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diff --git a/tensorflow/contrib/sparsemax/python/ops/sparsemax.py b/tensorflow/contrib/sparsemax/python/ops/sparsemax.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 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, dtypes
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import nn
+
+
+def sparsemax(logits, name=None):
+  """Computes sparsemax activations [1].
+
+  For each batch `i` and class `j` we have
+    sparsemax[i, j] = max(logits[i, j] - tau(logits[i, :]), 0)
+
+  [1]: https://arxiv.org/abs/1602.02068
+
+  Args:
+    logits: A `Tensor`. Must be one of the following types: `half`, `float32`,
+      `float64`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `logits`.
+  """
+
+  with ops.name_scope(name, "sparsemax", [logits]) as name:
+    logits = ops.convert_to_tensor(logits, name="logits")
+    obs = array_ops.shape(logits)[0]
+    dims = array_ops.shape(logits)[1]
+
+    z = logits - math_ops.reduce_mean(logits, axis=1)[:, array_ops.newaxis]
+
+    # sort z
+    z_sorted, _ = nn.top_k(z, k=dims)
+
+    # calculate k(z)
+    z_cumsum = math_ops.cumsum(z_sorted, axis=1)
+    k = math_ops.range(
+        1, math_ops.cast(dims, logits.dtype) + 1, dtype=logits.dtype
+    )
+    z_check = 1 + k * z_sorted > z_cumsum
+    # because the z_check vector is always [1,1,...1,0,0,...0] finding the
+    # (index + 1) of the last `1` is the same as just summing the number of 1.
+    k_z = math_ops.reduce_sum(math_ops.cast(z_check, dtypes.int32), axis=1)
+
+    # calculate tau(z)
+    indices = array_ops.stack([math_ops.range(0, obs), k_z - 1], axis=1)
+    tau_sum = array_ops.gather_nd(z_cumsum, indices)
+    tau_z = (tau_sum - 1) / math_ops.cast(k_z, logits.dtype)
+
+    # calculate p
+    return math_ops.maximum(
+        math_ops.cast(0, logits.dtype),
+        z - tau_z[:, array_ops.newaxis]
+    )