1 files changed, 10 insertions, 10 deletions
diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
index 145d5c40fa..d3bb0fda57 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py
@@ -729,7 +729,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertLess(loss, 0.07)
 
   def testSdcaOptimizerRealValuedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and real valued features."""
+    """Tests LinearClassifier with SDCAOptimizer and real valued features."""
 
     def input_fn():
       return {
@@ -776,7 +776,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerBucketizedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and bucketized features."""
+    """Tests LinearClassifier with SDCAOptimizer and bucketized features."""
 
     def input_fn():
       return {
@@ -802,7 +802,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerSparseFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and sparse features."""
+    """Tests LinearClassifier with SDCAOptimizer and sparse features."""
 
     def input_fn():
       return {
@@ -833,7 +833,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerWeightedSparseFeatures(self):
-    """LinearClasssifier with SDCAOptimizer and weighted sparse features."""
+    """LinearClassifier with SDCAOptimizer and weighted sparse features."""
 
     def input_fn():
       return {
@@ -864,7 +864,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerCrossedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and crossed features."""
+    """Tests LinearClassifier with SDCAOptimizer and crossed features."""
 
     def input_fn():
       return {
@@ -897,7 +897,7 @@ class LinearClassifierTest(test.TestCase):
     self.assertGreater(scores['accuracy'], 0.9)
 
   def testSdcaOptimizerMixedFeatures(self):
-    """Tests LinearClasssifier with SDCAOptimizer and a mix of features."""
+    """Tests LinearClassifier with SDCAOptimizer and a mix of features."""
 
     def input_fn():
       return {
@@ -1509,7 +1509,7 @@ class LinearRegressorTest(test.TestCase):
     self.assertLess(loss, 0.05)
 
   def testSdcaOptimizerSparseFeaturesWithL1Reg(self):
-    """Tests LinearClasssifier with SDCAOptimizer and sparse features."""
+    """Tests LinearClassifier with SDCAOptimizer and sparse features."""
 
     def input_fn():
       return {
@@ -1581,7 +1581,7 @@ class LinearRegressorTest(test.TestCase):
     self.assertLess(l1_reg_weights_norm, no_l1_reg_weights_norm)
 
   def testSdcaOptimizerBiasOnly(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when it's the only feature present.
@@ -1614,7 +1614,7 @@ class LinearRegressorTest(test.TestCase):
         regressor.get_variable_value('linear/bias_weight')[0], 0.25, err=0.1)
 
   def testSdcaOptimizerBiasAndOtherColumns(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when there are other features present.
@@ -1676,7 +1676,7 @@ class LinearRegressorTest(test.TestCase):
         regressor.get_variable_value('linear/b/weight')[0], 0.0, err=0.05)
 
   def testSdcaOptimizerBiasAndOtherColumnsFabricatedCentered(self):
-    """Tests LinearClasssifier with SDCAOptimizer and validates bias weight."""
+    """Tests LinearClassifier with SDCAOptimizer and validates bias weight."""
 
     def input_fn():
       """Testing the bias weight when there are other features present.