Fix wide_deep_test's TEST_CSV path and only test the wide_deep model for the sake of time (#2566)

official/wide_deep/wide_deep_test.py

@@ -41,8 +41,7 @@ TEST_INPUT_VALUES = {
     'occupation': 'abc',
 }
 
-TEST_TRAINING_CSV = os.path.join(os.path.dirname(os.path.realpath(__file__)),
-                                 'wide_deep_test.csv')
+TEST_CSV = os.path.join(os.path.dirname(__file__), 'wide_deep_test.csv')
 
 
 class BaseTest(tf.test.TestCase):
@@ -80,31 +79,25 @@ class BaseTest(tf.test.TestCase):
     # Train for 1 step to initialize model and evaluate initial loss
     model.train(
         input_fn=wide_deep.input_fn(
-            TEST_TRAINING_CSV, num_epochs=1, shuffle=True, batch_size=1),
+            TEST_CSV, num_epochs=1, shuffle=True, batch_size=1),
         steps=1)
     initial_results = model.evaluate(
         input_fn=wide_deep.input_fn(
-            TEST_TRAINING_CSV, num_epochs=1, shuffle=False, batch_size=1))
+            TEST_CSV, num_epochs=1, shuffle=False, batch_size=1))
 
     # Train for 40 steps at batch size 2 and evaluate final loss
     model.train(
         input_fn=wide_deep.input_fn(
-            TEST_TRAINING_CSV, num_epochs=None, shuffle=True, batch_size=2),
+            TEST_CSV, num_epochs=None, shuffle=True, batch_size=2),
         steps=40)
     final_results = model.evaluate(
         input_fn=wide_deep.input_fn(
-            TEST_TRAINING_CSV, num_epochs=1, shuffle=False, batch_size=1))
+            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)
     self.assertLess(final_results['loss'], initial_results['loss'])
 
-  def test_deep_estimator_training(self):
-    self.build_and_test_estimator('deep')
-
-  def test_wide_estimator_training(self):
-    self.build_and_test_estimator('wide')
-
   def test_wide_deep_estimator_training(self):
     self.build_and_test_estimator('wide_deep')