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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.
# ==============================================================================
"""Tests for tensorflow.ops.random_ops.random_gamma."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging
class RandomGammaTest(test.TestCase):
"""This is a medium test due to the moments computation taking some time."""
def setUp(self):
np.random.seed(137)
random_seed.set_random_seed(137)
def _Sampler(self, num, alpha, beta, dtype, use_gpu, seed=None):
def func():
with self.test_session(use_gpu=use_gpu, graph=ops.Graph()) as sess:
rng = random_ops.random_gamma(
[num], alpha, beta=beta, dtype=dtype, seed=seed)
ret = np.empty([10, num])
for i in xrange(10):
ret[i, :] = sess.run(rng)
return ret
return func
def testMomentsFloat32(self):
self._testMoments(dtypes.float32)
def testMomentsFloat64(self):
self._testMoments(dtypes.float64)
def _testMoments(self, dt):
try:
from scipy import stats # pylint: disable=g-import-not-at-top
except ImportError as e:
tf_logging.warn("Cannot test moments: %s" % e)
return
# Check the given array of samples matches the given theoretical moment
# function at different orders. The test is considered passing if the
# z-tests of all statistical moments are all below z_limit.
# Parameters:
# max_moments: the largest moments of the distribution to be tested
# stride: the distance between samples to check for statistical properties
# 0 means the n-th moment of each sample
# any other strides tests for spatial correlation between samples;
# z_limit: the maximum z-test we would consider the test to pass;
# The moments test is a z-value test. This is the largest z-value
# we want to tolerate. Since the z-test approximates a unit normal
# distribution, it should almost definitely never exceed 6.
z_limit = 6.0
for stride in 0, 1, 4, 17:
alphas = [0.2, 1.0, 3.0]
if dt == dtypes.float64:
alphas = [0.01] + alphas
for alpha in alphas:
for scale in 9, 17:
# Gamma moments only defined for values less than the scale param.
max_moment = min(6, scale // 2)
sampler = self._Sampler(
20000, alpha, 1 / scale, dt, use_gpu=False, seed=12345)
moments = [0] * (max_moment + 1)
moments_sample_count = [0] * (max_moment + 1)
x = np.array(sampler().flat) # sampler does 10x samples
for k in range(len(x)):
moment = 1.
for i in range(max_moment + 1):
index = k + i * stride
if index >= len(x):
break
moments[i] += moment
moments_sample_count[i] += 1
moment *= x[index]
for i in range(max_moment + 1):
moments[i] /= moments_sample_count[i]
for i in range(1, max_moment + 1):
g = stats.gamma(alpha, scale=scale)
if stride == 0:
moments_i_mean = g.moment(i)
moments_i_squared = g.moment(2 * i)
else:
moments_i_mean = pow(g.moment(1), i)
moments_i_squared = pow(g.moment(2), i)
# Calculate moment variance safely:
# This is just
# (moments_i_squared - moments_i_mean**2) / moments_sample_count[i]
normalized_moments_i_var = (
moments_i_mean / moments_sample_count[i] *
(moments_i_squared / moments_i_mean - moments_i_mean))
# Assume every operation has a small numerical error.
# It takes i multiplications to calculate one i-th moment.
error_per_moment = i * np.finfo(dt.as_numpy_dtype).eps
total_variance = (normalized_moments_i_var + error_per_moment)
tiny = np.finfo(dt.as_numpy_dtype).tiny
self.assertGreaterEqual(total_variance, 0)
if total_variance < tiny:
total_variance = tiny
# z_test is approximately a unit normal distribution.
z_test = abs(
(moments[i] - moments_i_mean) / math.sqrt(total_variance))
self.assertLess(z_test, z_limit)
def _testZeroDensity(self, alpha):
"""Zero isn't in the support of the gamma distribution.
But quantized floating point math has its limits.
TODO(bjp): Implement log-gamma sampler for small-shape distributions.
Args:
alpha: float shape value to test
"""
try:
from scipy import stats # pylint: disable=g-import-not-at-top
except ImportError as e:
tf_logging.warn("Cannot test zero density proportions: %s" % e)
return
allowable_zeros = {
dtypes.float16: stats.gamma(alpha).cdf(np.finfo(np.float16).tiny),
dtypes.float32: stats.gamma(alpha).cdf(np.finfo(np.float32).tiny),
dtypes.float64: stats.gamma(alpha).cdf(np.finfo(np.float64).tiny)
}
failures = []
for use_gpu in [False, True]:
for dt in dtypes.float16, dtypes.float32, dtypes.float64:
sampler = self._Sampler(
10000, alpha, 1.0, dt, use_gpu=use_gpu, seed=12345)
x = sampler()
allowable = allowable_zeros[dt] * x.size
allowable = allowable * 2 if allowable < 10 else allowable * 1.05
if np.sum(x <= 0) > allowable:
failures += [(use_gpu, dt)]
self.assertEqual([], failures)
def testNonZeroSmallShape(self):
self._testZeroDensity(0.01)
def testNonZeroSmallishShape(self):
self._testZeroDensity(0.35)
# Asserts that different trials (1000 samples per trial) is unlikely
# to see the same sequence of values. Will catch buggy
# implementations which uses the same random number seed.
def testDistinct(self):
for use_gpu in [False, True]:
for dt in dtypes.float16, dtypes.float32, dtypes.float64:
sampler = self._Sampler(1000, 2.0, 1.0, dt, use_gpu=use_gpu)
x = sampler()
y = sampler()
# Number of different samples.
count = (x == y).sum()
count_limit = 20 if dt == dtypes.float16 else 10
if count >= count_limit:
print(use_gpu, dt)
print("x = ", x)
print("y = ", y)
print("count = ", count)
self.assertLess(count, count_limit)
# Checks that the CPU and GPU implementation returns the same results,
# given the same random seed
def testCPUGPUMatch(self):
for dt in dtypes.float16, dtypes.float32, dtypes.float64:
results = {}
for use_gpu in [False, True]:
sampler = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu, seed=12345)
results[use_gpu] = sampler()
if dt == dtypes.float16:
self.assertAllClose(results[False], results[True], rtol=1e-3, atol=1e-3)
else:
self.assertAllClose(results[False], results[True], rtol=1e-6, atol=1e-6)
def testSeed(self):
for use_gpu in [False, True]:
for dt in dtypes.float16, dtypes.float32, dtypes.float64:
sx = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu, seed=345)
sy = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu, seed=345)
self.assertAllEqual(sx(), sy())
def testNoCSE(self):
"""CSE = constant subexpression eliminator.
SetIsStateful() should prevent two identical random ops from getting
merged.
"""
for dtype in dtypes.float16, dtypes.float32, dtypes.float64:
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
rnd1 = random_ops.random_gamma([24], 2.0, dtype=dtype)
rnd2 = random_ops.random_gamma([24], 2.0, dtype=dtype)
diff = rnd2 - rnd1
self.assertGreater(np.linalg.norm(diff.eval()), 0.1)
def testShape(self):
# Fully known shape.
rnd = random_ops.random_gamma([150], 2.0)
self.assertEqual([150], rnd.get_shape().as_list())
rnd = random_ops.random_gamma([150], 2.0, beta=[3.0, 4.0])
self.assertEqual([150, 2], rnd.get_shape().as_list())
rnd = random_ops.random_gamma([150], array_ops.ones([1, 2, 3]))
self.assertEqual([150, 1, 2, 3], rnd.get_shape().as_list())
rnd = random_ops.random_gamma([20, 30], array_ops.ones([1, 2, 3]))
self.assertEqual([20, 30, 1, 2, 3], rnd.get_shape().as_list())
rnd = random_ops.random_gamma(
[123], array_ops.placeholder(
dtypes.float32, shape=(2,)))
self.assertEqual([123, 2], rnd.get_shape().as_list())
# Partially known shape.
rnd = random_ops.random_gamma(
array_ops.placeholder(
dtypes.int32, shape=(1,)), array_ops.ones([7, 3]))
self.assertEqual([None, 7, 3], rnd.get_shape().as_list())
rnd = random_ops.random_gamma(
array_ops.placeholder(
dtypes.int32, shape=(3,)), array_ops.ones([9, 6]))
self.assertEqual([None, None, None, 9, 6], rnd.get_shape().as_list())
# Unknown shape.
rnd = random_ops.random_gamma(
array_ops.placeholder(dtypes.int32),
array_ops.placeholder(dtypes.float32))
self.assertIs(None, rnd.get_shape().ndims)
rnd = random_ops.random_gamma([50], array_ops.placeholder(dtypes.float32))
self.assertIs(None, rnd.get_shape().ndims)
def testPositive(self):
n = int(10e3)
for dt in [dtypes.float16, dtypes.float32, dtypes.float64]:
with self.cached_session():
x = random_ops.random_gamma(shape=[n], alpha=0.001, dtype=dt, seed=0)
self.assertEqual(0, math_ops.reduce_sum(math_ops.cast(
math_ops.less_equal(x, 0.), dtype=dtypes.int64)).eval())
if __name__ == "__main__":
test.main()
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