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diff --git a/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.py b/tensorflow/contrib/learn/python/learn/datasets/synthetic_test.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.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+
+import numpy as np
+from tensorflow.python.platform import test
+from tensorflow.contrib.learn.python.learn import datasets
+from tensorflow.contrib.learn.python.learn.datasets import synthetic
+
+class SyntheticTest(test.TestCase):
+  """Test synthetic dataset generation"""
+
+  def test_make_dataset(self):
+    """Test if the synthetic routine wrapper complains about the name"""
+    self.assertRaises(ValueError, datasets.make_dataset, name='_non_existing_name')
+
+  def test_all_datasets_callable(self):
+    """Test if all methods inside the `SYNTHETIC` are callable"""
+    self.assertIsInstance(datasets.SYNTHETIC, dict)
+    if len(datasets.SYNTHETIC) > 0:
+      for name, method in six.iteritems(datasets.SYNTHETIC):
+        self.assertTrue(callable(method))
+
+  def test_circles(self):
+    """Test if the circles are generated correctly
+
+    Tests:
+      - return type is `Dataset`
+      - returned `data` shape is (n_samples, n_features)
+      - returned `target` shape is (n_samples,)
+      - set of unique classes range is [0, n_classes)
+
+    TODO:
+      - all points have the same radius, if no `noise` specified
+    """
+    n_samples = 100
+    n_classes = 2
+    circ = synthetic.circles(n_samples = n_samples, noise = None, n_classes = n_classes)
+    self.assertIsInstance(circ, datasets.base.Dataset)
+    self.assertTupleEqual(circ.data.shape, (n_samples,2))
+    self.assertTupleEqual(circ.target.shape, (n_samples,))
+    self.assertSetEqual(set(circ.target), set(range(n_classes)))
+
+  def test_circles_replicable(self):
+    """Test if the data generation is replicable with a specified `seed`
+
+    Tests:
+      - return the same value if raised with the same seed
+      - return different values if noise or seed is different
+    """
+    seed = 42
+    noise = 0.1
+    circ0 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed)
+    circ1 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed)
+    np.testing.assert_array_equal(circ0.data, circ1.data)
+    np.testing.assert_array_equal(circ0.target, circ1.target)
+
+    circ1 = synthetic.circles(n_samples = 100, noise = noise, n_classes = 2, seed = seed+1)
+    self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data, circ1.data)
+    self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.target, circ1.target)
+
+    circ1 = synthetic.circles(n_samples = 100, noise = noise/2., n_classes = 2, seed = seed)
+    self.assertRaises(AssertionError, np.testing.assert_array_equal, circ0.data, circ1.data)
+
+  def test_spirals(self):
+    """Test if the circles are generated correctly
+
+    Tests:
+      - if mode is unknown, ValueError is raised
+      - return type is `Dataset`
+      - returned `data` shape is (n_samples, n_features)
+      - returned `target` shape is (n_samples,)
+      - set of unique classes range is [0, n_classes)
+    """
+    self.assertRaises(ValueError, synthetic.spirals, mode='_unknown_mode_spiral_')
+    n_samples = 100
+    modes = ('archimedes', 'bernoulli', 'fermat')
+    for mode in modes:
+      spir = synthetic.spirals(n_samples = n_samples, noise = None, mode = mode)
+      self.assertIsInstance(spir, datasets.base.Dataset)
+      self.assertTupleEqual(spir.data.shape, (n_samples,2))
+      self.assertTupleEqual(spir.target.shape, (n_samples,))
+      self.assertSetEqual(set(spir.target), set(range(2)))
+
+  def test_spirals_replicable(self):
+    """Test if the data generation is replicable with a specified `seed`
+
+    Tests:
+      - return the same value if raised with the same seed
+      - return different values if noise or seed is different
+    """
+    seed = 42
+    noise = 0.1
+    modes = ('archimedes', 'bernoulli', 'fermat')
+    for mode in modes:
+      spir0 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed)
+      spir1 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed)
+      np.testing.assert_array_equal(spir0.data, spir1.data)
+      np.testing.assert_array_equal(spir0.target, spir1.target)
+
+      spir1 = synthetic.spirals(n_samples = 1000, noise = noise, seed = seed+1)
+      self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data)
+      self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.target, spir1.target)
+
+      spir1 = synthetic.spirals(n_samples = 1000, noise = noise/2., seed = seed)
+      self.assertRaises(AssertionError, np.testing.assert_array_equal, spir0.data, spir1.data)
+
+
+if __name__ == "__main__":
+  test.main()