diff --git a/tensorflow/contrib/layers/python/layers/feature_column_ops.py b/tensorflow/contrib/layers/python/layers/feature_column_ops.py
index fa0047f05d8..78affea44cb 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column_ops.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column_ops.py
@@ -97,10 +97,13 @@ def _input_from_feature_columns(columns_to_tensors,
                                 trainable,
                                 scope,
                                 output_rank,
-                                default_name):
+                                default_name,
+                                cols_to_outs=None):
   """Implementation of `input_from(_sequence)_feature_columns`."""
   columns_to_tensors = columns_to_tensors.copy()
   check_feature_columns(feature_columns)
+  if cols_to_outs is not None and not isinstance(cols_to_outs, dict):
+    raise ValueError('cols_to_outs must be a dict unless None')
   with variable_scope.variable_scope(scope,
                                      default_name=default_name,
                                      values=columns_to_tensors.values()):
@@ -144,6 +147,8 @@ def _input_from_feature_columns(columns_to_tensors,
           except ValueError as e:
             raise ValueError('Error creating input layer for column: {}.\n'
                              '{}, {}'.format(column.name, e, ee))
+        if cols_to_outs is not None:
+          cols_to_outs[column] = output_tensors[-1]
     return array_ops.concat(output_tensors, output_rank - 1)
 
 
@@ -151,7 +156,8 @@ def input_from_feature_columns(columns_to_tensors,
                                feature_columns,
                                weight_collections=None,
                                trainable=True,
-                               scope=None):
+                               scope=None,
+                               cols_to_outs=None):
   """A tf.contrib.layers style input layer builder based on FeatureColumns.
 
   Generally a single example in training data is described with feature columns.
@@ -196,6 +202,8 @@ def input_from_feature_columns(columns_to_tensors,
     trainable: If `True` also add variables to the graph collection
       `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
     scope: Optional scope for variable_scope.
+    cols_to_outs: Optional dict from feature column to output tensor,
+      which is concatenated into the returned tensor.
 
   Returns:
     A Tensor which can be consumed by hidden layers in the neural network.
@@ -209,7 +217,8 @@ def input_from_feature_columns(columns_to_tensors,
                                      trainable,
                                      scope,
                                      output_rank=2,
-                                     default_name='input_from_feature_columns')
+                                     default_name='input_from_feature_columns',
+                                     cols_to_outs=cols_to_outs)
 
 
 @experimental
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 fbfa0e32de5..e6bbd86ab72 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column_ops_test.py
@@ -607,6 +607,31 @@ class CreateInputLayersForDNNsTest(test.TestCase):
       # Verify cross compatibility: Core builder output should equal to contrib.
       self.assertAllEqual(output.eval().shape, output_core.eval().shape)
 
+  def testAllDNNColumnsWithColumnwiseOutputs(self):
+    sparse_column = feature_column.sparse_column_with_keys(
+        "ids", ["a", "b", "c", "unseen"])
+    real_valued_column = feature_column.real_valued_column("income", 2)
+    one_hot_column = feature_column.one_hot_column(sparse_column)
+    embedding_column = feature_column.embedding_column(sparse_column, 10)
+    features = {
+        "ids":
+            sparse_tensor.SparseTensor(
+                values=["c", "b", "a"],
+                indices=[[0, 0], [1, 0], [2, 0]],
+                dense_shape=[3, 1]),
+        "income":
+            constant_op.constant([[20.3, 10], [110.3, 0.4], [-3.0, 30.4]]),
+    }
+    columns = [one_hot_column, embedding_column, real_valued_column]
+    cols_to_outs = {}
+    feature_column_ops.input_from_feature_columns(
+        features, columns, cols_to_outs=cols_to_outs)
+    with self.test_session():
+      variables_lib.global_variables_initializer().run()
+      lookup_ops.tables_initializer().run()
+      for column in columns:
+        self.assertTrue(column in cols_to_outs)
+
   def testRealValuedColumn(self):
     real_valued = feature_column.real_valued_column("price")
     features = {"price": constant_op.constant([[20.], [110], [-3]])}