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diff --git a/tensorflow/contrib/sparsemax/python/ops/sparsemax_loss.py b/tensorflow/contrib/sparsemax/python/ops/sparsemax_loss.py
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+++ b/tensorflow/contrib/sparsemax/python/ops/sparsemax_loss.py
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+# Copyright 2016 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.
+# ==============================================================================
+"""Sparsemax Loss op."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.util import loader
+from tensorflow.python.platform import resource_loader
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+
+
+def sparsemax_loss(logits, sparsemax, labels, name=None):
+  """Computes sparsemax loss function [1].
+
+  [1]: https://arxiv.org/abs/1602.02068
+
+  Args:
+    logits: A `Tensor`. Must be one of the following types: `half`, `float32`,
+      `float64`.
+    sparsemax: A `Tensor`. Must have the same type as `logits`.
+    labels: A `Tensor`. Must have the same type as `logits`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `logits`.
+  """
+
+  with ops.name_scope(name, "sparsemax_loss",
+                      [logits, sparsemax, labels]) as name:
+    logits = ops.convert_to_tensor(logits, name="logits")
+    sparsemax = ops.convert_to_tensor(sparsemax, name="sparsemax")
+    labels = ops.convert_to_tensor(labels, name="labels")
+
+    shifted_logits = logits - \
+        math_ops.reduce_mean(logits, axis=1)[:, array_ops.newaxis]
+
+    # sum over support
+    support = math_ops.cast(sparsemax > 0, sparsemax.dtype)
+    sum_s = support * sparsemax * (shifted_logits - 0.5 * sparsemax)
+
+    # - z_k + ||q||^2
+    q_part = labels * (0.5 * labels - shifted_logits)
+
+    return math_ops.reduce_sum(sum_s + q_part, axis=1)