595 lines
20 KiB
Python
595 lines
20 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for initializers in init_ops_v2."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import tensor_shape
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import init_ops_v2
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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class InitializersTest(test.TestCase):
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def _identical_test(self,
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init1,
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init2,
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assertion,
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shape=None,
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dtype=dtypes.float32):
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if shape is None:
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shape = [100]
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t1 = self.evaluate(init1(shape, dtype))
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t2 = self.evaluate(init2(shape, dtype))
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self.assertEqual(tensor_shape.as_shape(shape), t1.shape)
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self.assertEqual(tensor_shape.as_shape(shape), t2.shape)
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self.assertEqual(assertion, np.allclose(t1, t2, rtol=1e-15, atol=1e-15))
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def _duplicated_test(self, init, shape=None, dtype=dtypes.float32):
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if shape is None:
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shape = [100]
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t1 = self.evaluate(init(shape, dtype))
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t2 = self.evaluate(init(shape, dtype))
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self.assertEqual(tensor_shape.as_shape(shape), t1.shape)
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self.assertEqual(tensor_shape.as_shape(shape), t2.shape)
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self.assertFalse(np.allclose(t1, t2, rtol=1e-15, atol=1e-15))
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def _range_test(self,
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init,
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shape,
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target_mean=None,
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target_std=None,
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target_max=None,
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target_min=None):
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output = self.evaluate(init(shape))
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self.assertEqual(output.shape, shape)
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lim = 3e-2
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if target_std is not None:
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self.assertGreater(lim, abs(output.std() - target_std))
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if target_mean is not None:
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self.assertGreater(lim, abs(output.mean() - target_mean))
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if target_max is not None:
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self.assertGreater(lim, abs(output.max() - target_max))
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if target_min is not None:
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self.assertGreater(lim, abs(output.min() - target_min))
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def _partition_test(self, init):
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full_shape = (4, 2)
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partition_shape = (2, 2)
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partition_offset = (0, 0)
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full_value = self.evaluate(init(full_shape, dtype=dtypes.float32))
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got = self.evaluate(
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init(
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full_shape,
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dtype=dtypes.float32,
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partition_shape=partition_shape,
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partition_offset=partition_offset))
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self.assertEqual(got.shape, partition_shape)
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self.assertAllClose(
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got, array_ops.slice(full_value, partition_offset, partition_shape))
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class ConstantInitializersTest(InitializersTest):
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@test_util.run_in_graph_and_eager_modes
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def testZeros(self):
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self._range_test(
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init_ops_v2.Zeros(), shape=(4, 5), target_mean=0., target_max=0.)
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@test_util.run_in_graph_and_eager_modes
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def testZerosPartition(self):
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init = init_ops_v2.Zeros()
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self._partition_test(init)
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@test_util.run_in_graph_and_eager_modes
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def testZerosInvalidKwargs(self):
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init = init_ops_v2.Zeros()
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with self.assertRaisesWithLiteralMatch(TypeError,
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r"Unknown keyword arguments: dtpye"):
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init((2, 2), dtpye=dtypes.float32)
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@test_util.run_in_graph_and_eager_modes
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def testOnes(self):
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self._range_test(
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init_ops_v2.Ones(), shape=(4, 5), target_mean=1., target_max=1.)
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@test_util.run_in_graph_and_eager_modes
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def testOnesPartition(self):
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init = init_ops_v2.Ones()
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self._partition_test(init)
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@test_util.run_in_graph_and_eager_modes
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def testConstantInt(self):
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self._range_test(
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init_ops_v2.Constant(2),
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shape=(5, 6, 4),
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target_mean=2,
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target_max=2,
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target_min=2)
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@test_util.run_in_graph_and_eager_modes
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def testConstantPartition(self):
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init = init_ops_v2.Constant([1, 2, 3, 4])
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with self.assertRaisesWithLiteralMatch(
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ValueError,
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r"Constant initializer doesn't support partition-related arguments"):
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init((4, 2), dtype=dtypes.float32, partition_shape=(2, 2))
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@test_util.run_in_graph_and_eager_modes
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def testConstantTuple(self):
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init = init_ops_v2.constant_initializer((10, 20, 30))
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tensor = init(shape=[3])
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self.assertAllEqual(self.evaluate(tensor), [10, 20, 30])
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self.assertEqual(tensor.shape, [3])
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@test_util.run_in_graph_and_eager_modes
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def testConstantInvalidValue(self):
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c = constant_op.constant([1.0, 2.0, 3.0])
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with self.assertRaisesRegex(TypeError,
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r"Invalid type for initial value: .*Tensor.*"):
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init_ops_v2.constant_initializer(c)
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v = variables.Variable([3.0, 2.0, 1.0])
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with self.assertRaisesRegex(
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TypeError, r"Invalid type for initial value: .*Variable.*"):
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init_ops_v2.constant_initializer(v)
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def _testNDimConstantInitializer(self, value, shape, expected):
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with test_util.use_gpu():
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init = init_ops_v2.constant_initializer(value)
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x = init(shape)
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actual = self.evaluate(array_ops.reshape(x, [-1]))
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self.assertEqual(len(actual), len(expected))
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for a, e in zip(actual, expected):
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self.assertEqual(a, e)
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@test_util.run_in_graph_and_eager_modes
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def testNDimConstantInitializer(self):
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value = [0, 1, 2, 3, 4, 5]
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shape = [2, 3]
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expected = list(value)
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self._testNDimConstantInitializer(value, shape, expected)
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self._testNDimConstantInitializer(np.asarray(value), shape, expected)
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self._testNDimConstantInitializer(
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np.asarray(value).reshape(tuple(shape)), shape, expected)
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def _testNDimConstantInitializerIncorrectNumberValues(self, value, shape):
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with test_util.use_gpu():
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init = init_ops_v2.constant_initializer(value)
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self.assertRaises(TypeError, init, shape=shape)
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@test_util.run_in_graph_and_eager_modes
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def testNDimConstantInitializerIncorrectNumberValues(self):
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value = [0, 1, 2, 3, 4, 5]
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for shape in [[2, 4], [2, 2]]:
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self._testNDimConstantInitializerIncorrectNumberValues(value, shape)
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self._testNDimConstantInitializerIncorrectNumberValues(
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np.asarray(value), shape)
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self._testNDimConstantInitializerIncorrectNumberValues(
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np.asarray(value).reshape(tuple([2, 3])), shape)
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class RandomUniformInitializerTest(InitializersTest):
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@test_util.run_in_graph_and_eager_modes
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def testRangeInitializer(self):
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shape = (20, 6, 7)
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self._range_test(
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init_ops_v2.RandomUniform(minval=-1, maxval=1, seed=124),
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shape,
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target_mean=0.,
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target_max=1,
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target_min=-1)
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@test_util.run_in_graph_and_eager_modes
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def testInitializerIdentical(self):
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self.skipTest("Doesn't work without the graphs")
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init1 = init_ops_v2.RandomUniform(0, 7, seed=1)
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init2 = init_ops_v2.RandomUniform(0, 7, seed=1)
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self._identical_test(init1, init2, True)
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@test_util.run_in_graph_and_eager_modes
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def testInitializerDifferent(self):
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init1 = init_ops_v2.RandomUniform(0, 7, seed=1)
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init2 = init_ops_v2.RandomUniform(0, 7, seed=2)
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self._identical_test(init1, init2, False)
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@test_util.run_in_graph_and_eager_modes
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def testDuplicatedInitializer(self):
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init = init_ops_v2.RandomUniform(0.0, 1.0)
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self._duplicated_test(init)
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@test_util.run_in_graph_and_eager_modes
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def testInitializePartition(self):
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init = init_ops_v2.RandomUniform(0, 7, seed=1)
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self._partition_test(init)
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class RandomNormalInitializerTest(InitializersTest):
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@test_util.run_in_graph_and_eager_modes
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def testRangeInitializer(self):
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self._range_test(
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init_ops_v2.RandomNormal(mean=0, stddev=1, seed=153),
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shape=(8, 12, 99),
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target_mean=0.,
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target_std=1)
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@test_util.run_in_graph_and_eager_modes
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def testInitializerIdentical(self):
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self.skipTest("Doesn't work without the graphs")
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init1 = init_ops_v2.RandomNormal(0, 7, seed=1)
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init2 = init_ops_v2.RandomNormal(0, 7, seed=1)
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self._identical_test(init1, init2, True)
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@test_util.run_in_graph_and_eager_modes
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def testInitializerDifferent(self):
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init1 = init_ops_v2.RandomNormal(0, 7, seed=1)
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init2 = init_ops_v2.RandomNormal(0, 7, seed=2)
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self._identical_test(init1, init2, False)
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@test_util.run_in_graph_and_eager_modes
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def testDuplicatedInitializer(self):
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init = init_ops_v2.RandomNormal(0.0, 1.0)
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self._duplicated_test(init)
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@test_util.run_in_graph_and_eager_modes
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def testInitializePartition(self):
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if test_util.is_xla_enabled():
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self.skipTest(
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"XLA ignores seeds for RandomNormal, skip xla-enabled test.")
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init = init_ops_v2.RandomNormal(0, 7, seed=1)
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self._partition_test(init)
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class TruncatedNormalInitializerTest(InitializersTest):
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@test_util.run_in_graph_and_eager_modes
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def testRangeInitializer(self):
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self._range_test(
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init_ops_v2.TruncatedNormal(mean=0, stddev=1, seed=126),
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shape=(12, 99, 7),
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target_mean=0.,
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target_max=2,
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target_min=-2)
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@test_util.run_in_graph_and_eager_modes
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def testInitializerIdentical(self):
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self.skipTest("Not seeming to work in Eager mode")
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init1 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
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init2 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
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self._identical_test(init1, init2, True)
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@test_util.run_in_graph_and_eager_modes
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def testInitializerDifferent(self):
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init1 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
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init2 = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=2)
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self._identical_test(init1, init2, False)
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@test_util.run_in_graph_and_eager_modes
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def testDuplicatedInitializer(self):
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init = init_ops_v2.TruncatedNormal(0.0, 1.0)
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self._duplicated_test(init)
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@test_util.run_in_graph_and_eager_modes
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def testInitializePartition(self):
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init = init_ops_v2.TruncatedNormal(0.0, 1.0, seed=1)
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self._partition_test(init)
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@test_util.run_in_graph_and_eager_modes
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def testInvalidDataType(self):
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init = init_ops_v2.TruncatedNormal(0.0, 1.0)
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with self.assertRaises(ValueError):
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init([1], dtype=dtypes.int32)
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class VarianceScalingInitializerTest(InitializersTest):
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@test_util.run_in_graph_and_eager_modes
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def testTruncatedNormalDistribution(self):
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shape = [100, 100]
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expect_mean = 0.
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expect_var = 1. / shape[0]
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init = init_ops_v2.VarianceScaling(distribution="truncated_normal")
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with test_util.use_gpu(), test.mock.patch.object(
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random_ops, "truncated_normal",
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wraps=random_ops.truncated_normal) as mock_truncated_normal:
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x = self.evaluate(init(shape))
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self.assertTrue(mock_truncated_normal.called)
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self.assertNear(np.mean(x), expect_mean, err=1e-2)
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self.assertNear(np.var(x), expect_var, err=1e-2)
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@test_util.run_in_graph_and_eager_modes
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def testNormalDistribution(self):
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shape = [100, 100]
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expect_mean = 0.
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expect_var = 1. / shape[0]
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init = init_ops_v2.VarianceScaling(distribution="truncated_normal")
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with test_util.use_gpu(), test.mock.patch.object(
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random_ops, "truncated_normal",
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wraps=random_ops.truncated_normal) as mock_truncated_normal:
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x = self.evaluate(init(shape))
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self.assertTrue(mock_truncated_normal.called)
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self.assertNear(np.mean(x), expect_mean, err=1e-2)
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self.assertNear(np.var(x), expect_var, err=1e-2)
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@test_util.run_in_graph_and_eager_modes
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def testUntruncatedNormalDistribution(self):
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shape = [100, 100]
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expect_mean = 0.
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expect_var = 1. / shape[0]
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init = init_ops_v2.VarianceScaling(distribution="untruncated_normal")
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with test_util.use_gpu(), test.mock.patch.object(
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random_ops, "random_normal",
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wraps=random_ops.random_normal) as mock_random_normal:
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x = self.evaluate(init(shape))
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self.assertTrue(mock_random_normal.called)
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self.assertNear(np.mean(x), expect_mean, err=1e-2)
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self.assertNear(np.var(x), expect_var, err=1e-2)
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@test_util.run_in_graph_and_eager_modes
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def testUniformDistribution(self):
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shape = [100, 100]
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expect_mean = 0.
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expect_var = 1. / shape[0]
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init = init_ops_v2.VarianceScaling(distribution="uniform")
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with test_util.use_gpu():
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x = self.evaluate(init(shape))
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self.assertNear(np.mean(x), expect_mean, err=1e-2)
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self.assertNear(np.var(x), expect_var, err=1e-2)
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@test_util.run_in_graph_and_eager_modes
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def testInitializePartition(self):
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partition_shape = (100, 100)
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shape = [1000, 100]
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expect_mean = 0.
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expect_var = 1. / shape[0]
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init = init_ops_v2.VarianceScaling(distribution="untruncated_normal")
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with test_util.use_gpu(), test.mock.patch.object(
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random_ops, "random_normal",
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wraps=random_ops.random_normal) as mock_random_normal:
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x = self.evaluate(init(shape, partition_shape=partition_shape))
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self.assertTrue(mock_random_normal.called)
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self.assertEqual(x.shape, partition_shape)
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self.assertNear(np.mean(x), expect_mean, err=1e-3)
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self.assertNear(np.var(x), expect_var, err=1e-3)
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class OrthogonalInitializerTest(InitializersTest):
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@test_util.run_in_graph_and_eager_modes
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def testRangeInitializer(self):
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self._range_test(
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init_ops_v2.Orthogonal(seed=123), shape=(20, 20), target_mean=0.)
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@test_util.run_in_graph_and_eager_modes
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def testInitializerIdentical(self):
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self.skipTest("Doesn't work without the graphs")
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init1 = init_ops_v2.Orthogonal(seed=1)
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init2 = init_ops_v2.Orthogonal(seed=1)
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self._identical_test(init1, init2, True, (10, 10))
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@test_util.run_in_graph_and_eager_modes
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def testInitializerDifferent(self):
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init1 = init_ops_v2.Orthogonal(seed=1)
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init2 = init_ops_v2.Orthogonal(seed=2)
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self._identical_test(init1, init2, False, (10, 10))
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@test_util.run_in_graph_and_eager_modes
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def testDuplicatedInitializer(self):
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init = init_ops_v2.Orthogonal()
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self._duplicated_test(init, (10, 10))
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@test_util.run_in_graph_and_eager_modes
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def testInvalidDataType(self):
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init = init_ops_v2.Orthogonal()
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self.assertRaises(ValueError, init, shape=(10, 10), dtype=dtypes.string)
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@test_util.run_in_graph_and_eager_modes
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def testInvalidShape(self):
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init = init_ops_v2.Orthogonal()
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with test_util.use_gpu():
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self.assertRaises(ValueError, init, shape=[5])
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@test_util.run_in_graph_and_eager_modes
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def testGain(self):
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self.skipTest("Doesn't work without the graphs")
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init1 = init_ops_v2.Orthogonal(seed=1)
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init2 = init_ops_v2.Orthogonal(gain=3.14, seed=1)
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with test_util.use_gpu():
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t1 = self.evaluate(init1(shape=(10, 10)))
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t2 = self.evaluate(init2(shape=(10, 10)))
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self.assertAllClose(t1, t2 / 3.14)
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@test_util.run_in_graph_and_eager_modes
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def testShapesValues(self):
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for shape in [(10, 10), (10, 9, 8), (100, 5, 5), (50, 40), (40, 50)]:
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init = init_ops_v2.Orthogonal()
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tol = 1e-5
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with test_util.use_gpu():
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# Check the shape
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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()
|