Add test case for normalizer_fn support with sequence_feature_column

Signed-off-by: Yong Tang <yong.tang.github@outlook.com>

1 files changed, 40 insertions, 0 deletions

diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py
index 88f5d53516..57682c488e 100644
--- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py
+++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column_test.py
@@ -28,6 +28,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.ops import sparse_ops
 from tensorflow.python.platform import test
 from tensorflow.python.training import monitored_session
 
@@ -670,6 +671,7 @@ class SequenceNumericColumnTest(test.TestCase):
     self.assertEqual((1,), a.shape)
     self.assertEqual(0., a.default_value)
     self.assertEqual(dtypes.float32, a.dtype)
+    self.assertIsNone(a.normalizer_fn)
 
   def test_shape_saved_as_tuple(self):
     a = sfc.sequence_numeric_column('aaa', shape=[1, 2])
@@ -688,6 +690,10 @@ class SequenceNumericColumnTest(test.TestCase):
         ValueError, 'dtype must be convertible to float'):
       sfc.sequence_numeric_column('aaa', dtype=dtypes.string)
 
+  def test_normalizer_fn_must_be_callable(self):
+    with self.assertRaisesRegexp(TypeError, 'must be a callable'):
+      sfc.sequence_numeric_column('aaa', normalizer_fn='NotACallable')
+
   def test_get_sequence_dense_tensor(self):
     sparse_input = sparse_tensor.SparseTensorValue(
         # example 0, values [[0.], [1]]
@@ -708,6 +714,40 @@ class SequenceNumericColumnTest(test.TestCase):
       self.assertAllEqual(
           expected_dense_tensor, dense_tensor.eval(session=sess))
 
+  def test_get_sequence_dense_tensor_with_normalizer_fn(self):
+
+    def _increment_two(input_sparse_tensor):
+      return sparse_ops.sparse_add(
+          input_sparse_tensor,
+          sparse_tensor.SparseTensor(((0, 0), (1, 1)), (2.0, 2.0), (2, 2))
+      )
+
+    sparse_input = sparse_tensor.SparseTensorValue(
+        # example 0, values [[0.], [1]]
+        # example 1, [[10.]]
+        indices=((0, 0), (0, 1), (1, 0)),
+        values=(0., 1., 10.),
+        dense_shape=(2, 2))
+
+    # Before _increment_two:
+    #   [[0.], [1.]],
+    #   [[10.], [0.]],
+    # After _increment_two:
+    #   [[2.], [1.]],
+    #   [[10.], [2.]],
+    expected_dense_tensor = [
+        [[2.], [1.]],
+        [[10.], [2.]],
+    ]
+    numeric_column = sfc.sequence_numeric_column('aaa', normalizer_fn=_increment_two)
+
+    dense_tensor, _ = numeric_column._get_sequence_dense_tensor(
+        _LazyBuilder({'aaa': sparse_input}))
+
+    with monitored_session.MonitoredSession() as sess:
+      self.assertAllEqual(
+          expected_dense_tensor, dense_tensor.eval(session=sess))
+
   def test_get_sequence_dense_tensor_with_shape(self):
     """Tests get_sequence_dense_tensor with shape !=(1,)."""
     sparse_input = sparse_tensor.SparseTensorValue(