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diff --git a/tensorflow/python/kernel_tests/distributions/beta_test.py b/tensorflow/python/kernel_tests/distributions/beta_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 importlib
+
+import numpy as np
+
+from tensorflow.python.client import session
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import random_seed
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops.distributions import beta as beta_lib
+from tensorflow.python.ops.distributions import kullback_leibler
+from tensorflow.python.platform import test
+from tensorflow.python.platform import tf_logging
+
+
+def try_import(name):  # pylint: disable=invalid-name
+  module = None
+  try:
+    module = importlib.import_module(name)
+  except ImportError as e:
+    tf_logging.warning("Could not import %s: %s" % (name, str(e)))
+  return module
+
+
+special = try_import("scipy.special")
+stats = try_import("scipy.stats")
+
+
+class BetaTest(test.TestCase):
+
+  def testSimpleShapes(self):
+    with self.test_session():
+      a = np.random.rand(3)
+      b = np.random.rand(3)
+      dist = beta_lib.Beta(a, b)
+      self.assertAllEqual([], dist.event_shape_tensor().eval())
+      self.assertAllEqual([3], dist.batch_shape_tensor().eval())
+      self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape)
+      self.assertEqual(tensor_shape.TensorShape([3]), dist.batch_shape)
+
+  def testComplexShapes(self):
+    with self.test_session():
+      a = np.random.rand(3, 2, 2)
+      b = np.random.rand(3, 2, 2)
+      dist = beta_lib.Beta(a, b)
+      self.assertAllEqual([], dist.event_shape_tensor().eval())
+      self.assertAllEqual([3, 2, 2], dist.batch_shape_tensor().eval())
+      self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape)
+      self.assertEqual(
+          tensor_shape.TensorShape([3, 2, 2]), dist.batch_shape)
+
+  def testComplexShapesBroadcast(self):
+    with self.test_session():
+      a = np.random.rand(3, 2, 2)
+      b = np.random.rand(2, 2)
+      dist = beta_lib.Beta(a, b)
+      self.assertAllEqual([], dist.event_shape_tensor().eval())
+      self.assertAllEqual([3, 2, 2], dist.batch_shape_tensor().eval())
+      self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape)
+      self.assertEqual(
+          tensor_shape.TensorShape([3, 2, 2]), dist.batch_shape)
+
+  def testAlphaProperty(self):
+    a = [[1., 2, 3]]
+    b = [[2., 4, 3]]
+    with self.test_session():
+      dist = beta_lib.Beta(a, b)
+      self.assertEqual([1, 3], dist.concentration1.get_shape())
+      self.assertAllClose(a, dist.concentration1.eval())
+
+  def testBetaProperty(self):
+    a = [[1., 2, 3]]
+    b = [[2., 4, 3]]
+    with self.test_session():
+      dist = beta_lib.Beta(a, b)
+      self.assertEqual([1, 3], dist.concentration0.get_shape())
+      self.assertAllClose(b, dist.concentration0.eval())
+
+  def testPdfXProper(self):
+    a = [[1., 2, 3]]
+    b = [[2., 4, 3]]
+    with self.test_session():
+      dist = beta_lib.Beta(a, b, validate_args=True)
+      dist.prob([.1, .3, .6]).eval()
+      dist.prob([.2, .3, .5]).eval()
+      # Either condition can trigger.
+      with self.assertRaisesOpError("sample must be positive"):
+        dist.prob([-1., 0.1, 0.5]).eval()
+      with self.assertRaisesOpError("sample must be positive"):
+        dist.prob([0., 0.1, 0.5]).eval()
+      with self.assertRaisesOpError("sample must be no larger than `1`"):
+        dist.prob([.1, .2, 1.2]).eval()
+
+  def testPdfTwoBatches(self):
+    with self.test_session():
+      a = [1., 2]
+      b = [1., 2]
+      x = [.5, .5]
+      dist = beta_lib.Beta(a, b)
+      pdf = dist.prob(x)
+      self.assertAllClose([1., 3. / 2], pdf.eval())
+      self.assertEqual((2,), pdf.get_shape())
+
+  def testPdfTwoBatchesNontrivialX(self):
+    with self.test_session():
+      a = [1., 2]
+      b = [1., 2]
+      x = [.3, .7]
+      dist = beta_lib.Beta(a, b)
+      pdf = dist.prob(x)
+      self.assertAllClose([1, 63. / 50], pdf.eval())
+      self.assertEqual((2,), pdf.get_shape())
+
+  def testPdfUniformZeroBatch(self):
+    with self.test_session():
+      # This is equivalent to a uniform distribution
+      a = 1.
+      b = 1.
+      x = np.array([.1, .2, .3, .5, .8], dtype=np.float32)
+      dist = beta_lib.Beta(a, b)
+      pdf = dist.prob(x)
+      self.assertAllClose([1.] * 5, pdf.eval())
+      self.assertEqual((5,), pdf.get_shape())
+
+  def testPdfAlphaStretchedInBroadcastWhenSameRank(self):
+    with self.test_session():
+      a = [[1., 2]]
+      b = [[1., 2]]
+      x = [[.5, .5], [.3, .7]]
+      dist = beta_lib.Beta(a, b)
+      pdf = dist.prob(x)
+      self.assertAllClose([[1., 3. / 2], [1., 63. / 50]], pdf.eval())
+      self.assertEqual((2, 2), pdf.get_shape())
+
+  def testPdfAlphaStretchedInBroadcastWhenLowerRank(self):
+    with self.test_session():
+      a = [1., 2]
+      b = [1., 2]
+      x = [[.5, .5], [.2, .8]]
+      pdf = beta_lib.Beta(a, b).prob(x)
+      self.assertAllClose([[1., 3. / 2], [1., 24. / 25]], pdf.eval())
+      self.assertEqual((2, 2), pdf.get_shape())
+
+  def testPdfXStretchedInBroadcastWhenSameRank(self):
+    with self.test_session():
+      a = [[1., 2], [2., 3]]
+      b = [[1., 2], [2., 3]]
+      x = [[.5, .5]]
+      pdf = beta_lib.Beta(a, b).prob(x)
+      self.assertAllClose([[1., 3. / 2], [3. / 2, 15. / 8]], pdf.eval())
+      self.assertEqual((2, 2), pdf.get_shape())
+
+  def testPdfXStretchedInBroadcastWhenLowerRank(self):
+    with self.test_session():
+      a = [[1., 2], [2., 3]]
+      b = [[1., 2], [2., 3]]
+      x = [.5, .5]
+      pdf = beta_lib.Beta(a, b).prob(x)
+      self.assertAllClose([[1., 3. / 2], [3. / 2, 15. / 8]], pdf.eval())
+      self.assertEqual((2, 2), pdf.get_shape())
+
+  def testBetaMean(self):
+    with session.Session():
+      a = [1., 2, 3]
+      b = [2., 4, 1.2]
+      dist = beta_lib.Beta(a, b)
+      self.assertEqual(dist.mean().get_shape(), (3,))
+      if not stats:
+        return
+      expected_mean = stats.beta.mean(a, b)
+      self.assertAllClose(expected_mean, dist.mean().eval())
+
+  def testBetaVariance(self):
+    with session.Session():
+      a = [1., 2, 3]
+      b = [2., 4, 1.2]
+      dist = beta_lib.Beta(a, b)
+      self.assertEqual(dist.variance().get_shape(), (3,))
+      if not stats:
+        return
+      expected_variance = stats.beta.var(a, b)
+      self.assertAllClose(expected_variance, dist.variance().eval())
+
+  def testBetaMode(self):
+    with session.Session():
+      a = np.array([1.1, 2, 3])
+      b = np.array([2., 4, 1.2])
+      expected_mode = (a - 1) / (a + b - 2)
+      dist = beta_lib.Beta(a, b)
+      self.assertEqual(dist.mode().get_shape(), (3,))
+      self.assertAllClose(expected_mode, dist.mode().eval())
+
+  def testBetaModeInvalid(self):
+    with session.Session():
+      a = np.array([1., 2, 3])
+      b = np.array([2., 4, 1.2])
+      dist = beta_lib.Beta(a, b, allow_nan_stats=False)
+      with self.assertRaisesOpError("Condition x < y.*"):
+        dist.mode().eval()
+
+      a = np.array([2., 2, 3])
+      b = np.array([1., 4, 1.2])
+      dist = beta_lib.Beta(a, b, allow_nan_stats=False)
+      with self.assertRaisesOpError("Condition x < y.*"):
+        dist.mode().eval()
+
+  def testBetaModeEnableAllowNanStats(self):
+    with session.Session():
+      a = np.array([1., 2, 3])
+      b = np.array([2., 4, 1.2])
+      dist = beta_lib.Beta(a, b, allow_nan_stats=True)
+
+      expected_mode = (a - 1) / (a + b - 2)
+      expected_mode[0] = np.nan
+      self.assertEqual((3,), dist.mode().get_shape())
+      self.assertAllClose(expected_mode, dist.mode().eval())
+
+      a = np.array([2., 2, 3])
+      b = np.array([1., 4, 1.2])
+      dist = beta_lib.Beta(a, b, allow_nan_stats=True)
+
+      expected_mode = (a - 1) / (a + b - 2)
+      expected_mode[0] = np.nan
+      self.assertEqual((3,), dist.mode().get_shape())
+      self.assertAllClose(expected_mode, dist.mode().eval())
+
+  def testBetaEntropy(self):
+    with session.Session():
+      a = [1., 2, 3]
+      b = [2., 4, 1.2]
+      dist = beta_lib.Beta(a, b)
+      self.assertEqual(dist.entropy().get_shape(), (3,))
+      if not stats:
+        return
+      expected_entropy = stats.beta.entropy(a, b)
+      self.assertAllClose(expected_entropy, dist.entropy().eval())
+
+  def testBetaSample(self):
+    with self.test_session():
+      a = 1.
+      b = 2.
+      beta = beta_lib.Beta(a, b)
+      n = constant_op.constant(100000)
+      samples = beta.sample(n)
+      sample_values = samples.eval()
+      self.assertEqual(sample_values.shape, (100000,))
+      self.assertFalse(np.any(sample_values < 0.0))
+      if not stats:
+        return
+      self.assertLess(
+          stats.kstest(
+              # Beta is a univariate distribution.
+              sample_values,
+              stats.beta(a=1., b=2.).cdf)[0],
+          0.01)
+      # The standard error of the sample mean is 1 / (sqrt(18 * n))
+      self.assertAllClose(
+          sample_values.mean(axis=0), stats.beta.mean(a, b), atol=1e-2)
+      self.assertAllClose(
+          np.cov(sample_values, rowvar=0), stats.beta.var(a, b), atol=1e-1)
+
+  # Test that sampling with the same seed twice gives the same results.
+  def testBetaSampleMultipleTimes(self):
+    with self.test_session():
+      a_val = 1.
+      b_val = 2.
+      n_val = 100
+
+      random_seed.set_random_seed(654321)
+      beta1 = beta_lib.Beta(concentration1=a_val,
+                            concentration0=b_val,
+                            name="beta1")
+      samples1 = beta1.sample(n_val, seed=123456).eval()
+
+      random_seed.set_random_seed(654321)
+      beta2 = beta_lib.Beta(concentration1=a_val,
+                            concentration0=b_val,
+                            name="beta2")
+      samples2 = beta2.sample(n_val, seed=123456).eval()
+
+      self.assertAllClose(samples1, samples2)
+
+  def testBetaSampleMultidimensional(self):
+    with self.test_session():
+      a = np.random.rand(3, 2, 2).astype(np.float32)
+      b = np.random.rand(3, 2, 2).astype(np.float32)
+      beta = beta_lib.Beta(a, b)
+      n = constant_op.constant(100000)
+      samples = beta.sample(n)
+      sample_values = samples.eval()
+      self.assertEqual(sample_values.shape, (100000, 3, 2, 2))
+      self.assertFalse(np.any(sample_values < 0.0))
+      if not stats:
+        return
+      self.assertAllClose(
+          sample_values[:, 1, :].mean(axis=0),
+          stats.beta.mean(a, b)[1, :],
+          atol=1e-1)
+
+  def testBetaCdf(self):
+    with self.test_session():
+      shape = (30, 40, 50)
+      for dt in (np.float32, np.float64):
+        a = 10. * np.random.random(shape).astype(dt)
+        b = 10. * np.random.random(shape).astype(dt)
+        x = np.random.random(shape).astype(dt)
+        actual = beta_lib.Beta(a, b).cdf(x).eval()
+        self.assertAllEqual(np.ones(shape, dtype=np.bool), 0. <= x)
+        self.assertAllEqual(np.ones(shape, dtype=np.bool), 1. >= x)
+        if not stats:
+          return
+        self.assertAllClose(stats.beta.cdf(x, a, b), actual, rtol=1e-4, atol=0)
+
+  def testBetaLogCdf(self):
+    with self.test_session():
+      shape = (30, 40, 50)
+      for dt in (np.float32, np.float64):
+        a = 10. * np.random.random(shape).astype(dt)
+        b = 10. * np.random.random(shape).astype(dt)
+        x = np.random.random(shape).astype(dt)
+        actual = math_ops.exp(beta_lib.Beta(a, b).log_cdf(x)).eval()
+        self.assertAllEqual(np.ones(shape, dtype=np.bool), 0. <= x)
+        self.assertAllEqual(np.ones(shape, dtype=np.bool), 1. >= x)
+        if not stats:
+          return
+        self.assertAllClose(stats.beta.cdf(x, a, b), actual, rtol=1e-4, atol=0)
+
+  def testBetaWithSoftplusConcentration(self):
+    with self.test_session():
+      a, b = -4.2, -9.1
+      dist = beta_lib.BetaWithSoftplusConcentration(a, b)
+      self.assertAllClose(nn_ops.softplus(a).eval(), dist.concentration1.eval())
+      self.assertAllClose(nn_ops.softplus(b).eval(), dist.concentration0.eval())
+
+  def testBetaBetaKL(self):
+    with self.test_session() as sess:
+      for shape in [(10,), (4, 5)]:
+        a1 = 6.0 * np.random.random(size=shape) + 1e-4
+        b1 = 6.0 * np.random.random(size=shape) + 1e-4
+        a2 = 6.0 * np.random.random(size=shape) + 1e-4
+        b2 = 6.0 * np.random.random(size=shape) + 1e-4
+        # Take inverse softplus of values to test BetaWithSoftplusConcentration
+        a1_sp = np.log(np.exp(a1) - 1.0)
+        b1_sp = np.log(np.exp(b1) - 1.0)
+        a2_sp = np.log(np.exp(a2) - 1.0)
+        b2_sp = np.log(np.exp(b2) - 1.0)
+
+        d1 = beta_lib.Beta(concentration1=a1, concentration0=b1)
+        d2 = beta_lib.Beta(concentration1=a2, concentration0=b2)
+        d1_sp = beta_lib.BetaWithSoftplusConcentration(concentration1=a1_sp,
+                                                       concentration0=b1_sp)
+        d2_sp = beta_lib.BetaWithSoftplusConcentration(concentration1=a2_sp,
+                                                       concentration0=b2_sp)
+
+        if not special:
+          return
+        kl_expected = (special.betaln(a2, b2) - special.betaln(a1, b1) +
+                       (a1 - a2) * special.digamma(a1) +
+                       (b1 - b2) * special.digamma(b1) +
+                       (a2 - a1 + b2 - b1) * special.digamma(a1 + b1))
+
+        for dist1 in [d1, d1_sp]:
+          for dist2 in [d2, d2_sp]:
+            kl = kullback_leibler.kl_divergence(dist1, dist2)
+            kl_val = sess.run(kl)
+            self.assertEqual(kl.get_shape(), shape)
+            self.assertAllClose(kl_val, kl_expected)
+
+        # Make sure KL(d1||d1) is 0
+        kl_same = sess.run(kullback_leibler.kl_divergence(d1, d1))
+        self.assertAllClose(kl_same, np.zeros_like(kl_expected))
+
+
+if __name__ == "__main__":
+  test.main()