'country': tf.SparseTensor(
               values=['IT', 'US', 'GB'],
               indices=[[0, 0], [1, 3], [2, 1]],
-              shape=[3, 5]),
+              dense_shape=[3, 5]),
           'weights': tf.constant([[3.0], [5.0], [7.0]])
       }, tf.constant([[1.55], [-1.25], [-3.0]])
 
@@ -1285,7 +1285,7 @@ class LinearRegressorTest(tf.test.TestCase):
           'country': tf.SparseTensor(
               values=['IT', 'US', 'GB'],
               indices=[[0, 0], [1, 3], [2, 1]],
-              shape=[3, 5]),
+              dense_shape=[3, 5]),
           'weights': tf.constant([[10.0], [10.0], [10.0]])
       }, tf.constant([[1.4], [-0.8], [2.6]])