Automated g4 rollback of changelist 201011811

PiperOrigin-RevId: 201033171

2 files changed, 4 insertions, 59 deletions

diff --git a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py
index 05bcdac2ca..84a413c791 100644
--- a/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py
+++ b/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py
@@ -346,8 +346,7 @@ def sequence_numeric_column(
     key,
     shape=(1,),
     default_value=0.,
-    dtype=dtypes.float32,
-    normalizer_fn=None):
+    dtype=dtypes.float32):
   """Returns a feature column that represents sequences of numeric data.
 
   Example:
@@ -371,12 +370,6 @@ def sequence_numeric_column(
     default_value: A single value compatible with `dtype` that is used for
       padding the sparse data into a dense `Tensor`.
     dtype: The type of values.
-    normalizer_fn: If not `None`, a function that can be used to normalize the
-      value of the tensor after `default_value` is applied for parsing.
-      Normalizer function takes the input `Tensor` as its argument, and returns
-      the output `Tensor`. (e.g. lambda x: (x - 3.0) / 4.2). Please note that
-      even though the most common use case of this function is normalization, it
-      can be used for any kind of Tensorflow transformations.
 
   Returns:
     A `_SequenceNumericColumn`.
@@ -390,16 +383,12 @@ def sequence_numeric_column(
   if not (dtype.is_integer or dtype.is_floating):
     raise ValueError('dtype must be convertible to float. '
                      'dtype: {}, key: {}'.format(dtype, key))
-  if normalizer_fn is not None and not callable(normalizer_fn):
-    raise TypeError(
-        'normalizer_fn must be a callable. Given: {}'.format(normalizer_fn))
 
   return _SequenceNumericColumn(
       key,
       shape=shape,
       default_value=default_value,
-      dtype=dtype,
-      normalizer_fn=normalizer_fn)
+      dtype=dtype)
 
 
 def _assert_all_equal_and_return(tensors, name=None):
@@ -418,7 +407,7 @@ class _SequenceNumericColumn(
     fc._SequenceDenseColumn,
     collections.namedtuple(
         '_SequenceNumericColumn',
-        ['key', 'shape', 'default_value', 'dtype', 'normalizer_fn'])):
+        ['key', 'shape', 'default_value', 'dtype'])):
   """Represents sequences of numeric data."""
 
   @property
@@ -430,10 +419,7 @@ class _SequenceNumericColumn(
     return {self.key: parsing_ops.VarLenFeature(self.dtype)}
 
   def _transform_feature(self, inputs):
-    input_tensor = inputs.get(self.key)
-    if self.normalizer_fn is not None:
-      input_tensor = self.normalizer_fn(input_tensor)
-    return input_tensor
+    return inputs.get(self.key)
 
   @property
   def _variable_shape(self):
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 45d7b74046..ee74cf56dc 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
@@ -28,7 +28,6 @@ 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
 
@@ -948,7 +947,6 @@ 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])
@@ -967,10 +965,6 @@ 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]]
@@ -991,41 +985,6 @@ 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(