Merge branch 'master' into ENH/feature_importances_for_boosted_tree

6 files changed, 1143 insertions, 17 deletions

diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD
index 77f62df99d..6db311d52d 100644
--- a/tensorflow/contrib/estimator/BUILD
+++ b/tensorflow/contrib/estimator/BUILD
@@ -18,6 +18,7 @@ py_library(
         ":boosted_trees",
         ":dnn",
         ":dnn_linear_combined",
+        ":dnn_with_layer_annotations",
         ":early_stopping",
         ":export",
         ":exporter",
@@ -127,6 +128,61 @@ py_test(
 )
 
 py_library(
+    name = "dnn_with_layer_annotations",
+    srcs = ["python/estimator/dnn_with_layer_annotations.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:init_ops",
+        "//tensorflow/python:layers",
+        "//tensorflow/python:nn",
+        "//tensorflow/python:partitioned_variables",
+        "//tensorflow/python:summary",
+        "//tensorflow/python:variable_scope",
+        "//tensorflow/python/estimator",
+        "//tensorflow/python/estimator:head",
+        "//tensorflow/python/estimator:model_fn",
+        "//tensorflow/python/estimator:optimizers",
+        "//tensorflow/python/feature_column",
+        "//tensorflow/python/ops/losses",
+        "//tensorflow/python/saved_model:utils",
+    ],
+)
+
+py_test(
+    name = "dnn_with_layer_annotations_test",
+    size = "medium",
+    srcs = ["python/estimator/dnn_with_layer_annotations_test.py"],
+    shard_count = 4,
+    srcs_version = "PY2AND3",
+    tags = [
+        "no_pip",
+        "notsan",  # b/67510291
+    ],
+    deps = [
+        ":dnn_with_layer_annotations",
+        "//tensorflow/core:protos_all_py",
+        "//tensorflow/python:client_testlib",
+        "//tensorflow/python:data_flow_ops",
+        "//tensorflow/python:dtypes",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:parsing_ops",
+        "//tensorflow/python:platform",
+        "//tensorflow/python:summary",
+        "//tensorflow/python:training",
+        "//tensorflow/python/estimator:dnn",
+        "//tensorflow/python/estimator:dnn_testing_utils",
+        "//tensorflow/python/estimator:export_export",
+        "//tensorflow/python/estimator:numpy_io",
+        "//tensorflow/python/estimator:pandas_io",
+        "//tensorflow/python/estimator:prediction_keys",
+        "//tensorflow/python/feature_column",
+        "@six_archive//:six",
+    ],
+)
+
+py_library(
     name = "dnn_linear_combined",
     srcs = ["python/estimator/dnn_linear_combined.py"],
     srcs_version = "PY2AND3",
@@ -446,6 +502,7 @@ py_library(
         "//tensorflow/python/estimator",
         "//tensorflow/python/estimator:head",
         "//tensorflow/python/estimator:optimizers",
+        "//tensorflow/python/ops/losses",
         "@six_archive//:six",
     ],
 )
diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py
index 258860f263..78914ecaca 100644
--- a/tensorflow/contrib/estimator/__init__.py
+++ b/tensorflow/contrib/estimator/__init__.py
@@ -22,6 +22,7 @@ from __future__ import print_function
 from tensorflow.contrib.estimator.python.estimator.baseline import *
 from tensorflow.contrib.estimator.python.estimator.boosted_trees import *
 from tensorflow.contrib.estimator.python.estimator.dnn import *
+from tensorflow.contrib.estimator.python.estimator.dnn_with_layer_annotations import *
 from tensorflow.contrib.estimator.python.estimator.dnn_linear_combined import *
 from tensorflow.contrib.estimator.python.estimator.early_stopping import *
 from tensorflow.contrib.estimator.python.estimator.export import *
@@ -76,6 +77,8 @@ _allowed_symbols = [
     'build_raw_supervised_input_receiver_fn',
     'build_supervised_input_receiver_fn_from_input_fn',
     'SavedModelEstimator'
+    'DNNClassifierWithLayerAnnotations',
+    'DNNRegressorWithLayerAnnotations',
 ]
 
 remove_undocumented(__name__, allowed_exception_list=_allowed_symbols)
diff --git a/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py b/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py
new file mode 100644
index 0000000000..152431d1b2
--- /dev/null
+++ b/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py
@@ -0,0 +1,434 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Deep Neural Network estimators with layer annotations."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import contextlib
+import pickle
+
+from google.protobuf.any_pb2 import Any
+
+from tensorflow.python.estimator import estimator
+from tensorflow.python.estimator import model_fn
+from tensorflow.python.estimator.canned import dnn
+from tensorflow.python.feature_column import feature_column as feature_column_lib
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import nn
+from tensorflow.python.ops.losses import losses
+from tensorflow.python.saved_model import utils as saved_model_utils
+
+
+class LayerAnnotationsCollectionNames(object):
+  """Names for the collections containing the annotations."""
+
+  UNPROCESSED_FEATURES = 'layer_annotations/unprocessed_features'
+  PROCESSED_FEATURES = 'layer_annotatons/processed_features'
+  FEATURE_COLUMNS = 'layer_annotations/feature_columns'
+
+  @classmethod
+  def keys(cls, collection_name):
+    return '%s/keys' % collection_name
+
+  @classmethod
+  def values(cls, collection_name):
+    return '%s/values' % collection_name
+
+
+def serialize_feature_column(feature_column):
+  if isinstance(feature_column, feature_column_lib._EmbeddingColumn):  # pylint: disable=protected-access
+    # We can't pickle nested functions, and we don't need the value of
+    # layer_creator in most cases anyway, so just discard its value.
+    args = feature_column._asdict()
+    args['layer_creator'] = None
+    temp = type(feature_column)(**args)
+    return pickle.dumps(temp)
+  return pickle.dumps(feature_column)
+
+
+def _to_any_wrapped_tensor_info(tensor):
+  """Converts a `Tensor` to a `TensorInfo` wrapped in a proto `Any`."""
+  any_buf = Any()
+  tensor_info = saved_model_utils.build_tensor_info(tensor)
+  any_buf.Pack(tensor_info)
+  return any_buf
+
+
+def make_input_layer_with_layer_annotations(original_input_layer, mode):
+  """Make an input_layer replacement function that adds layer annotations."""
+
+  def input_layer_with_layer_annotations(features,
+                                         feature_columns,
+                                         weight_collections=None,
+                                         trainable=True,
+                                         cols_to_vars=None,
+                                         cols_to_output_tensors=None):
+    """Returns a dense `Tensor` as input layer based on given `feature_columns`.
+
+    Generally a single example in training data is described with
+    FeatureColumns.
+    At the first layer of the model, this column oriented data should be
+    converted
+    to a single `Tensor`.
+
+    This is like tf.feature_column.input_layer, except with added
+    Integrated-Gradient annotations.
+
+    Args:
+      features: A mapping from key to tensors. `_FeatureColumn`s look up via
+        these keys. For example `numeric_column('price')` will look at 'price'
+        key in this dict. Values can be a `SparseTensor` or a `Tensor` depends
+        on corresponding `_FeatureColumn`.
+      feature_columns: An iterable containing the FeatureColumns to use as
+        inputs to your model. All items should be instances of classes derived
+        from `_DenseColumn` such as `numeric_column`, `embedding_column`,
+        `bucketized_column`, `indicator_column`. If you have categorical
+        features, you can wrap them with an `embedding_column` or
+        `indicator_column`.
+      weight_collections: A list of collection names to which the Variable will
+        be added. Note that variables will also be added to collections
+        `tf.GraphKeys.GLOBAL_VARIABLES` and `ops.GraphKeys.MODEL_VARIABLES`.
+      trainable: If `True` also add the variable to the graph collection
+        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+      cols_to_vars: If not `None`, must be a dictionary that will be filled with
+        a mapping from `_FeatureColumn` to list of `Variable`s.  For example,
+        after the call, we might have cols_to_vars = {_EmbeddingColumn(
+        categorical_column=_HashedCategoricalColumn( key='sparse_feature',
+        hash_bucket_size=5, dtype=tf.string), dimension=10): [<tf.Variable
+        'some_variable:0' shape=(5, 10), <tf.Variable 'some_variable:1'
+          shape=(5, 10)]} If a column creates no variables, its value will be an
+          empty list.
+      cols_to_output_tensors: If not `None`, must be a dictionary that will be
+        filled with a mapping from '_FeatureColumn' to the associated output
+        `Tensor`s.
+
+    Returns:
+      A `Tensor` which represents input layer of a model. Its shape
+      is (batch_size, first_layer_dimension) and its dtype is `float32`.
+      first_layer_dimension is determined based on given `feature_columns`.
+
+    Raises:
+      ValueError: features and feature_columns have different lengths.
+    """
+
+    local_cols_to_output_tensors = {}
+    input_layer = original_input_layer(
+        features=features,
+        feature_columns=feature_columns,
+        weight_collections=weight_collections,
+        trainable=trainable,
+        cols_to_vars=cols_to_vars,
+        cols_to_output_tensors=local_cols_to_output_tensors)
+
+    if cols_to_output_tensors is not None:
+      cols_to_output_tensors = local_cols_to_output_tensors
+
+    if mode and mode == model_fn.ModeKeys.PREDICT:
+      # Only annotate in PREDICT mode.
+
+      # Annotate features.
+      # These are the parsed Tensors, before embedding.
+
+      # Only annotate features used by FeatureColumns.
+      # We figure which ones are used by FeatureColumns by creating a parsing
+      # spec and looking at the keys.
+      spec = feature_column_lib.make_parse_example_spec(feature_columns)
+      for key in spec.keys():
+        tensor = features[key]
+        ops.add_to_collection(
+            LayerAnnotationsCollectionNames.keys(
+                LayerAnnotationsCollectionNames.UNPROCESSED_FEATURES), key)
+        ops.add_to_collection(
+            LayerAnnotationsCollectionNames.values(
+                LayerAnnotationsCollectionNames.UNPROCESSED_FEATURES),
+            _to_any_wrapped_tensor_info(tensor))
+
+      # Annotate feature columns.
+      for column in feature_columns:
+        # TODO(cyfoo): Find a better way to serialize and deserialize
+        # _FeatureColumn.
+        ops.add_to_collection(LayerAnnotationsCollectionNames.FEATURE_COLUMNS,
+                              serialize_feature_column(column))
+
+      for column, tensor in local_cols_to_output_tensors.items():
+        ops.add_to_collection(
+            LayerAnnotationsCollectionNames.keys(
+                LayerAnnotationsCollectionNames.PROCESSED_FEATURES),
+            column.name)
+        ops.add_to_collection(
+            LayerAnnotationsCollectionNames.values(
+                LayerAnnotationsCollectionNames.PROCESSED_FEATURES),
+            _to_any_wrapped_tensor_info(tensor))
+
+    return input_layer
+
+  return input_layer_with_layer_annotations
+
+
+@contextlib.contextmanager
+def _monkey_patch(module, function, replacement):
+  old_function = getattr(module, function)
+  setattr(module, function, replacement)
+  yield
+  setattr(module, function, old_function)
+
+
+def DNNClassifierWithLayerAnnotations(  # pylint: disable=invalid-name
+    hidden_units,
+    feature_columns,
+    model_dir=None,
+    n_classes=2,
+    weight_column=None,
+    label_vocabulary=None,
+    optimizer='Adagrad',
+    activation_fn=nn.relu,
+    dropout=None,
+    input_layer_partitioner=None,
+    config=None,
+    warm_start_from=None,
+    loss_reduction=losses.Reduction.SUM):
+  """A classifier for TensorFlow DNN models with layer annotations.
+
+  This classifier is fuctionally identical to estimator.DNNClassifier as far as
+  training and evaluating models is concerned. The key difference is that this
+  classifier adds additional layer annotations, which can be used for computing
+  Integrated Gradients.
+
+  Integrated Gradients is a method for attributing a classifier's predictions
+  to its input features (https://arxiv.org/pdf/1703.01365.pdf). Given an input
+  instance, the method assigns attribution scores to individual features in
+  proportion to the feature's importance to the classifier's prediction.
+
+  See estimator.DNNClassifer for example code for training and evaluating models
+  using this classifier.
+
+  This classifier is checkpoint-compatible with estimator.DNNClassifier and
+  therefore the following should work seamlessly:
+
+  # Instantiate ordinary estimator as usual.
+  estimator = tf.estimator.DNNClassifier(
+    config, feature_columns, hidden_units, ...)
+
+  # Train estimator, export checkpoint.
+  tf.estimator.train_and_evaluate(estimator, ...)
+
+  # Instantiate estimator with annotations with the same configuration as the
+  # ordinary estimator.
+  estimator_with_annotations = (
+    tf.contrib.estimator.DNNClassifierWithLayerAnnotations(
+      config, feature_columns, hidden_units, ...))
+
+  # Call export_savedmodel with the same arguments as the ordinary estimator,
+  # using the checkpoint produced for the ordinary estimator.
+  estimator_with_annotations.export_saved_model(
+    export_dir_base, serving_input_receiver, ...
+    checkpoint_path='/path/to/ordinary/estimator/checkpoint/model.ckpt-1234')
+
+  Args:
+    hidden_units: Iterable of number hidden units per layer. All layers are
+      fully connected. Ex. `[64, 32]` means first layer has 64 nodes and second
+      one has 32.
+    feature_columns: An iterable containing all the feature columns used by the
+      model. All items in the set should be instances of classes derived from
+      `_FeatureColumn`.
+    model_dir: Directory to save model parameters, graph and etc. This can also
+      be used to load checkpoints from the directory into a estimator to
+      continue training a previously saved model.
+    n_classes: Number of label classes. Defaults to 2, namely binary
+      classification. Must be > 1.
+    weight_column: A string or a `_NumericColumn` created by
+      `tf.feature_column.numeric_column` defining feature column representing
+      weights. It is used to down weight or boost examples during training. It
+      will be multiplied by the loss of the example. If it is a string, it is
+      used as a key to fetch weight tensor from the `features`. If it is a
+      `_NumericColumn`, raw tensor is fetched by key `weight_column.key`, then
+      weight_column.normalizer_fn is applied on it to get weight tensor.
+    label_vocabulary: A list of strings represents possible label values. If
+      given, labels must be string type and have any value in
+      `label_vocabulary`. If it is not given, that means labels are already
+      encoded as integer or float within [0, 1] for `n_classes=2` and encoded as
+      integer values in {0, 1,..., n_classes-1} for `n_classes`>2 . Also there
+      will be errors if vocabulary is not provided and labels are string.
+    optimizer: An instance of `tf.Optimizer` used to train the model. Defaults
+      to Adagrad optimizer.
+    activation_fn: Activation function applied to each layer. If `None`, will
+      use `tf.nn.relu`.
+    dropout: When not `None`, the probability we will drop out a given
+      coordinate.
+    input_layer_partitioner: Optional. Partitioner for input layer. Defaults to
+      `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
+    config: `RunConfig` object to configure the runtime settings.
+    warm_start_from: A string filepath to a checkpoint to warm-start from, or a
+      `WarmStartSettings` object to fully configure warm-starting.  If the
+      string filepath is provided instead of a `WarmStartSettings`, then all
+      weights are warm-started, and it is assumed that vocabularies and Tensor
+      names are unchanged.
+    loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to
+      reduce training loss over batch. Defaults to `SUM`.
+
+  Returns:
+    DNNClassifier with layer annotations.
+  """
+
+  original = dnn.DNNClassifier(
+      hidden_units=hidden_units,
+      feature_columns=feature_columns,
+      model_dir=model_dir,
+      n_classes=n_classes,
+      weight_column=weight_column,
+      label_vocabulary=label_vocabulary,
+      optimizer=optimizer,
+      activation_fn=activation_fn,
+      dropout=dropout,
+      input_layer_partitioner=input_layer_partitioner,
+      config=config,
+      warm_start_from=warm_start_from,
+      loss_reduction=loss_reduction)
+
+  def _model_fn(features, labels, mode, config):
+    with _monkey_patch(
+        feature_column_lib, 'input_layer',
+        make_input_layer_with_layer_annotations(feature_column_lib.input_layer,
+                                                mode)):
+      return original.model_fn(features, labels, mode, config)
+
+  return estimator.Estimator(
+      model_fn=_model_fn,
+      model_dir=model_dir,
+      config=config,
+      warm_start_from=warm_start_from)
+
+
+def DNNRegressorWithLayerAnnotations(  # pylint: disable=invalid-name
+    hidden_units,
+    feature_columns,
+    model_dir=None,
+    label_dimension=1,
+    weight_column=None,
+    optimizer='Adagrad',
+    activation_fn=nn.relu,
+    dropout=None,
+    input_layer_partitioner=None,
+    config=None,
+    warm_start_from=None,
+    loss_reduction=losses.Reduction.SUM,
+):
+  """A regressor for TensorFlow DNN models with layer annotations.
+
+  This regressor is fuctionally identical to estimator.DNNRegressor as far as
+  training and evaluating models is concerned. The key difference is that this
+  classifier adds additional layer annotations, which can be used for computing
+  Integrated Gradients.
+
+  Integrated Gradients is a method for attributing a classifier's predictions
+  to its input features (https://arxiv.org/pdf/1703.01365.pdf). Given an input
+  instance, the method assigns attribution scores to individual features in
+  proportion to the feature's importance to the classifier's prediction.
+
+  See estimator.DNNRegressor for example code for training and evaluating models
+  using this regressor.
+
+  This regressor is checkpoint-compatible with estimator.DNNRegressor and
+  therefore the following should work seamlessly:
+
+  # Instantiate ordinary estimator as usual.
+  estimator = tf.estimator.DNNRegressor(
+    config, feature_columns, hidden_units, ...)
+
+  # Train estimator, export checkpoint.
+  tf.estimator.train_and_evaluate(estimator, ...)
+
+  # Instantiate estimator with annotations with the same configuration as the
+  # ordinary estimator.
+  estimator_with_annotations = (
+    tf.contrib.estimator.DNNRegressorWithLayerAnnotations(
+      config, feature_columns, hidden_units, ...))
+
+  # Call export_savedmodel with the same arguments as the ordinary estimator,
+  # using the checkpoint produced for the ordinary estimator.
+  estimator_with_annotations.export_saved_model(
+    export_dir_base, serving_input_receiver, ...
+    checkpoint_path='/path/to/ordinary/estimator/checkpoint/model.ckpt-1234')
+
+  Args:
+    hidden_units: Iterable of number hidden units per layer. All layers are
+      fully connected. Ex. `[64, 32]` means first layer has 64 nodes and second
+      one has 32.
+    feature_columns: An iterable containing all the feature columns used by the
+      model. All items in the set should be instances of classes derived from
+      `_FeatureColumn`.
+    model_dir: Directory to save model parameters, graph and etc. This can also
+      be used to load checkpoints from the directory into a estimator to
+      continue training a previously saved model.
+    label_dimension: Number of regression targets per example. This is the size
+      of the last dimension of the labels and logits `Tensor` objects
+      (typically, these have shape `[batch_size, label_dimension]`).
+    weight_column: A string or a `_NumericColumn` created by
+      `tf.feature_column.numeric_column` defining feature column representing
+      weights. It is used to down weight or boost examples during training. It
+      will be multiplied by the loss of the example. If it is a string, it is
+      used as a key to fetch weight tensor from the `features`. If it is a
+      `_NumericColumn`, raw tensor is fetched by key `weight_column.key`, then
+      weight_column.normalizer_fn is applied on it to get weight tensor.
+    optimizer: An instance of `tf.Optimizer` used to train the model. Defaults
+      to Adagrad optimizer.
+    activation_fn: Activation function applied to each layer. If `None`, will
+      use `tf.nn.relu`.
+    dropout: When not `None`, the probability we will drop out a given
+      coordinate.
+    input_layer_partitioner: Optional. Partitioner for input layer. Defaults to
+      `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
+    config: `RunConfig` object to configure the runtime settings.
+    warm_start_from: A string filepath to a checkpoint to warm-start from, or a
+      `WarmStartSettings` object to fully configure warm-starting.  If the
+      string filepath is provided instead of a `WarmStartSettings`, then all
+      weights are warm-started, and it is assumed that vocabularies and Tensor
+      names are unchanged.
+    loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how to
+      reduce training loss over batch. Defaults to `SUM`.
+
+  Returns:
+    DNNRegressor with layer annotations.
+  """
+
+  original = dnn.DNNRegressor(
+      hidden_units=hidden_units,
+      feature_columns=feature_columns,
+      model_dir=model_dir,
+      label_dimension=label_dimension,
+      weight_column=weight_column,
+      optimizer=optimizer,
+      activation_fn=activation_fn,
+      dropout=dropout,
+      input_layer_partitioner=input_layer_partitioner,
+      config=config,
+      warm_start_from=warm_start_from,
+      loss_reduction=loss_reduction,
+  )
+
+  def _model_fn(features, labels, mode, config):
+    with _monkey_patch(
+        feature_column_lib, 'input_layer',
+        make_input_layer_with_layer_annotations(feature_column_lib.input_layer,
+                                                mode)):
+      return original.model_fn(features, labels, mode, config)
+
+  return estimator.Estimator(
+      model_fn=_model_fn,
+      model_dir=model_dir,
+      config=config,
+      warm_start_from=warm_start_from)
diff --git a/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations_test.py b/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations_test.py
new file mode 100644
index 0000000000..2fe3d4c72e
--- /dev/null
+++ b/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations_test.py
@@ -0,0 +1,611 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for dnn_with_layer_annotations.py."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import shutil
+import tempfile
+
+import numpy as np
+import six
+
+from tensorflow.contrib.estimator.python.estimator import dnn_with_layer_annotations
+from tensorflow.core.example import example_pb2
+from tensorflow.core.example import feature_pb2
+from tensorflow.python.estimator import model_fn as model_fn_lib
+from tensorflow.python.estimator.canned import dnn
+from tensorflow.python.estimator.canned import dnn_testing_utils
+from tensorflow.python.estimator.canned import prediction_keys
+from tensorflow.python.estimator.export import export
+from tensorflow.python.estimator.inputs import numpy_io
+from tensorflow.python.estimator.inputs import pandas_io
+from tensorflow.python.feature_column import feature_column
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import data_flow_ops
+from tensorflow.python.ops import parsing_ops
+from tensorflow.python.platform import gfile
+from tensorflow.python.platform import test
+from tensorflow.python.summary.writer import writer_cache
+from tensorflow.python.training import input as input_lib
+from tensorflow.python.training import queue_runner
+
+try:
+  # pylint: disable=g-import-not-at-top
+  import pandas as pd
+  HAS_PANDAS = True
+except IOError:
+  # Pandas writes a temporary file during import. If it fails, don't use pandas.
+  HAS_PANDAS = False
+except ImportError:
+  HAS_PANDAS = False
+
+
+def _dnn_classifier_fn(*args, **kwargs):
+  return dnn_with_layer_annotations.DNNClassifierWithLayerAnnotations(
+      *args, **kwargs)
+
+
+class DNNWarmStartingTest(dnn_testing_utils.BaseDNNWarmStartingTest,
+                          test.TestCase):
+
+  def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
+    test.TestCase.__init__(self, methodName)
+    dnn_testing_utils.BaseDNNWarmStartingTest.__init__(self, _dnn_classifier_fn,
+                                                       _dnn_regressor_fn)
+
+
+class DNNWithLayerAnnotationsClassifierEvaluateTest(
+    dnn_testing_utils.BaseDNNClassifierEvaluateTest, test.TestCase):
+
+  def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
+    test.TestCase.__init__(self, methodName)
+    dnn_testing_utils.BaseDNNClassifierEvaluateTest.__init__(
+        self, _dnn_classifier_fn)
+
+
+class DNNClassifierWithLayerAnnotationsPredictTest(
+    dnn_testing_utils.BaseDNNClassifierPredictTest, test.TestCase):
+
+  def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
+    test.TestCase.__init__(self, methodName)
+    dnn_testing_utils.BaseDNNClassifierPredictTest.__init__(
+        self, _dnn_classifier_fn)
+
+
+class DNNClassifierWithLayerAnnotationsTrainTest(
+    dnn_testing_utils.BaseDNNClassifierTrainTest, test.TestCase):
+
+  def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
+    test.TestCase.__init__(self, methodName)
+    dnn_testing_utils.BaseDNNClassifierTrainTest.__init__(
+        self, _dnn_classifier_fn)
+
+
+def _dnn_regressor_fn(*args, **kwargs):
+  return dnn_with_layer_annotations.DNNRegressorWithLayerAnnotations(
+      *args, **kwargs)
+
+
+class DNNWithLayerAnnotationsTest(test.TestCase):
+
+  def setUp(self):
+    self._model_dir = tempfile.mkdtemp()
+
+  def _getLayerAnnotationCollection(self, graph, collection_name):
+    keys = graph.get_collection(
+        dnn_with_layer_annotations.LayerAnnotationsCollectionNames.keys(
+            collection_name))
+    values = graph.get_collection(
+        dnn_with_layer_annotations.LayerAnnotationsCollectionNames.values(
+            collection_name))
+    if len(keys) != len(values):
+      raise ValueError('keys and values should have same length. lengths were: '
+                       '%d and %d, and elements were %s and %s' %
+                       (len(keys), len(values), keys, values))
+    return dict(zip(keys, values))
+
+  def _testAnnotationsPresentForEstimator(self, estimator_class):
+    feature_columns = [
+        feature_column.numeric_column('x', shape=(1,)),
+        feature_column.embedding_column(
+            feature_column.categorical_column_with_vocabulary_list(
+                'y', vocabulary_list=['a', 'b', 'c']),
+            dimension=3)
+    ]
+    estimator = estimator_class(
+        hidden_units=(2, 2),
+        feature_columns=feature_columns,
+        model_dir=self._model_dir)
+    model_fn = estimator.model_fn
+
+    graph = ops.Graph()
+    with graph.as_default():
+      model_fn({
+          'x': array_ops.constant([1.0]),
+          'y': array_ops.constant(['a'])
+      }, {},
+               model_fn_lib.ModeKeys.PREDICT,
+               config=None)
+
+      unprocessed_features = self._getLayerAnnotationCollection(
+          graph, dnn_with_layer_annotations.LayerAnnotationsCollectionNames
+          .UNPROCESSED_FEATURES)
+      processed_features = self._getLayerAnnotationCollection(
+          graph, dnn_with_layer_annotations.LayerAnnotationsCollectionNames
+          .PROCESSED_FEATURES)
+      feature_columns = graph.get_collection(
+          dnn_with_layer_annotations.LayerAnnotationsCollectionNames
+          .FEATURE_COLUMNS)
+
+      self.assertItemsEqual(unprocessed_features.keys(), ['x', 'y'])
+      self.assertEqual(2, len(processed_features.keys()))
+      self.assertEqual(2, len(feature_columns))
+
+  def testAnnotationsPresentForClassifier(self):
+    self._testAnnotationsPresentForEstimator(
+        dnn_with_layer_annotations.DNNClassifierWithLayerAnnotations)
+
+  def testAnnotationsPresentForRegressor(self):
+    self._testAnnotationsPresentForEstimator(
+        dnn_with_layer_annotations.DNNRegressorWithLayerAnnotations)
+
+  def _testCheckpointCompatibleWithNonAnnotatedEstimator(
+      self, train_input_fn, predict_input_fn, non_annotated_class,
+      annotated_class, prediction_key, estimator_args):
+    input_dimension = 2
+    feature_columns = [
+        feature_column.numeric_column('x', shape=(input_dimension,))
+    ]
+    estimator = non_annotated_class(
+        model_dir=self._model_dir,
+        hidden_units=(2, 2),
+        feature_columns=feature_columns,
+        **estimator_args)
+
+    estimator.train(train_input_fn, steps=10)
+
+    predictions = np.array(
+        [x[prediction_key] for x in estimator.predict(predict_input_fn)])
+
+    annotated_estimator = annotated_class(
+        model_dir=self._model_dir,
+        hidden_units=(2, 2),
+        feature_columns=feature_columns,
+        warm_start_from=self._model_dir,
+        **estimator_args)
+
+    annotated_predictions = np.array([
+        x[prediction_key] for x in annotated_estimator.predict(predict_input_fn)
+    ])
+
+    self.assertAllEqual(predictions.shape, annotated_predictions.shape)
+    for i, (a, b) in enumerate(
+        zip(predictions.flatten(), annotated_predictions.flatten())):
+      self.assertAlmostEqual(a, b, msg='index=%d' % i)
+
+  def testCheckpointCompatibleForClassifier(self):
+    n_classes = 2
+    input_dimension = 2
+    batch_size = 10
+    data = np.linspace(
+        0., n_classes - 1., batch_size * input_dimension, dtype=np.float32)
+    x_data = data.reshape(batch_size, input_dimension)
+    y_data = np.reshape(
+        np.rint(data[:batch_size]).astype(np.int64), (batch_size, 1))
+    # learn y = x
+    train_input_fn = numpy_io.numpy_input_fn(
+        x={'x': x_data},
+        y=y_data,
+        batch_size=batch_size,
+        num_epochs=None,
+        shuffle=True)
+    predict_input_fn = numpy_io.numpy_input_fn(
+        x={'x': x_data}, batch_size=batch_size, shuffle=False)
+
+    self._testCheckpointCompatibleWithNonAnnotatedEstimator(
+        train_input_fn,
+        predict_input_fn,
+        dnn.DNNClassifier,
+        dnn_with_layer_annotations.DNNClassifierWithLayerAnnotations,
+        prediction_key=prediction_keys.PredictionKeys.PROBABILITIES,
+        estimator_args={'n_classes': n_classes})
+
+  def testCheckpointCompatibleForRegressor(self):
+    label_dimension = 2
+    batch_size = 10
+    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
+    data = data.reshape(batch_size, label_dimension)
+    # learn y = x
+    train_input_fn = numpy_io.numpy_input_fn(
+        x={'x': data},
+        y=data,
+        batch_size=batch_size,
+        num_epochs=None,
+        shuffle=True)
+    predict_input_fn = numpy_io.numpy_input_fn(
+        x={'x': data}, batch_size=batch_size, shuffle=False)
+
+    self._testCheckpointCompatibleWithNonAnnotatedEstimator(
+        train_input_fn,
+        predict_input_fn,
+        dnn.DNNRegressor,
+        dnn_with_layer_annotations.DNNRegressorWithLayerAnnotations,
+        prediction_key=prediction_keys.PredictionKeys.PREDICTIONS,
+        estimator_args={'label_dimension': label_dimension})
+
+
+class DNNRegressorWithLayerAnnotationsEvaluateTest(
+    dnn_testing_utils.BaseDNNRegressorEvaluateTest, test.TestCase):
+
+  def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
+    test.TestCase.__init__(self, methodName)
+    dnn_testing_utils.BaseDNNRegressorEvaluateTest.__init__(
+        self, _dnn_regressor_fn)
+
+
+class DNNRegressorWithLayerAnnotationsPredictTest(
+    dnn_testing_utils.BaseDNNRegressorPredictTest, test.TestCase):
+
+  def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
+    test.TestCase.__init__(self, methodName)
+    dnn_testing_utils.BaseDNNRegressorPredictTest.__init__(
+        self, _dnn_regressor_fn)
+
+
+class DNNRegressorWithLayerAnnotationsTrainTest(
+    dnn_testing_utils.BaseDNNRegressorTrainTest, test.TestCase):
+
+  def __init__(self, methodName='runTest'):  # pylint: disable=invalid-name
+    test.TestCase.__init__(self, methodName)
+    dnn_testing_utils.BaseDNNRegressorTrainTest.__init__(
+        self, _dnn_regressor_fn)
+
+
+def _queue_parsed_features(feature_map):
+  tensors_to_enqueue = []
+  keys = []
+  for key, tensor in six.iteritems(feature_map):
+    keys.append(key)
+    tensors_to_enqueue.append(tensor)
+  queue_dtypes = [x.dtype for x in tensors_to_enqueue]
+  input_queue = data_flow_ops.FIFOQueue(capacity=100, dtypes=queue_dtypes)
+  queue_runner.add_queue_runner(
+      queue_runner.QueueRunner(input_queue,
+                               [input_queue.enqueue(tensors_to_enqueue)]))
+  dequeued_tensors = input_queue.dequeue()
+  return {keys[i]: dequeued_tensors[i] for i in range(len(dequeued_tensors))}
+
+
+class DNNRegressorWithLayerAnnotationsIntegrationTest(test.TestCase):
+
+  def setUp(self):
+    self._model_dir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    if self._model_dir:
+      writer_cache.FileWriterCache.clear()
+      shutil.rmtree(self._model_dir)
+
+  def _test_complete_flow(self, train_input_fn, eval_input_fn, predict_input_fn,
+                          input_dimension, label_dimension, batch_size):
+    feature_columns = [
+        feature_column.numeric_column('x', shape=(input_dimension,))
+    ]
+    est = dnn_with_layer_annotations.DNNRegressorWithLayerAnnotations(
+        hidden_units=(2, 2),
+        feature_columns=feature_columns,
+        label_dimension=label_dimension,
+        model_dir=self._model_dir)
+
+    # TRAIN
+    num_steps = 10
+    est.train(train_input_fn, steps=num_steps)
+
+    # EVALUTE
+    scores = est.evaluate(eval_input_fn)
+    self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
+    self.assertIn('loss', six.iterkeys(scores))
+
+    # PREDICT
+    predictions = np.array([
+        x[prediction_keys.PredictionKeys.PREDICTIONS]
+        for x in est.predict(predict_input_fn)
+    ])
+    self.assertAllEqual((batch_size, label_dimension), predictions.shape)
+
+    # EXPORT
+    feature_spec = feature_column.make_parse_example_spec(feature_columns)
+    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
+        feature_spec)
+    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
+                                       serving_input_receiver_fn)
+    self.assertTrue(gfile.Exists(export_dir))
+
+  def test_numpy_input_fn(self):
+    """Tests complete flow with numpy_input_fn."""
+    label_dimension = 2
+    batch_size = 10
+    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
+    data = data.reshape(batch_size, label_dimension)
+    # learn y = x
+    train_input_fn = numpy_io.numpy_input_fn(
+        x={'x': data},
+        y=data,
+        batch_size=batch_size,
+        num_epochs=None,
+        shuffle=True)
+    eval_input_fn = numpy_io.numpy_input_fn(
+        x={'x': data}, y=data, batch_size=batch_size, shuffle=False)
+    predict_input_fn = numpy_io.numpy_input_fn(
+        x={'x': data}, batch_size=batch_size, shuffle=False)
+
+    self._test_complete_flow(
+        train_input_fn=train_input_fn,
+        eval_input_fn=eval_input_fn,
+        predict_input_fn=predict_input_fn,
+        input_dimension=label_dimension,
+        label_dimension=label_dimension,
+        batch_size=batch_size)
+
+  def test_pandas_input_fn(self):
+    """Tests complete flow with pandas_input_fn."""
+    if not HAS_PANDAS:
+      return
+    label_dimension = 1
+    batch_size = 10
+    data = np.linspace(0., 2., batch_size, dtype=np.float32)
+    x = pd.DataFrame({'x': data})
+    y = pd.Series(data)
+    train_input_fn = pandas_io.pandas_input_fn(
+        x=x, y=y, batch_size=batch_size, num_epochs=None, shuffle=True)
+    eval_input_fn = pandas_io.pandas_input_fn(
+        x=x, y=y, batch_size=batch_size, shuffle=False)
+    predict_input_fn = pandas_io.pandas_input_fn(
+        x=x, batch_size=batch_size, shuffle=False)
+
+    self._test_complete_flow(
+        train_input_fn=train_input_fn,
+        eval_input_fn=eval_input_fn,
+        predict_input_fn=predict_input_fn,
+        input_dimension=label_dimension,
+        label_dimension=label_dimension,
+        batch_size=batch_size)
+
+  def test_input_fn_from_parse_example(self):
+    """Tests complete flow with input_fn constructed from parse_example."""
+    label_dimension = 2
+    batch_size = 10
+    data = np.linspace(0., 2., batch_size * label_dimension, dtype=np.float32)
+    data = data.reshape(batch_size, label_dimension)
+
+    serialized_examples = []
+    for datum in data:
+      example = example_pb2.Example(
+          features=feature_pb2.Features(
+              feature={
+                  'x':
+                      feature_pb2.Feature(
+                          float_list=feature_pb2.FloatList(value=datum)),
+                  'y':
+                      feature_pb2.Feature(
+                          float_list=feature_pb2.FloatList(value=datum)),
+              }))
+      serialized_examples.append(example.SerializeToString())
+
+    feature_spec = {
+        'x': parsing_ops.FixedLenFeature([label_dimension], dtypes.float32),
+        'y': parsing_ops.FixedLenFeature([label_dimension], dtypes.float32),
+    }
+
+    def _train_input_fn():
+      feature_map = parsing_ops.parse_example(serialized_examples, feature_spec)
+      features = _queue_parsed_features(feature_map)
+      labels = features.pop('y')
+      return features, labels
+
+    def _eval_input_fn():
+      feature_map = parsing_ops.parse_example(
+          input_lib.limit_epochs(serialized_examples, num_epochs=1),
+          feature_spec)
+      features = _queue_parsed_features(feature_map)
+      labels = features.pop('y')
+      return features, labels
+
+    def _predict_input_fn():
+      feature_map = parsing_ops.parse_example(
+          input_lib.limit_epochs(serialized_examples, num_epochs=1),
+          feature_spec)
+      features = _queue_parsed_features(feature_map)
+      features.pop('y')
+      return features, None
+
+    self._test_complete_flow(
+        train_input_fn=_train_input_fn,
+        eval_input_fn=_eval_input_fn,
+        predict_input_fn=_predict_input_fn,
+        input_dimension=label_dimension,
+        label_dimension=label_dimension,
+        batch_size=batch_size)
+
+
+class DNNClassifierWithLayerAnnotationsIntegrationTest(test.TestCase):
+
+  def setUp(self):
+    self._model_dir = tempfile.mkdtemp()
+
+  def tearDown(self):
+    if self._model_dir:
+      writer_cache.FileWriterCache.clear()
+      shutil.rmtree(self._model_dir)
+
+  def _as_label(self, data_in_float):
+    return np.rint(data_in_float).astype(np.int64)
+
+  def _test_complete_flow(self, train_input_fn, eval_input_fn, predict_input_fn,
+                          input_dimension, n_classes, batch_size):
+    feature_columns = [
+        feature_column.numeric_column('x', shape=(input_dimension,))
+    ]
+    est = dnn_with_layer_annotations.DNNClassifierWithLayerAnnotations(
+        hidden_units=(2, 2),
+        feature_columns=feature_columns,
+        n_classes=n_classes,
+        model_dir=self._model_dir)
+
+    # TRAIN
+    num_steps = 10
+    est.train(train_input_fn, steps=num_steps)
+
+    # EVALUTE
+    scores = est.evaluate(eval_input_fn)
+    self.assertEqual(num_steps, scores[ops.GraphKeys.GLOBAL_STEP])
+    self.assertIn('loss', six.iterkeys(scores))
+
+    # PREDICT
+    predicted_proba = np.array([
+        x[prediction_keys.PredictionKeys.PROBABILITIES]
+        for x in est.predict(predict_input_fn)
+    ])
+    self.assertAllEqual((batch_size, n_classes), predicted_proba.shape)
+
+    # EXPORT
+    feature_spec = feature_column.make_parse_example_spec(feature_columns)
+    serving_input_receiver_fn = export.build_parsing_serving_input_receiver_fn(
+        feature_spec)
+    export_dir = est.export_savedmodel(tempfile.mkdtemp(),
+                                       serving_input_receiver_fn)
+    self.assertTrue(gfile.Exists(export_dir))
+
+  def test_numpy_input_fn(self):
+    """Tests complete flow with numpy_input_fn."""
+    n_classes = 3
+    input_dimension = 2
+    batch_size = 10
+    data = np.linspace(
+        0., n_classes - 1., batch_size * input_dimension, dtype=np.float32)
+    x_data = data.reshape(batch_size, input_dimension)
+    y_data = np.reshape(self._as_label(data[:batch_size]), (batch_size, 1))
+    # learn y = x
+    train_input_fn = numpy_io.numpy_input_fn(
+        x={'x': x_data},
+        y=y_data,
+        batch_size=batch_size,
+        num_epochs=None,
+        shuffle=True)
+    eval_input_fn = numpy_io.numpy_input_fn(
+        x={'x': x_data}, y=y_data, batch_size=batch_size, shuffle=False)
+    predict_input_fn = numpy_io.numpy_input_fn(
+        x={'x': x_data}, batch_size=batch_size, shuffle=False)
+
+    self._test_complete_flow(
+        train_input_fn=train_input_fn,
+        eval_input_fn=eval_input_fn,
+        predict_input_fn=predict_input_fn,
+        input_dimension=input_dimension,
+        n_classes=n_classes,
+        batch_size=batch_size)
+
+  def test_pandas_input_fn(self):
+    """Tests complete flow with pandas_input_fn."""
+    if not HAS_PANDAS:
+      return
+    input_dimension = 1
+    n_classes = 3
+    batch_size = 10
+    data = np.linspace(0., n_classes - 1., batch_size, dtype=np.float32)
+    x = pd.DataFrame({'x': data})
+    y = pd.Series(self._as_label(data))
+    train_input_fn = pandas_io.pandas_input_fn(
+        x=x, y=y, batch_size=batch_size, num_epochs=None, shuffle=True)
+    eval_input_fn = pandas_io.pandas_input_fn(
+        x=x, y=y, batch_size=batch_size, shuffle=False)
+    predict_input_fn = pandas_io.pandas_input_fn(
+        x=x, batch_size=batch_size, shuffle=False)
+
+    self._test_complete_flow(
+        train_input_fn=train_input_fn,
+        eval_input_fn=eval_input_fn,
+        predict_input_fn=predict_input_fn,
+        input_dimension=input_dimension,
+        n_classes=n_classes,
+        batch_size=batch_size)
+
+  def test_input_fn_from_parse_example(self):
+    """Tests complete flow with input_fn constructed from parse_example."""
+    input_dimension = 2
+    n_classes = 3
+    batch_size = 10
+    data = np.linspace(
+        0., n_classes - 1., batch_size * input_dimension, dtype=np.float32)
+    data = data.reshape(batch_size, input_dimension)
+
+    serialized_examples = []
+    for datum in data:
+      example = example_pb2.Example(
+          features=feature_pb2.Features(
+              feature={
+                  'x':
+                      feature_pb2.Feature(
+                          float_list=feature_pb2.FloatList(value=datum)),
+                  'y':
+                      feature_pb2.Feature(
+                          int64_list=feature_pb2.Int64List(
+                              value=self._as_label(datum[:1]))),
+              }))
+      serialized_examples.append(example.SerializeToString())
+
+    feature_spec = {
+        'x': parsing_ops.FixedLenFeature([input_dimension], dtypes.float32),
+        'y': parsing_ops.FixedLenFeature([1], dtypes.int64),
+    }
+
+    def _train_input_fn():
+      feature_map = parsing_ops.parse_example(serialized_examples, feature_spec)
+      features = _queue_parsed_features(feature_map)
+      labels = features.pop('y')
+      return features, labels
+
+    def _eval_input_fn():
+      feature_map = parsing_ops.parse_example(
+          input_lib.limit_epochs(serialized_examples, num_epochs=1),
+          feature_spec)
+      features = _queue_parsed_features(feature_map)
+      labels = features.pop('y')
+      return features, labels
+
+    def _predict_input_fn():
+      feature_map = parsing_ops.parse_example(
+          input_lib.limit_epochs(serialized_examples, num_epochs=1),
+          feature_spec)
+      features = _queue_parsed_features(feature_map)
+      features.pop('y')
+      return features, None
+
+    self._test_complete_flow(
+        train_input_fn=_train_input_fn,
+        eval_input_fn=_eval_input_fn,
+        predict_input_fn=_predict_input_fn,
+        input_dimension=input_dimension,
+        n_classes=n_classes,
+        batch_size=batch_size)
+
+
+if __name__ == '__main__':
+  test.main()
diff --git a/tensorflow/contrib/estimator/python/estimator/rnn.py b/tensorflow/contrib/estimator/python/estimator/rnn.py
index 7c49cd00d1..98660bb731 100644
--- a/tensorflow/contrib/estimator/python/estimator/rnn.py
+++ b/tensorflow/contrib/estimator/python/estimator/rnn.py
@@ -37,6 +37,7 @@ from tensorflow.python.ops import partitioned_variables
 from tensorflow.python.ops import rnn
 from tensorflow.python.ops import rnn_cell
 from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops.losses import losses
 from tensorflow.python.summary import summary
 from tensorflow.python.training import optimizer as optimizer_lib
 from tensorflow.python.training import training_util
@@ -405,6 +406,7 @@ class RNNClassifier(estimator.Estimator):
                weight_column=None,
                label_vocabulary=None,
                optimizer='Adagrad',
+               loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE,
                input_layer_partitioner=None,
                config=None):
     """Initializes a `RNNClassifier` instance.
@@ -454,6 +456,8 @@ class RNNClassifier(estimator.Estimator):
         string.
       optimizer: An instance of `tf.Optimizer` or string specifying optimizer
         type. Defaults to Adagrad optimizer.
+      loss_reduction: One of `tf.losses.Reduction` except `NONE`. Describes how
+        to reduce training loss over batch. Defaults to `SUM_OVER_BATCH_SIZE`.
       input_layer_partitioner: Optional. Partitioner for input layer. Defaults
         to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
       config: `RunConfig` object to configure the runtime settings.
@@ -467,11 +471,15 @@ class RNNClassifier(estimator.Estimator):
     if n_classes == 2:
       head = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss(  # pylint: disable=protected-access
           weight_column=weight_column,
-          label_vocabulary=label_vocabulary)
+          label_vocabulary=label_vocabulary,
+          loss_reduction=loss_reduction)
     else:
       head = head_lib._multi_class_head_with_softmax_cross_entropy_loss(  # pylint: disable=protected-access
-          n_classes, weight_column=weight_column,
-          label_vocabulary=label_vocabulary)
+          n_classes,
+          weight_column=weight_column,
+          label_vocabulary=label_vocabulary,
+          loss_reduction=loss_reduction)
+
     def _model_fn(features, labels, mode, config):
       return _rnn_model_fn(
           features=features,
diff --git a/tensorflow/contrib/estimator/python/estimator/rnn_test.py b/tensorflow/contrib/estimator/python/estimator/rnn_test.py
index 959b40371a..1aebed348d 100644
--- a/tensorflow/contrib/estimator/python/estimator/rnn_test.py
+++ b/tensorflow/contrib/estimator/python/estimator/rnn_test.py
@@ -713,7 +713,7 @@ class RNNClassifierTrainingTest(test.TestCase):
 
     # Uses same checkpoint and examples as testBinaryClassEvaluationMetrics.
     # See that test for loss calculation.
-    mock_optimizer = self._mock_optimizer(expected_loss=1.119661)
+    mock_optimizer = self._mock_optimizer(expected_loss=0.559831)
 
     sequence_feature_columns = [
         seq_fc.sequence_numeric_column('price', shape=(1,))]
@@ -748,7 +748,7 @@ class RNNClassifierTrainingTest(test.TestCase):
 
     # Uses same checkpoint and examples as testMultiClassEvaluationMetrics.
     # See that test for loss calculation.
-    mock_optimizer = self._mock_optimizer(expected_loss=2.662932)
+    mock_optimizer = self._mock_optimizer(expected_loss=1.331465)
 
     sequence_feature_columns = [
         seq_fc.sequence_numeric_column('price', shape=(1,))]
@@ -812,20 +812,32 @@ class RNNClassifierEvaluationTest(test.TestCase):
     # probability = exp(logits) / (1 + exp(logits)) = [[0.353593], [0.504930]]
     # loss = -label * ln(p) - (1 - label) * ln(1 - p)
     #      = [[0.436326], [0.683335]]
+    # sum_over_batch_size = (0.436326 + 0.683335)/2
     expected_metrics = {
-        ops.GraphKeys.GLOBAL_STEP: global_step,
-        metric_keys.MetricKeys.LOSS: 1.119661,
-        metric_keys.MetricKeys.LOSS_MEAN: 0.559831,
-        metric_keys.MetricKeys.ACCURACY: 1.0,
-        metric_keys.MetricKeys.PREDICTION_MEAN: 0.429262,
-        metric_keys.MetricKeys.LABEL_MEAN: 0.5,
-        metric_keys.MetricKeys.ACCURACY_BASELINE: 0.5,
+        ops.GraphKeys.GLOBAL_STEP:
+            global_step,
+        metric_keys.MetricKeys.LOSS:
+            0.559831,
+        metric_keys.MetricKeys.LOSS_MEAN:
+            0.559831,
+        metric_keys.MetricKeys.ACCURACY:
+            1.0,
+        metric_keys.MetricKeys.PREDICTION_MEAN:
+            0.429262,
+        metric_keys.MetricKeys.LABEL_MEAN:
+            0.5,
+        metric_keys.MetricKeys.ACCURACY_BASELINE:
+            0.5,
         # With default threshold of 0.5, the model is a perfect classifier.
-        metric_keys.MetricKeys.RECALL: 1.0,
-        metric_keys.MetricKeys.PRECISION: 1.0,
+        metric_keys.MetricKeys.RECALL:
+            1.0,
+        metric_keys.MetricKeys.PRECISION:
+            1.0,
         # Positive example is scored above negative, so AUC = 1.0.
-        metric_keys.MetricKeys.AUC: 1.0,
-        metric_keys.MetricKeys.AUC_PR: 1.0,
+        metric_keys.MetricKeys.AUC:
+            1.0,
+        metric_keys.MetricKeys.AUC_PR:
+            1.0,
     }
     self.assertAllClose(
         sorted_key_dict(expected_metrics), sorted_key_dict(eval_metrics))
@@ -871,9 +883,10 @@ class RNNClassifierEvaluationTest(test.TestCase):
     #                          [0.059494, 0.572639, 0.367866]]
     # loss = -1. * log(softmax[label])
     #      = [[2.105432], [0.557500]]
+    # sum_over_batch_size = (2.105432 + 0.557500)/2
     expected_metrics = {
         ops.GraphKeys.GLOBAL_STEP: global_step,
-        metric_keys.MetricKeys.LOSS: 2.662932,
+        metric_keys.MetricKeys.LOSS: 1.331465,
         metric_keys.MetricKeys.LOSS_MEAN: 1.331466,
         metric_keys.MetricKeys.ACCURACY: 0.5,
     }