1 files changed, 11 insertions, 3 deletions
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,