message: Update keras feature column test to explicitly use keras symbols.
diff: @@ -26,6 +26,7 @@ from tensorflow.python.feature_column import feature_column_lib as fc
  from tensorflow.python.keras import keras_parameterized
  from tensorflow.python.keras import metrics as metrics_module
  from tensorflow.python.keras import testing_utils
  +from tensorflow.python.keras.feature_column import dense_features as df
  from tensorflow.python.keras.utils import np_utils
  from tensorflow.python.platform import test
  @@ -34,7 +35,7 @@ class TestDNNModel(keras.models.Model):
  def __init__(self, feature_columns, units, name=None, **kwargs):
  super(TestDNNModel, self).__init__(name=name, **kwargs)
  - self._input_layer = fc.DenseFeatures(feature_columns, name='input_layer')
  + self._input_layer = df.DenseFeatures(feature_columns, name='input_layer')
  self._dense_layer = keras.layers.Dense(units, name='dense_layer')
  def call(self, features):
  @@ -52,7 +53,7 @@ class FeatureColumnsIntegrationTest(keras_parameterized.TestCase):
  def test_sequential_model(self):
  columns = [fc.numeric_column('a')]
  model = keras.models.Sequential([
  - fc.DenseFeatures(columns),
  + df.DenseFeatures(columns),
  keras.layers.Dense(64, activation='relu'),
  keras.layers.Dense(20, activation='softmax')
  ])
  @@ -74,7 +75,7 @@ class FeatureColumnsIntegrationTest(keras_parameterized.TestCase):
  def test_sequential_model_with_ds_input(self):
  columns = [fc.numeric_column('a')]
  model = keras.models.Sequential([
  - fc.DenseFeatures(columns),
  + df.DenseFeatures(columns),
  keras.layers.Dense(64, activation='relu'),
  keras.layers.Dense(20, activation='softmax')
  ])
  @@ -112,7 +113,7 @@ class FeatureColumnsIntegrationTest(keras_parameterized.TestCase):
  crossed_feature = fc.indicator_column(crossed_feature)
  feature_columns.append(crossed_feature)
  - feature_layer = fc.DenseFeatures(feature_columns)
  + feature_layer = df.DenseFeatures(feature_columns)
  model = keras.models.Sequential([
  feature_layer,
  @@ -185,7 +186,7 @@ class FeatureColumnsIntegrationTest(keras_parameterized.TestCase):
  col_a = fc.numeric_column('a')
  col_b = fc.numeric_column('b')
  - feature_layer = fc.DenseFeatures([col_a, col_b], name='fc')
  + feature_layer = df.DenseFeatures([col_a, col_b], name='fc')
  dense = keras.layers.Dense(4)
  # This seems problematic.... We probably need something for DenseFeatures
  @@ -213,8 +214,8 @@ class FeatureColumnsIntegrationTest(keras_parameterized.TestCase):
  col_b = fc.numeric_column('b')
  col_c = fc.numeric_column('c')
  - fc1 = fc.DenseFeatures([col_a, col_b], name='fc1')
  - fc2 = fc.DenseFeatures([col_b, col_c], name='fc2')
  + fc1 = df.DenseFeatures([col_a, col_b], name='fc1')
  + fc2 = df.DenseFeatures([col_b, col_c], name='fc2')
  dense = keras.layers.Dense(4)
  # This seems problematic.... We probably need something for DenseFeatures
  @@ -289,7 +290,7 @@ class FeatureColumnsIntegrationTest(keras_parameterized.TestCase):
  categorical_cols = [fc.categorical_column_with_hash_bucket('cabin', 10)]
  feature_cols = ([fc.numeric_column('age')]
  + [fc.indicator_column(cc) for cc in categorical_cols])
  - layers = [fc.DenseFeatures(feature_cols),
  + layers = [df.DenseFeatures(feature_cols),
  keras.layers.Dense(128),
  keras.layers.Dense(1)]
  