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# Copyright 2017 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 random-number generation ops in the XLA JIT compiler."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops.distributions import special_math
from tensorflow.python.platform import googletest
class RandomOpsTest(xla_test.XLATestCase):
"""Test cases for random-number generating operators."""
def _random_types(self):
return set(self.numeric_types) - set(self.complex_types)
def _testRngIsNotConstant(self, rng, dtype):
# Tests that 'rng' does not always return the same value.
with self.test_session() as sess:
with self.test_scope():
x = rng(dtype)
# The random-number generator, if working correctly, should produce the
# same output multiple times with low probability.
y = sess.run(x)
z = sess.run(x)
w = sess.run(x)
# We use exact equality here. If the random-number generator is producing
# deterministic output, all three outputs will be bitwise identical.
self.assertTrue((not np.array_equal(y, z)) or
(not np.array_equal(z, w)) or (not np.array_equal(y, w)))
def testRandomUniformIsNotConstant(self):
def rng(dtype):
return random_ops.random_uniform(shape=[2], dtype=dtype, maxval=1000000)
for dtype in self._random_types():
self._testRngIsNotConstant(rng, dtype)
def testRandomNormalIsNotConstant(self):
def rng(dtype):
return random_ops.random_normal(shape=[2], dtype=dtype)
# TODO(b/34339814): implement inverse erf support for non-F32 types.
dtype = dtypes.float32
self._testRngIsNotConstant(rng, dtype)
def testRandomUniformIsInRange(self):
for dtype in self._random_types():
with self.test_session() as sess:
with self.test_scope():
x = random_ops.random_uniform(
shape=[1000], dtype=dtype, minval=-2, maxval=33)
y = sess.run(x)
self.assertTrue((y >= -2).sum() == 1000)
self.assertTrue((y < 33).sum() == 1000)
def testTruncatedNormalIsNotConstant(self):
def rng(dtype):
return random_ops.truncated_normal(shape=[2], dtype=dtype)
# TODO(b/34339814): implement inverse erf support for non-F32 types.
self._testRngIsNotConstant(rng, dtypes.float32)
def testTruncatedNormalIsInRange(self):
count = 10000000
# TODO(b/34339814): implement inverse erf support for non-F32 types.
for dtype in [dtypes.float32]:
with self.test_session() as sess:
with self.test_scope():
x = random_ops.truncated_normal(shape=[count], dtype=dtype, seed=42)
y = sess.run(x)
def normal_cdf(x):
return .5 * math.erfc(-x / math.sqrt(2))
def normal_pdf(x):
return math.exp(-(x**2) / 2.) / math.sqrt(2 * math.pi)
def probit(x, sess=sess):
return sess.run(special_math.ndtri(x))
a = -2.
b = 2.
mu = 0.
sigma = 1.
alpha = (a - mu) / sigma
beta = (b - mu) / sigma
z = normal_cdf(beta) - normal_cdf(alpha)
self.assertTrue((y >= a).sum() == count)
self.assertTrue((y <= b).sum() == count)
# For more information on these calculations, see:
# Burkardt, John. "The Truncated Normal Distribution".
# Department of Scientific Computing website. Florida State University.
expected_mean = mu + (normal_pdf(alpha) - normal_pdf(beta)) / z * sigma
actual_mean = np.mean(y)
self.assertAllClose(actual_mean, expected_mean, atol=2e-4)
expected_median = mu + probit(
(normal_cdf(alpha) + normal_cdf(beta)) / 2.) * sigma
actual_median = np.median(y)
self.assertAllClose(actual_median, expected_median, atol=8e-4)
expected_variance = sigma**2 * (1 + (
(alpha * normal_pdf(alpha) - beta * normal_pdf(beta)) / z) - (
(normal_pdf(alpha) - normal_pdf(beta)) / z)**2)
actual_variance = np.var(y)
self.assertAllClose(actual_variance, expected_variance, rtol=3e-4)
def testShuffle1d(self):
with self.test_session() as sess:
with self.test_scope():
x = math_ops.range(1 << 16)
shuffle = random_ops.random_shuffle(x)
result = sess.run(shuffle)
expected = range(1 << 16)
# Compare sets to avoid randomness behavior changes but make sure still
# have all the values.
self.assertAllEqual(set(result), set(expected))
def testShuffle2d(self):
with self.test_session() as sess:
with self.test_scope():
x = array_ops.diag(math_ops.range(20))
shuffle = random_ops.random_shuffle(x)
result = sess.run(shuffle)
expected = np.diag(range(20)).flatten()
# Compare sets to avoid randomness behavior changes but make sure still
# have all the values.
self.assertAllEqual(len(result.flatten()), len(expected))
self.assertAllEqual(set(result.flatten()), set(expected))
if __name__ == '__main__':
googletest.main()
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