Make contrib real_valued_column cross compatible with core feature_column builders.

Change: 155090692

3 files changed, 43 insertions, 3 deletions

diff --git a/tensorflow/contrib/layers/BUILD b/tensorflow/contrib/layers/BUILD
index aba8eabe10..fe661a5625 100644
--- a/tensorflow/contrib/layers/BUILD
+++ b/tensorflow/contrib/layers/BUILD
@@ -108,6 +108,7 @@ tf_custom_op_py_library(
         "//tensorflow/python:util",
         "//tensorflow/python:variable_scope",
         "//tensorflow/python:variables",
+        "//tensorflow/python/feature_column",
         "@six_archive//:six",
     ],
 )
diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py
index d6d5bf2294..04fe2370d1 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column.py
@@ -136,8 +136,10 @@ from tensorflow.contrib.layers.python.layers import layers
 from tensorflow.contrib.layers.python.ops import bucketization_op
 from tensorflow.contrib.layers.python.ops import sparse_feature_cross_op
 from tensorflow.contrib.layers.python.ops import sparse_ops as contrib_sparse_ops
+from tensorflow.python.feature_column import feature_column as fc_core
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import sparse_tensor as sparse_tensor_py
+from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
@@ -1497,9 +1499,12 @@ def _real_valued_var_len_column(column_name,
                                  is_sparse)
 
 
-class _RealValuedColumn(_FeatureColumn, collections.namedtuple(
-    "_RealValuedColumn",
-    ["column_name", "dimension", "default_value", "dtype", "normalizer"])):
+class _RealValuedColumn(
+    _FeatureColumn,
+    fc_core._DenseColumn,  # pylint: disable=protected-access
+    collections.namedtuple(
+        "_RealValuedColumn",
+        ["column_name", "dimension", "default_value", "dtype", "normalizer"])):
   """Represents a real valued feature column also known as continuous features.
 
   Instances of this class are immutable. The dictionary returned by InputBuilder
@@ -1569,6 +1574,23 @@ class _RealValuedColumn(_FeatureColumn, collections.namedtuple(
   def _to_dense_tensor(self, input_tensor):
     return input_tensor
 
+  @property
+  def _variable_shape(self):
+    return tensor_shape.TensorShape((self.dimension))
+
+  def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
+    del weight_collections
+    del trainable
+    return inputs.get(self)
+
+  def _transform_feature(self, inputs):
+    return math_ops.to_float(
+        self._normalized_input_tensor(inputs.get(self.name)))
+
+  @property
+  def _parse_example_config(self):
+    return self.config
+
 
 def real_valued_column(column_name,
                        dimension=1,
diff --git a/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py b/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py
index 632836fee4..b2dad0162e 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py
@@ -27,6 +27,7 @@ from tensorflow.contrib.layers.python.layers import feature_column
 from tensorflow.contrib.layers.python.layers import feature_column_ops
 from tensorflow.core.example import example_pb2
 from tensorflow.core.example import feature_pb2
+from tensorflow.python.feature_column import feature_column as fc_core
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -610,6 +611,10 @@ class CreateInputLayersForDNNsTest(test.TestCase):
                                                            [real_valued])
     with self.test_session():
       self.assertAllClose(output.eval(), features["price"].eval())
+      # Verify cross compatibility: Core builder output should equal to contrib.
+      self.assertAllClose(output.eval(),
+                          fc_core.make_input_layer(features,
+                                                   [real_valued]).eval())
 
   def testRealValuedColumnWithMultiDimensions(self):
     real_valued = feature_column.real_valued_column("price", 2)
@@ -620,6 +625,10 @@ class CreateInputLayersForDNNsTest(test.TestCase):
                                                            [real_valued])
     with self.test_session():
       self.assertAllClose(output.eval(), features["price"].eval())
+      # Verify cross compatibility: Core builder output should equal to contrib.
+      self.assertAllClose(output.eval(),
+                          fc_core.make_input_layer(features,
+                                                   [real_valued]).eval())
 
   def testRealValuedColumnSparse(self):
     sparse_real_valued = feature_column._real_valued_var_len_column(
@@ -640,6 +649,10 @@ class CreateInputLayersForDNNsTest(test.TestCase):
                                                            [real_valued])
     with self.test_session():
       self.assertAllClose(output.eval(), features["price"].eval() - 2)
+      # Verify cross compatibility: Core builder output should equal to contrib.
+      self.assertAllClose(output.eval(),
+                          fc_core.make_input_layer(features,
+                                                   [real_valued]).eval())
 
   def testRealValuedColumnWithMultiDimensionsAndNormalizer(self):
     real_valued = feature_column.real_valued_column(
@@ -651,6 +664,10 @@ class CreateInputLayersForDNNsTest(test.TestCase):
                                                            [real_valued])
     with self.test_session():
       self.assertAllClose(output.eval(), features["price"].eval() - 2)
+      # Verify cross compatibility: Core builder output should equal to contrib.
+      self.assertAllClose(output.eval(),
+                          fc_core.make_input_layer(features,
+                                                   [real_valued]).eval())
 
   def testBucketizedColumnWithNormalizerSucceedsForDNN(self):
     bucket = feature_column.bucketized_column(