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diff --git a/tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py b/tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py
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+# Copyright 2015 Google Inc. 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.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import sparse_ops
+
+
+"""Confusion matrix related metrics."""
+
+
+def confusion_matrix(predictions, targets, num_classes=None, name=None):
+  """Computes the confusion matrix from predictions and targets
+
+  Calculate the Confusion Matrix for a pair of prediction and
+  target 1-D int arrays.
+
+  Considering a prediction array such as: `[1, 2, 3]`
+  And a target array such as: `[2, 2, 3]`
+
+  The confusion matrix returned would be the following one:
+      [[0, 0, 0]
+       [0, 1, 0]
+       [0, 1, 0]
+       [0, 0, 1]]
+
+  Where the matrix rows represent the prediction labels and the columns
+  represents the target labels. The confusion matrix is always a 2-D array
+  of shape [n, n], where n is the number of valid labels for a given
+  classification task. Both prediction and target must be 1-D arrays of
+  the same shape in order for this function to work.
+
+  Args:
+    predictions: A 1-D array represeting the predictions for a given
+                 classification.
+    targets: A 1-D represeting the real labels for the classification task.
+    num_classes: The possible number of labels the classification task can
+                 have. If this value is not provided, it will be calculated
+                 using both predictions and targets array.
+
+  Returns:
+    A l X l matrix represeting the confusion matrix, where l in the number of
+    possible labels in the classification task.
+
+  Raises:
+    ValueError: If both predictions and targets are not 1-D vectors and do not
+                have the same size.
+  """
+  with ops.op_scope([predictions, targets, num_classes], name,
+                    'confusion_matrix') as name:
+    predictions = ops.convert_to_tensor(
+      predictions, name="predictions", dtype=dtypes.int64)
+    targets = ops.convert_to_tensor(
+      targets, name="targets", dtype=dtypes.int64)
+
+    if num_classes is None:
+      num_classes = math_ops.maximum(math_ops.reduce_max(predictions),
+                                     math_ops.reduce_max(targets)) + 1
+
+    shape = array_ops.pack([num_classes, num_classes])
+    indices = array_ops.transpose(
+      array_ops.pack([predictions, targets]))
+    values = array_ops.ones_like(predictions, dtype=dtypes.int32)
+    cm_sparse = ops.SparseTensor(
+      indices=indices, values=values, shape=shape)
+    zero_matrix = array_ops.zeros(math_ops.to_int32(shape), dtypes.int32)
+
+    return sparse_ops.sparse_add(zero_matrix, cm_sparse)