diff --git a/tensorflow/contrib/losses/python/losses/__init__.py b/tensorflow/contrib/losses/python/losses/__init__.py
index 0a549a6bc27..05bad60cabf 100644
--- a/tensorflow/contrib/losses/python/losses/__init__.py
+++ b/tensorflow/contrib/losses/python/losses/__init__.py
@@ -19,7 +19,10 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.losses.python.losses.loss_ops import absolute_difference
+from tensorflow.contrib.losses.python.losses.loss_ops import add_loss
 from tensorflow.contrib.losses.python.losses.loss_ops import cosine_distance
+from tensorflow.contrib.losses.python.losses.loss_ops import get_losses
+from tensorflow.contrib.losses.python.losses.loss_ops import get_total_loss
 from tensorflow.contrib.losses.python.losses.loss_ops import log
 from tensorflow.contrib.losses.python.losses.loss_ops import sigmoid_cross_entropy
 from tensorflow.contrib.losses.python.losses.loss_ops import softmax_cross_entropy
diff --git a/tensorflow/contrib/losses/python/losses/loss_ops.py b/tensorflow/contrib/losses/python/losses/loss_ops.py
index c9947a4ec2b..2c22fb73d0e 100644
--- a/tensorflow/contrib/losses/python/losses/loss_ops.py
+++ b/tensorflow/contrib/losses/python/losses/loss_ops.py
@@ -104,9 +104,11 @@ weighted average over the individual prediction errors:
   weight = tf.div(weight, tf.size(weight))
   loss = tf.contrib.losses.sum_of_squares(predictions, depths, weight)
 
-
 @@absolute_difference
+@@add_loss
 @@cosine_distance
+@@get_losses
+@@get_total_loss
 @@log
 @@sigmoid_cross_entropy
 @@softmax_cross_entropy
@@ -252,6 +254,61 @@ def _num_present(losses, weight, per_batch=False):
   return num_per_batch if per_batch else math_ops.reduce_sum(num_per_batch)
 
 
+def add_loss(loss):
+  """Adds a externally defined loss to collection of losses.
+
+  Args:
+    loss: A loss `Tensor`.
+  """
+  ops.add_to_collection(ops.GraphKeys.LOSSES, loss)
+
+
+def get_losses(scope=None):
+  """Gets the list of loss variables.
+
+  Args:
+    scope: an optional scope for filtering the losses to return.
+
+  Returns:
+    a list of loss variables.
+  """
+  return ops.get_collection(ops.GraphKeys.LOSSES, scope)
+
+
+def get_regularization_losses(scope=None):
+  """Gets the regularization losses.
+
+  Args:
+    scope: an optional scope for filtering the losses to return.
+
+  Returns:
+    A list of loss variables.
+  """
+  return ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope)
+
+
+def get_total_loss(add_regularization_losses=True, name="total_loss"):
+  """Returns a tensor whose value represents the total loss.
+
+  Notice that the function adds the given losses to the regularization losses.
+
+  Args:
+    add_regularization_losses: A boolean indicating whether or not to use the
+      regularization losses in the sum.
+    name: The name of the returned tensor.
+
+  Returns:
+    A `Tensor` whose value represents the total loss.
+
+  Raises:
+    ValueError: if `losses` is not iterable.
+  """
+  losses = get_losses()
+  if add_regularization_losses:
+    losses += get_regularization_losses()
+  return math_ops.add_n(losses, name=name)
+
+
 def absolute_difference(predictions, targets, weight=1.0, scope=None):
   """Adds an Absolute Difference loss to the training procedure.