tensorflow/python/ops/init_ops_v2_test.py - third_party/github.com/tensorflow/tensorflow - Git at Google

 # Copyright 2018 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.
 # ==============================================================================
 """Tests for initializers in init_ops_v2."""

 import numpy as np

 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops_v2
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test


 class InitializersTest(test.TestCase):

   def _identical_test(self,
                       init1,
                       init2,
                       assertion,
                       shape=None,
                       dtype=dtypes.float32):
     if shape is None:
       shape = [100]
     t1 = self.evaluate(init1(shape, dtype))
     t2 = self.evaluate(init2(shape, dtype))
     self.assertEqual(tensor_shape.as_shape(shape), t1.shape)
     self.assertEqual(tensor_shape.as_shape(shape), t2.shape)
     self.assertEqual(assertion, np.allclose(t1, t2, rtol=1e-15, atol=1e-15))

   def _duplicated_test(self, init, shape=None, dtype=dtypes.float32):
     if shape is None:
       shape = [100]
     t1 = self.evaluate(init(shape, dtype))
     t2 = self.evaluate(init(shape, dtype))
     self.assertEqual(tensor_shape.as_shape(shape), t1.shape)
     self.assertEqual(tensor_shape.as_shape(shape), t2.shape)
     self.assertFalse(np.allclose(t1, t2, rtol=1e-15, atol=1e-15))

   def _range_test(self,
                   init,
                   shape,
                   target_mean=None,
                   target_std=None,
                   target_max=None,
                   target_min=None):
     output = self.evaluate(init(shape))
     self.assertEqual(output.shape, shape)
     lim = 3e-2
     if target_std is not None:
       self.assertGreater(lim, abs(output.std() - target_std))
     if target_mean is not None:
       self.assertGreater(lim, abs(output.mean() - target_mean))
     if target_max is not None:
       self.assertGreater(lim, abs(output.max() - target_max))
     if target_min is not None:
       self.assertGreater(lim, abs(output.min() - target_min))

   def _partition_test(self, init):
     full_shape = (4, 2)
     partition_shape = (2, 2)
     partition_offset = (0, 0)
     full_value = self.evaluate(init(full_shape, dtype=dtypes.float32))
     got = self.evaluate(
         init(
             full_shape,
             dtype=dtypes.float32,
             partition_shape=partition_shape,
             partition_offset=partition_offset))
     self.assertEqual(got.shape, partition_shape)
     self.assertAllClose(
         got, array_ops.slice(full_value, partition_offset, partition_shape))


 class ConstantInitializersTest(InitializersTest):

   @test_util.run_in_graph_and_eager_modes
   def testZeros(self):
     self._range_test(
         init_ops_v2.Zeros(), shape=(4, 5), target_mean=0., target_max=0.)

   @test_util.run_in_graph_and_eager_modes
   def testZerosPartition(self):
     init = init_ops_v2.Zeros()
     self._partition_test(init)

   @test_util.run_in_graph_and_eager_modes
   def testZerosInvalidKwargs(self):
     init = init_ops_v2.Zeros()
     with self.assertRaisesRegex(
         TypeError, r"Keyword argument should be one of .* Received: dtpye"):
       init((2, 2), dtpye=dtypes.float32)

   @test_util.run_in_graph_and_eager_modes
   def testOnes(self):
     self._range_test(
         init_ops_v2.Ones(), shape=(4, 5), target_mean=1., target_max=1.)

   @test_util.run_in_graph_and_eager_modes
   def testOnesPartition(self):
     init = init_ops_v2.Ones()
     self._partition_test(init)

   @test_util.run_in_graph_and_eager_modes
   def testConstantInt(self):
     self._range_test(
         init_ops_v2.Constant(2),
         shape=(5, 6, 4),
         target_mean=2,
         target_max=2,
         target_min=2)

   @test_util.run_in_graph_and_eager_modes
   def testConstantPartition(self):
     init = init_ops_v2.Constant([1, 2, 3, 4])
     with self.assertRaisesWithLiteralMatch(
         ValueError,
         r"Constant initializer doesn't support partition-related arguments"):
       init((4, 2), dtype=dtypes.float32, partition_shape=(2, 2))

   @test_util.run_in_graph_and_eager_modes
   def testConstantTuple(self):
     init = init_ops_v2.constant_initializer((10, 20, 30))
     tensor = init(shape=[3])
     self.assertAllEqual(self.evaluate(tensor), [10, 20, 30])
     self.assertEqual(tensor.shape, [3])

   @test_util.run_in_graph_and_eager_modes
   def testConstantInvalidValue(self):
     c = constant_op.constant([1.0, 2.0, 3.0])
     with self.assertRaisesRegex(TypeError,
                                 r"Invalid type for initial value: .*Tensor.*"):
       init_ops_v2.constant_initializer(c)
     v = variables.Variable([3.0, 2.0, 1.0])
     with self.assertRaisesRegex(
         TypeError, r"Invalid type for initial value: .*Variable.*"):
       init_ops_v2.constant_initializer(v)

   def _testNDimConstantInitializer(self, value, shape, expected):
     with test_util.use_gpu():
       init = init_ops_v2.constant_initializer(value)
       x = init(shape)

       actual = self.evaluate(array_ops.reshape(x, [-1]))
       self.assertEqual(len(actual), len(expected))
       for a, e in zip(actual, expected):
         self.assertEqual(a, e)

   @test_util.run_in_graph_and_eager_modes
   def testNDimConstantInitializer(self):
     value = [0, 1, 2, 3, 4, 5]
     shape = [2, 3]
     expected = list(value)

     self._testNDimConstantInitializer(value, shape, expected)
     self._testNDimConstantInitializer(np.asarray(value), shape, expected)
     self._testNDimConstantInitializer(
         np.asarray(value).reshape(tuple(shape)), shape, expected)

   def _testNDimConstantInitializerIncorrectNumberValues(self, value, shape):
     with test_util.use_gpu():
       init = init_ops_v2.constant_initializer(value)
       self.assertRaises(TypeError, init, shape=shape)

   @test_util.run_in_graph_and_eager_modes
   def testNDimConstantInitializerIncorrectNumberValues(self):
     value = [0, 1, 2, 3, 4, 5]

     for shape in [[2, 4], [2, 2]]:
       self._testNDimConstantInitializerIncorrectNumberValues(value, shape)
       self._testNDimConstantInitializerIncorrectNumberValues(
           np.asarray(value), shape)
       self._testNDimConstantInitializerIncorrectNumberValues(
           np.asarray(value).reshape(tuple([2, 3])), shape)


 class RandomUniformInitializerTest(InitializersTest):

   @test_util.run_in_graph_and_eager_modes
   def testRangeInitializer(self):
     shape = (20, 6, 7)
     self._range_test(
         init_ops_v2.RandomUniform(minval=-1, maxval=1, seed=124),
         shape,
         target_mean=0.,
         target_max=1,
         target_min=-1)

   @test_util.run_in_graph_and_eager_modes
   def testInitializerIdentical(self):
     self.skipTest("Doesn't work without the graphs")
     init1 = init_ops_v2.RandomUniform(0, 7, seed=1)
     init2 = init_ops_v2.RandomUniform(0, 7, seed=1)
     self._identical_test(init1, init2, True)

   @test_util.run_in_graph_and_eager_modes
   def testInitializerDifferent(self):
     init1 = init_ops_v2.RandomUniform(0, 7, seed=1)
     init2 = init_ops_v2.RandomUniform(0, 7, seed=2)
     self._identical_test(init1, init2, False)

   @test_util.run_in_graph_and_eager_modes
   def testDuplicatedInitializer(self):
     init = init_ops_v2.RandomUniform(0.0, 1.0)
     self._duplicated_test(init)

   @test_util.run_in_graph_and_eager_modes
   def testInitializePartition(self):
     init = init_ops_v2.RandomUniform(0, 7, seed=1)
     self._partition_test(init)


 class RandomNormalInitializerTest(InitializersTest):

   @test_util.run_in_graph_and_eager_modes
   def testRangeInitializer(self):
     self._range_test(
         init_ops_v2.RandomNormal(mean=0, stddev=1, seed=153),
         shape=(8, 12, 99),
         target_mean=0.,
         target_std=1)

   @test_util.run_in_graph_and_eager_modes
   def testInitializerIdentical(self):
     self.skipTest("Doesn't work without the graphs")
     init1 = init_ops_v2.RandomNormal(0, 7, seed=1)
     init2 = init_ops_v2.RandomNormal(0, 7, seed=1)
     self._identical_test(init1, init2, True)

   @test_util.run_in_graph_and_eager_modes
   def testInitializerDifferent(self):
     init1 = init_ops_v2.RandomNormal(0, 7, seed=1)
     init2 = init_ops_v2.RandomNormal(0, 7, seed=2)
     self._identical_test(init1, init2, False)

   @test_util.run_in_graph_and_eager_modes
   def testDuplicatedInitializer(self):
     init = init_ops_v2.RandomNormal(0.0, 1.0)
     self._duplicated_test(init)

   @test_util.run_in_graph_and_eager_modes
   def testInitializePartition(self):
     if test_util.is_xla_enabled():
       self.skipTest(
           "XLA ignores seeds for RandomNormal, skip xla-enabled test.")
     init = init_ops_v2.RandomNormal(0, 7, seed=1)
     self._partition_test(init)


 class TruncatedNormalInitializerTest(InitializersTest):

   @test_util.run_in_graph_and_eager_modes
   def testRangeInitializer(self):
     self._range_test(
         init_ops_v2.TruncatedNormal(mean=0, stddev=1, seed=126),
         shape=(12, 99, 7),
         target_mean=0.,
         target_max=2,
         target_min=-2)

   @test_util.run_in_graph_and_eager_modes
   def testInitializerIdentical(self):
     self.skipTest("Not seeming to work in Eager mode")
     init1 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
     init2 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
     self._identical_test(init1, init2, True)

   @test_util.run_in_graph_and_eager_modes
   def testInitializerDifferent(self):
     init1 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
     init2 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=2)
     self._identical_test(init1, init2, False)

   @test_util.run_in_graph_and_eager_modes
   def testDuplicatedInitializer(self):
     init = init_ops_v2.TruncatedNormal(0.0, 1.0)
     self._duplicated_test(init)

   @test_util.run_in_graph_and_eager_modes
   def testInitializePartition(self):
     init = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
     self._partition_test(init)

   @test_util.run_in_graph_and_eager_modes
   def testInvalidDataType(self):
     init = init_ops_v2.TruncatedNormal(0.0, 1.0)
     with self.assertRaises(ValueError):
       init([1], dtype=dtypes.int32)


 class VarianceScalingInitializerTest(InitializersTest):

   @test_util.run_in_graph_and_eager_modes
   def testTruncatedNormalDistribution(self):
     shape = [100, 100]
     expect_mean = 0.
     expect_var = 1. / shape[0]
     init = init_ops_v2.VarianceScaling(distribution="truncated_normal")

     with test_util.use_gpu(), test.mock.patch.object(
         random_ops, "truncated_normal",
         wraps=random_ops.truncated_normal) as mock_truncated_normal:
       x = self.evaluate(init(shape))
       self.assertTrue(mock_truncated_normal.called)

     self.assertNear(np.mean(x), expect_mean, err=1e-2)
     self.assertNear(np.var(x), expect_var, err=1e-2)

   @test_util.run_in_graph_and_eager_modes
   def testNormalDistribution(self):
     shape = [100, 100]
     expect_mean = 0.
     expect_var = 1. / shape[0]
     init = init_ops_v2.VarianceScaling(distribution="truncated_normal")

     with test_util.use_gpu(), test.mock.patch.object(
         random_ops, "truncated_normal",
         wraps=random_ops.truncated_normal) as mock_truncated_normal:
       x = self.evaluate(init(shape))
       self.assertTrue(mock_truncated_normal.called)

     self.assertNear(np.mean(x), expect_mean, err=1e-2)
     self.assertNear(np.var(x), expect_var, err=1e-2)

   @test_util.run_in_graph_and_eager_modes
   def testUntruncatedNormalDistribution(self):
     shape = [100, 100]
     expect_mean = 0.
     expect_var = 1. / shape[0]
     init = init_ops_v2.VarianceScaling(distribution="untruncated_normal")

     with test_util.use_gpu(), test.mock.patch.object(
         random_ops, "random_normal",
         wraps=random_ops.random_normal) as mock_random_normal:
       x = self.evaluate(init(shape))
       self.assertTrue(mock_random_normal.called)

     self.assertNear(np.mean(x), expect_mean, err=1e-2)
     self.assertNear(np.var(x), expect_var, err=1e-2)

   @test_util.run_in_graph_and_eager_modes
   def testUniformDistribution(self):
     shape = [100, 100]
     expect_mean = 0.
     expect_var = 1. / shape[0]
     init = init_ops_v2.VarianceScaling(distribution="uniform")

     with test_util.use_gpu():
       x = self.evaluate(init(shape))

     self.assertNear(np.mean(x), expect_mean, err=1e-2)
     self.assertNear(np.var(x), expect_var, err=1e-2)

   @test_util.run_in_graph_and_eager_modes
   def testInitializePartition(self):
     partition_shape = (100, 100)
     shape = [1000, 100]
     expect_mean = 0.
     expect_var = 1. / shape[0]
     init = init_ops_v2.VarianceScaling(distribution="untruncated_normal")

     with test_util.use_gpu(), test.mock.patch.object(
         random_ops, "random_normal",
         wraps=random_ops.random_normal) as mock_random_normal:
       x = self.evaluate(init(shape, partition_shape=partition_shape))
       self.assertTrue(mock_random_normal.called)

     self.assertEqual(x.shape, partition_shape)
     self.assertNear(np.mean(x), expect_mean, err=2e-3)
     self.assertNear(np.var(x), expect_var, err=2e-3)


 class OrthogonalInitializerTest(InitializersTest):

   @test_util.run_in_graph_and_eager_modes
   def testRangeInitializer(self):
     self._range_test(
         init_ops_v2.Orthogonal(seed=123), shape=(20, 20), target_mean=0.)

   @test_util.run_in_graph_and_eager_modes
   def testInitializerIdentical(self):
     self.skipTest("Doesn't work without the graphs")
     init1 = init_ops_v2.Orthogonal(seed=1)
     init2 = init_ops_v2.Orthogonal(seed=1)
     self._identical_test(init1, init2, True, (10, 10))

   @test_util.run_in_graph_and_eager_modes
   def testInitializerDifferent(self):
     init1 = init_ops_v2.Orthogonal(seed=1)
     init2 = init_ops_v2.Orthogonal(seed=2)
     self._identical_test(init1, init2, False, (10, 10))

   @test_util.run_in_graph_and_eager_modes
   def testDuplicatedInitializer(self):
     init = init_ops_v2.Orthogonal()
     self._duplicated_test(init, (10, 10))

   @test_util.run_in_graph_and_eager_modes
   def testInvalidDataType(self):
     init = init_ops_v2.Orthogonal()
     self.assertRaises(ValueError, init, shape=(10, 10), dtype=dtypes.string)

   @test_util.run_in_graph_and_eager_modes
   def testInvalidShape(self):
     init = init_ops_v2.Orthogonal()
     with test_util.use_gpu():
       self.assertRaises(ValueError, init, shape=[5])

   @test_util.run_in_graph_and_eager_modes
   def testGain(self):
     self.skipTest("Doesn't work without the graphs")
     init1 = init_ops_v2.Orthogonal(seed=1)
     init2 = init_ops_v2.Orthogonal(gain=3.14, seed=1)
     with test_util.use_gpu():
       t1 = self.evaluate(init1(shape=(10, 10)))
       t2 = self.evaluate(init2(shape=(10, 10)))
     self.assertAllClose(t1, t2 / 3.14)

   @test_util.run_in_graph_and_eager_modes
   def testShapesValues(self):
     for shape in [(10, 10), (10, 9, 8), (100, 5, 5), (50, 40), (40, 50)]:
       init = init_ops_v2.Orthogonal()
       tol = 1e-5
       with test_util.use_gpu():
         # Check the shape
         t = self.evaluate(init(shape))
         self.assertAllEqual(shape, t.shape)
         # Check orthogonality by computing the inner product
         t = t.reshape((np.prod(t.shape[:-1]), t.shape[-1]))
         if t.shape[0] > t.shape[1]:
           self.assertAllClose(
               np.dot(t.T, t), np.eye(t.shape[1]), rtol=tol, atol=tol)
         else:
           self.assertAllClose(
               np.dot(t, t.T), np.eye(t.shape[0]), rtol=tol, atol=tol)

   @test_util.run_in_graph_and_eager_modes
   def testPartition(self):
     init = init_ops_v2.Orthogonal(seed=1)
     with self.assertRaisesWithLiteralMatch(
         ValueError,
         r"Orthogonal initializer doesn't support partition-related arguments"):
       init((4, 2), dtype=dtypes.float32, partition_shape=(2, 2))


 class IdentityInitializerTest(InitializersTest):

   @test_util.run_in_graph_and_eager_modes
   def testRange(self):
     with self.assertRaises(ValueError):
       shape = (3, 4, 5)
       self._range_test(
           init_ops_v2.Identity(),
           shape=shape,
           target_mean=1. / shape[0],
           target_max=1.)

     shape = (3, 3)
     self._range_test(
         init_ops_v2.Identity(),
         shape=shape,
         target_mean=1. / shape[0],
         target_max=1.)

   @test_util.run_in_graph_and_eager_modes
   def testInvalidDataType(self):
     init = init_ops_v2.Identity()
     self.assertRaises(ValueError, init, shape=[10, 5], dtype=dtypes.int32)

   @test_util.run_in_graph_and_eager_modes
   def testInvalidShape(self):
     init = init_ops_v2.Identity()
     with test_util.use_gpu():
       self.assertRaises(ValueError, init, shape=[5, 7, 7])
       self.assertRaises(ValueError, init, shape=[5])
       self.assertRaises(ValueError, init, shape=[])

   @test_util.run_in_graph_and_eager_modes
   def testNonSquare(self):
     init = init_ops_v2.Identity()
     shape = (10, 5)
     with test_util.use_gpu():
       self.assertAllClose(self.evaluate(init(shape)), np.eye(*shape))

   @test_util.run_in_graph_and_eager_modes
   def testGain(self):
     shape = (10, 10)
     for dtype in [dtypes.float32, dtypes.float64]:
       init_default = init_ops_v2.Identity()
       init_custom = init_ops_v2.Identity(gain=0.9)
       with test_util.use_gpu():
         self.assertAllClose(
             self.evaluate(init_default(shape, dtype=dtype)), np.eye(*shape))
       with test_util.use_gpu():
         self.assertAllClose(
             self.evaluate(init_custom(shape, dtype=dtype)),
             np.eye(*shape) * 0.9)

   @test_util.run_in_graph_and_eager_modes
   def testPartition(self):
     init = init_ops_v2.Identity()
     with self.assertRaisesWithLiteralMatch(
         ValueError,
         r"Identity initializer doesn't support partition-related arguments"):
       init((4, 2), dtype=dtypes.float32, partition_shape=(2, 2))


 class GlorotInitializersTest(InitializersTest):

   @test_util.run_in_graph_and_eager_modes
   def testGlorotUniform(self):
     shape = (5, 6, 4, 2)
     fan_in, fan_out = init_ops_v2._compute_fans(shape)
     std = np.sqrt(2. / (fan_in + fan_out))
     self._range_test(
         init_ops_v2.GlorotUniform(seed=123),
         shape,
         target_mean=0.,
         target_std=std)

   @test_util.run_in_graph_and_eager_modes
   def test_GlorotNormal(self):
     shape = (5, 6, 4, 2)
     fan_in, fan_out = init_ops_v2._compute_fans(shape)
     std = np.sqrt(2. / (fan_in + fan_out))
     self._range_test(
         init_ops_v2.GlorotNormal(seed=123),
         shape,
         target_mean=0.,
         target_std=std)


 class MethodInitializers(InitializersTest):

   @test_util.run_in_graph_and_eager_modes
   def testLecunUniform(self):
     shape = (5, 6, 4, 2)
     fan_in, _ = init_ops_v2._compute_fans(shape)
     std = np.sqrt(1. / fan_in)
     self._range_test(
         init_ops_v2.lecun_uniform(seed=123),
         shape,
         target_mean=0.,
         target_std=std)

   @test_util.run_in_graph_and_eager_modes
   def testHeUniform(self):
     shape = (5, 6, 4, 2)
     fan_in, _ = init_ops_v2._compute_fans(shape)
     std = np.sqrt(2. / fan_in)
     self._range_test(
         init_ops_v2.he_uniform(seed=123), shape, target_mean=0., target_std=std)

   @test_util.run_in_graph_and_eager_modes
   def testLecunNormal(self):
     shape = (5, 6, 4, 2)
     fan_in, _ = init_ops_v2._compute_fans(shape)
     std = np.sqrt(1. / fan_in)
     self._range_test(
         init_ops_v2.lecun_normal(seed=123),
         shape,
         target_mean=0.,
         target_std=std)

   @test_util.run_in_graph_and_eager_modes
   def testHeNormal(self):
     shape = (5, 6, 4, 2)
     fan_in, _ = init_ops_v2._compute_fans(shape)
     std = np.sqrt(2. / fan_in)
     self._range_test(
         init_ops_v2.he_normal(seed=123), shape, target_mean=0., target_std=std)


 if __name__ == "__main__":
   test.main()
	# Copyright 2018 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.
	# ==============================================================================
	"""Tests for initializers in init_ops_v2."""

	import numpy as np

	from tensorflow.python.framework import constant_op
	from tensorflow.python.framework import dtypes
	from tensorflow.python.framework import tensor_shape
	from tensorflow.python.framework import test_util
	from tensorflow.python.ops import array_ops
	from tensorflow.python.ops import init_ops_v2
	from tensorflow.python.ops import random_ops
	from tensorflow.python.ops import variables
	from tensorflow.python.platform import test


	class InitializersTest(test.TestCase):

	def _identical_test(self,
	init1,
	init2,
	assertion,
	shape=None,
	dtype=dtypes.float32):
	if shape is None:
	shape = [100]
	t1 = self.evaluate(init1(shape, dtype))
	t2 = self.evaluate(init2(shape, dtype))
	self.assertEqual(tensor_shape.as_shape(shape), t1.shape)
	self.assertEqual(tensor_shape.as_shape(shape), t2.shape)
	self.assertEqual(assertion, np.allclose(t1, t2, rtol=1e-15, atol=1e-15))

	def _duplicated_test(self, init, shape=None, dtype=dtypes.float32):
	if shape is None:
	shape = [100]
	t1 = self.evaluate(init(shape, dtype))
	t2 = self.evaluate(init(shape, dtype))
	self.assertEqual(tensor_shape.as_shape(shape), t1.shape)
	self.assertEqual(tensor_shape.as_shape(shape), t2.shape)
	self.assertFalse(np.allclose(t1, t2, rtol=1e-15, atol=1e-15))

	def _range_test(self,
	init,
	shape,
	target_mean=None,
	target_std=None,
	target_max=None,
	target_min=None):
	output = self.evaluate(init(shape))
	self.assertEqual(output.shape, shape)
	lim = 3e-2
	if target_std is not None:
	self.assertGreater(lim, abs(output.std() - target_std))
	if target_mean is not None:
	self.assertGreater(lim, abs(output.mean() - target_mean))
	if target_max is not None:
	self.assertGreater(lim, abs(output.max() - target_max))
	if target_min is not None:
	self.assertGreater(lim, abs(output.min() - target_min))

	def _partition_test(self, init):
	full_shape = (4, 2)
	partition_shape = (2, 2)
	partition_offset = (0, 0)
	full_value = self.evaluate(init(full_shape, dtype=dtypes.float32))
	got = self.evaluate(
	init(
	full_shape,
	dtype=dtypes.float32,
	partition_shape=partition_shape,
	partition_offset=partition_offset))
	self.assertEqual(got.shape, partition_shape)
	self.assertAllClose(
	got, array_ops.slice(full_value, partition_offset, partition_shape))


	class ConstantInitializersTest(InitializersTest):

	@test_util.run_in_graph_and_eager_modes
	def testZeros(self):
	self._range_test(
	init_ops_v2.Zeros(), shape=(4, 5), target_mean=0., target_max=0.)

	@test_util.run_in_graph_and_eager_modes
	def testZerosPartition(self):
	init = init_ops_v2.Zeros()
	self._partition_test(init)

	@test_util.run_in_graph_and_eager_modes
	def testZerosInvalidKwargs(self):
	init = init_ops_v2.Zeros()
	with self.assertRaisesRegex(
	TypeError, r"Keyword argument should be one of .* Received: dtpye"):
	init((2, 2), dtpye=dtypes.float32)

	@test_util.run_in_graph_and_eager_modes
	def testOnes(self):
	self._range_test(
	init_ops_v2.Ones(), shape=(4, 5), target_mean=1., target_max=1.)

	@test_util.run_in_graph_and_eager_modes
	def testOnesPartition(self):
	init = init_ops_v2.Ones()
	self._partition_test(init)

	@test_util.run_in_graph_and_eager_modes
	def testConstantInt(self):
	self._range_test(
	init_ops_v2.Constant(2),
	shape=(5, 6, 4),
	target_mean=2,
	target_max=2,
	target_min=2)

	@test_util.run_in_graph_and_eager_modes
	def testConstantPartition(self):
	init = init_ops_v2.Constant([1, 2, 3, 4])
	with self.assertRaisesWithLiteralMatch(
	ValueError,
	r"Constant initializer doesn't support partition-related arguments"):
	init((4, 2), dtype=dtypes.float32, partition_shape=(2, 2))

	@test_util.run_in_graph_and_eager_modes
	def testConstantTuple(self):
	init = init_ops_v2.constant_initializer((10, 20, 30))
	tensor = init(shape=[3])
	self.assertAllEqual(self.evaluate(tensor), [10, 20, 30])
	self.assertEqual(tensor.shape, [3])

	@test_util.run_in_graph_and_eager_modes
	def testConstantInvalidValue(self):
	c = constant_op.constant([1.0, 2.0, 3.0])
	with self.assertRaisesRegex(TypeError,
	r"Invalid type for initial value: .Tensor."):
	init_ops_v2.constant_initializer(c)
	v = variables.Variable([3.0, 2.0, 1.0])
	with self.assertRaisesRegex(
	TypeError, r"Invalid type for initial value: .Variable."):
	init_ops_v2.constant_initializer(v)

	def _testNDimConstantInitializer(self, value, shape, expected):
	with test_util.use_gpu():
	init = init_ops_v2.constant_initializer(value)
	x = init(shape)

	actual = self.evaluate(array_ops.reshape(x, [-1]))
	self.assertEqual(len(actual), len(expected))
	for a, e in zip(actual, expected):
	self.assertEqual(a, e)

	@test_util.run_in_graph_and_eager_modes
	def testNDimConstantInitializer(self):
	value = [0, 1, 2, 3, 4, 5]
	shape = [2, 3]
	expected = list(value)

	self._testNDimConstantInitializer(value, shape, expected)
	self._testNDimConstantInitializer(np.asarray(value), shape, expected)
	self._testNDimConstantInitializer(
	np.asarray(value).reshape(tuple(shape)), shape, expected)

	def _testNDimConstantInitializerIncorrectNumberValues(self, value, shape):
	with test_util.use_gpu():
	init = init_ops_v2.constant_initializer(value)
	self.assertRaises(TypeError, init, shape=shape)

	@test_util.run_in_graph_and_eager_modes
	def testNDimConstantInitializerIncorrectNumberValues(self):
	value = [0, 1, 2, 3, 4, 5]

	for shape in [[2, 4], [2, 2]]:
	self._testNDimConstantInitializerIncorrectNumberValues(value, shape)
	self._testNDimConstantInitializerIncorrectNumberValues(
	np.asarray(value), shape)
	self._testNDimConstantInitializerIncorrectNumberValues(
	np.asarray(value).reshape(tuple([2, 3])), shape)


	class RandomUniformInitializerTest(InitializersTest):

	@test_util.run_in_graph_and_eager_modes
	def testRangeInitializer(self):
	shape = (20, 6, 7)
	self._range_test(
	init_ops_v2.RandomUniform(minval=-1, maxval=1, seed=124),
	shape,
	target_mean=0.,
	target_max=1,
	target_min=-1)

	@test_util.run_in_graph_and_eager_modes
	def testInitializerIdentical(self):
	self.skipTest("Doesn't work without the graphs")
	init1 = init_ops_v2.RandomUniform(0, 7, seed=1)
	init2 = init_ops_v2.RandomUniform(0, 7, seed=1)
	self._identical_test(init1, init2, True)

	@test_util.run_in_graph_and_eager_modes
	def testInitializerDifferent(self):
	init1 = init_ops_v2.RandomUniform(0, 7, seed=1)
	init2 = init_ops_v2.RandomUniform(0, 7, seed=2)
	self._identical_test(init1, init2, False)

	@test_util.run_in_graph_and_eager_modes
	def testDuplicatedInitializer(self):
	init = init_ops_v2.RandomUniform(0.0, 1.0)
	self._duplicated_test(init)

	@test_util.run_in_graph_and_eager_modes
	def testInitializePartition(self):
	init = init_ops_v2.RandomUniform(0, 7, seed=1)
	self._partition_test(init)


	class RandomNormalInitializerTest(InitializersTest):

	@test_util.run_in_graph_and_eager_modes
	def testRangeInitializer(self):
	self._range_test(
	init_ops_v2.RandomNormal(mean=0, stddev=1, seed=153),
	shape=(8, 12, 99),
	target_mean=0.,
	target_std=1)

	@test_util.run_in_graph_and_eager_modes
	def testInitializerIdentical(self):
	self.skipTest("Doesn't work without the graphs")
	init1 = init_ops_v2.RandomNormal(0, 7, seed=1)
	init2 = init_ops_v2.RandomNormal(0, 7, seed=1)
	self._identical_test(init1, init2, True)

	@test_util.run_in_graph_and_eager_modes
	def testInitializerDifferent(self):
	init1 = init_ops_v2.RandomNormal(0, 7, seed=1)
	init2 = init_ops_v2.RandomNormal(0, 7, seed=2)
	self._identical_test(init1, init2, False)

	@test_util.run_in_graph_and_eager_modes
	def testDuplicatedInitializer(self):
	init = init_ops_v2.RandomNormal(0.0, 1.0)
	self._duplicated_test(init)

	@test_util.run_in_graph_and_eager_modes
	def testInitializePartition(self):
	if test_util.is_xla_enabled():
	self.skipTest(
	"XLA ignores seeds for RandomNormal, skip xla-enabled test.")
	init = init_ops_v2.RandomNormal(0, 7, seed=1)
	self._partition_test(init)


	class TruncatedNormalInitializerTest(InitializersTest):

	@test_util.run_in_graph_and_eager_modes
	def testRangeInitializer(self):
	self._range_test(
	init_ops_v2.TruncatedNormal(mean=0, stddev=1, seed=126),
	shape=(12, 99, 7),
	target_mean=0.,
	target_max=2,
	target_min=-2)

	@test_util.run_in_graph_and_eager_modes
	def testInitializerIdentical(self):
	self.skipTest("Not seeming to work in Eager mode")
	init1 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
	init2 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
	self._identical_test(init1, init2, True)

	@test_util.run_in_graph_and_eager_modes
	def testInitializerDifferent(self):
	init1 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
	init2 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=2)
	self._identical_test(init1, init2, False)

	@test_util.run_in_graph_and_eager_modes
	def testDuplicatedInitializer(self):
	init = init_ops_v2.TruncatedNormal(0.0, 1.0)
	self._duplicated_test(init)

	@test_util.run_in_graph_and_eager_modes
	def testInitializePartition(self):
	init = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
	self._partition_test(init)

	@test_util.run_in_graph_and_eager_modes
	def testInvalidDataType(self):
	init = init_ops_v2.TruncatedNormal(0.0, 1.0)
	with self.assertRaises(ValueError):
	init([1], dtype=dtypes.int32)


	class VarianceScalingInitializerTest(InitializersTest):

	@test_util.run_in_graph_and_eager_modes
	def testTruncatedNormalDistribution(self):
	shape = [100, 100]
	expect_mean = 0.
	expect_var = 1. / shape[0]
	init = init_ops_v2.VarianceScaling(distribution="truncated_normal")

	with test_util.use_gpu(), test.mock.patch.object(
	random_ops, "truncated_normal",
	wraps=random_ops.truncated_normal) as mock_truncated_normal:
	x = self.evaluate(init(shape))
	self.assertTrue(mock_truncated_normal.called)

	self.assertNear(np.mean(x), expect_mean, err=1e-2)
	self.assertNear(np.var(x), expect_var, err=1e-2)

	@test_util.run_in_graph_and_eager_modes
	def testNormalDistribution(self):
	shape = [100, 100]
	expect_mean = 0.
	expect_var = 1. / shape[0]
	init = init_ops_v2.VarianceScaling(distribution="truncated_normal")

	with test_util.use_gpu(), test.mock.patch.object(
	random_ops, "truncated_normal",
	wraps=random_ops.truncated_normal) as mock_truncated_normal:
	x = self.evaluate(init(shape))
	self.assertTrue(mock_truncated_normal.called)

	self.assertNear(np.mean(x), expect_mean, err=1e-2)
	self.assertNear(np.var(x), expect_var, err=1e-2)

	@test_util.run_in_graph_and_eager_modes
	def testUntruncatedNormalDistribution(self):
	shape = [100, 100]
	expect_mean = 0.
	expect_var = 1. / shape[0]
	init = init_ops_v2.VarianceScaling(distribution="untruncated_normal")

	with test_util.use_gpu(), test.mock.patch.object(
	random_ops, "random_normal",
	wraps=random_ops.random_normal) as mock_random_normal:
	x = self.evaluate(init(shape))
	self.assertTrue(mock_random_normal.called)

	self.assertNear(np.mean(x), expect_mean, err=1e-2)
	self.assertNear(np.var(x), expect_var, err=1e-2)

	@test_util.run_in_graph_and_eager_modes
	def testUniformDistribution(self):
	shape = [100, 100]
	expect_mean = 0.
	expect_var = 1. / shape[0]
	init = init_ops_v2.VarianceScaling(distribution="uniform")

	with test_util.use_gpu():
	x = self.evaluate(init(shape))

	self.assertNear(np.mean(x), expect_mean, err=1e-2)
	self.assertNear(np.var(x), expect_var, err=1e-2)

	@test_util.run_in_graph_and_eager_modes
	def testInitializePartition(self):
	partition_shape = (100, 100)
	shape = [1000, 100]
	expect_mean = 0.
	expect_var = 1. / shape[0]
	init = init_ops_v2.VarianceScaling(distribution="untruncated_normal")

	with test_util.use_gpu(), test.mock.patch.object(
	random_ops, "random_normal",
	wraps=random_ops.random_normal) as mock_random_normal:
	x = self.evaluate(init(shape, partition_shape=partition_shape))
	self.assertTrue(mock_random_normal.called)

	self.assertEqual(x.shape, partition_shape)
	self.assertNear(np.mean(x), expect_mean, err=2e-3)
	self.assertNear(np.var(x), expect_var, err=2e-3)


	class OrthogonalInitializerTest(InitializersTest):

	@test_util.run_in_graph_and_eager_modes
	def testRangeInitializer(self):
	self._range_test(
	init_ops_v2.Orthogonal(seed=123), shape=(20, 20), target_mean=0.)

	@test_util.run_in_graph_and_eager_modes
	def testInitializerIdentical(self):
	self.skipTest("Doesn't work without the graphs")
	init1 = init_ops_v2.Orthogonal(seed=1)
	init2 = init_ops_v2.Orthogonal(seed=1)
	self._identical_test(init1, init2, True, (10, 10))

	@test_util.run_in_graph_and_eager_modes
	def testInitializerDifferent(self):
	init1 = init_ops_v2.Orthogonal(seed=1)
	init2 = init_ops_v2.Orthogonal(seed=2)
	self._identical_test(init1, init2, False, (10, 10))

	@test_util.run_in_graph_and_eager_modes
	def testDuplicatedInitializer(self):
	init = init_ops_v2.Orthogonal()
	self._duplicated_test(init, (10, 10))

	@test_util.run_in_graph_and_eager_modes
	def testInvalidDataType(self):
	init = init_ops_v2.Orthogonal()
	self.assertRaises(ValueError, init, shape=(10, 10), dtype=dtypes.string)

	@test_util.run_in_graph_and_eager_modes
	def testInvalidShape(self):
	init = init_ops_v2.Orthogonal()
	with test_util.use_gpu():
	self.assertRaises(ValueError, init, shape=[5])

	@test_util.run_in_graph_and_eager_modes
	def testGain(self):
	self.skipTest("Doesn't work without the graphs")
	init1 = init_ops_v2.Orthogonal(seed=1)
	init2 = init_ops_v2.Orthogonal(gain=3.14, seed=1)
	with test_util.use_gpu():
	t1 = self.evaluate(init1(shape=(10, 10)))
	t2 = self.evaluate(init2(shape=(10, 10)))
	self.assertAllClose(t1, t2 / 3.14)

	@test_util.run_in_graph_and_eager_modes
	def testShapesValues(self):
	for shape in [(10, 10), (10, 9, 8), (100, 5, 5), (50, 40), (40, 50)]:
	init = init_ops_v2.Orthogonal()
	tol = 1e-5
	with test_util.use_gpu():
	# Check the shape
	t = self.evaluate(init(shape))
	self.assertAllEqual(shape, t.shape)
	# Check orthogonality by computing the inner product
	t = t.reshape((np.prod(t.shape[:-1]), t.shape[-1]))
	if t.shape[0] > t.shape[1]:
	self.assertAllClose(
	np.dot(t.T, t), np.eye(t.shape[1]), rtol=tol, atol=tol)
	else:
	self.assertAllClose(
	np.dot(t, t.T), np.eye(t.shape[0]), rtol=tol, atol=tol)

	@test_util.run_in_graph_and_eager_modes
	def testPartition(self):
	init = init_ops_v2.Orthogonal(seed=1)
	with self.assertRaisesWithLiteralMatch(
	ValueError,
	r"Orthogonal initializer doesn't support partition-related arguments"):
	init((4, 2), dtype=dtypes.float32, partition_shape=(2, 2))


	class IdentityInitializerTest(InitializersTest):

	@test_util.run_in_graph_and_eager_modes
	def testRange(self):
	with self.assertRaises(ValueError):
	shape = (3, 4, 5)
	self._range_test(
	init_ops_v2.Identity(),
	shape=shape,
	target_mean=1. / shape[0],
	target_max=1.)

	shape = (3, 3)
	self._range_test(
	init_ops_v2.Identity(),
	shape=shape,
	target_mean=1. / shape[0],
	target_max=1.)

	@test_util.run_in_graph_and_eager_modes
	def testInvalidDataType(self):
	init = init_ops_v2.Identity()
	self.assertRaises(ValueError, init, shape=[10, 5], dtype=dtypes.int32)

	@test_util.run_in_graph_and_eager_modes
	def testInvalidShape(self):
	init = init_ops_v2.Identity()
	with test_util.use_gpu():
	self.assertRaises(ValueError, init, shape=[5, 7, 7])
	self.assertRaises(ValueError, init, shape=[5])
	self.assertRaises(ValueError, init, shape=[])

	@test_util.run_in_graph_and_eager_modes
	def testNonSquare(self):
	init = init_ops_v2.Identity()
	shape = (10, 5)
	with test_util.use_gpu():
	self.assertAllClose(self.evaluate(init(shape)), np.eye(*shape))

	@test_util.run_in_graph_and_eager_modes
	def testGain(self):
	shape = (10, 10)
	for dtype in [dtypes.float32, dtypes.float64]:
	init_default = init_ops_v2.Identity()
	init_custom = init_ops_v2.Identity(gain=0.9)
	with test_util.use_gpu():
	self.assertAllClose(
	self.evaluate(init_default(shape, dtype=dtype)), np.eye(*shape))
	with test_util.use_gpu():
	self.assertAllClose(
	self.evaluate(init_custom(shape, dtype=dtype)),
	np.eye(shape) 0.9)

	@test_util.run_in_graph_and_eager_modes
	def testPartition(self):
	init = init_ops_v2.Identity()
	with self.assertRaisesWithLiteralMatch(
	ValueError,
	r"Identity initializer doesn't support partition-related arguments"):
	init((4, 2), dtype=dtypes.float32, partition_shape=(2, 2))


	class GlorotInitializersTest(InitializersTest):

	@test_util.run_in_graph_and_eager_modes
	def testGlorotUniform(self):
	shape = (5, 6, 4, 2)
	fan_in, fan_out = init_ops_v2._compute_fans(shape)
	std = np.sqrt(2. / (fan_in + fan_out))
	self._range_test(
	init_ops_v2.GlorotUniform(seed=123),
	shape,
	target_mean=0.,
	target_std=std)

	@test_util.run_in_graph_and_eager_modes
	def test_GlorotNormal(self):
	shape = (5, 6, 4, 2)
	fan_in, fan_out = init_ops_v2._compute_fans(shape)
	std = np.sqrt(2. / (fan_in + fan_out))
	self._range_test(
	init_ops_v2.GlorotNormal(seed=123),
	shape,
	target_mean=0.,
	target_std=std)


	class MethodInitializers(InitializersTest):

	@test_util.run_in_graph_and_eager_modes
	def testLecunUniform(self):
	shape = (5, 6, 4, 2)
	fan_in, _ = init_ops_v2._compute_fans(shape)
	std = np.sqrt(1. / fan_in)
	self._range_test(
	init_ops_v2.lecun_uniform(seed=123),
	shape,
	target_mean=0.,
	target_std=std)

	@test_util.run_in_graph_and_eager_modes
	def testHeUniform(self):
	shape = (5, 6, 4, 2)
	fan_in, _ = init_ops_v2._compute_fans(shape)
	std = np.sqrt(2. / fan_in)
	self._range_test(
	init_ops_v2.he_uniform(seed=123), shape, target_mean=0., target_std=std)

	@test_util.run_in_graph_and_eager_modes
	def testLecunNormal(self):
	shape = (5, 6, 4, 2)
	fan_in, _ = init_ops_v2._compute_fans(shape)
	std = np.sqrt(1. / fan_in)
	self._range_test(
	init_ops_v2.lecun_normal(seed=123),
	shape,
	target_mean=0.,
	target_std=std)

	@test_util.run_in_graph_and_eager_modes
	def testHeNormal(self):
	shape = (5, 6, 4, 2)
	fan_in, _ = init_ops_v2._compute_fans(shape)
	std = np.sqrt(2. / fan_in)
	self._range_test(
	init_ops_v2.he_normal(seed=123), shape, target_mean=0., target_std=std)


	if __name__ == "__main__":
	test.main()