diff --git a/official/wide_deep/wide_deep_test.csv b/official/wide_deep/wide_deep_test.csv
index f1814ad66a9a8836c7eccd240082a38d6c6b578a..374397dbd1f979ba9320840732620d76b9725c91 100644
--- a/official/wide_deep/wide_deep_test.csv
+++ b/official/wide_deep/wide_deep_test.csv
@@ -18,3 +18,13 @@
 32,Private,186824,HS-grad,9,Never-married,Machine-op-inspct,Unmarried,,,0,0,40,,<=50K
 38,Private,28887,11th,7,Married-civ-spouse,Sales,Husband,,,0,0,50,,<=50K
 43,Self-emp-not-inc,292175,Masters,14,Divorced,Exec-managerial,Unmarried,,,0,0,45,,>50K
+40,Private,193524,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,,,0,0,60,,>50K
+56,Local-gov,216851,Bachelors,13,Married-civ-spouse,Tech-support,Husband,,,0,0,40,,>50K
+54,?,180211,Some-college,10,Married-civ-spouse,?,Husband,,,0,0,60,,>50K
+22,State-gov,311512,Some-college,10,Married-civ-spouse,Other-service,Husband,,,0,0,15,,<=50K
+31,Private,84154,Some-college,10,Married-civ-spouse,Sales,Husband,,,0,0,38,,>50K
+57,Federal-gov,337895,Bachelors,13,Married-civ-spouse,Prof-specialty,Husband,,,0,0,40,,>50K
+47,Private,51835,Prof-school,15,Married-civ-spouse,Prof-specialty,Wife,,,0,1902,60,,>50K
+50,Federal-gov,251585,Bachelors,13,Divorced,Exec-managerial,Not-in-family,,,0,0,55,,>50K
+25,Private,289980,HS-grad,9,Never-married,Handlers-cleaners,Not-in-family,,,0,0,35,,<=50K
+42,Private,116632,Doctorate,16,Married-civ-spouse,Prof-specialty,Husband,,,0,0,45,,>50K
diff --git a/official/wide_deep/wide_deep_test.py b/official/wide_deep/wide_deep_test.py
index ff19a699d31688f7bc5d68ab6a65957b038efe5a..b09cbdd54ddfa330c2f02b5726ee36288db08d34 100644
--- a/official/wide_deep/wide_deep_test.py
+++ b/official/wide_deep/wide_deep_test.py
@@ -85,18 +85,23 @@ class BaseTest(tf.test.TestCase):
         input_fn=lambda: wide_deep.input_fn(
             TEST_CSV, num_epochs=1, shuffle=False, batch_size=1))
 
-    # Train for 40 steps at batch size 2 and evaluate final loss
+    # Train for 100 epochs at batch size 3 and evaluate final loss
     model.train(
         input_fn=lambda: wide_deep.input_fn(
-            TEST_CSV, num_epochs=None, shuffle=True, batch_size=2),
-        steps=40)
+            TEST_CSV, num_epochs=100, shuffle=True, batch_size=3))
     final_results = model.evaluate(
         input_fn=lambda: wide_deep.input_fn(
             TEST_CSV, num_epochs=1, shuffle=False, batch_size=1))
 
     print('%s initial results:' % model_type, initial_results)
     print('%s final results:' % model_type, final_results)
+
+    # Ensure loss has decreased, while accuracy and both AUCs have increased.
     self.assertLess(final_results['loss'], initial_results['loss'])
+    self.assertGreater(final_results['auc'], initial_results['auc'])
+    self.assertGreater(final_results['auc_precision_recall'],
+                       initial_results['auc_precision_recall'])
+    self.assertGreater(final_results['accuracy'], initial_results['accuracy'])
 
   def test_wide_deep_estimator_training(self):
     self.build_and_test_estimator('wide_deep')