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# Copyright 2015 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."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
class RandomNormalTest(tf.test.TestCase):
def _Sampler(self, num, mu, sigma, dtype, use_gpu, seed=None):
def func():
with self.test_session(use_gpu=use_gpu, graph=tf.Graph()) as sess:
rng = tf.random_normal(
[num], mean=mu, stddev=sigma, dtype=dtype, seed=seed)
ret = np.empty([10, num])
for i in xrange(10):
ret[i, :] = sess.run(rng)
return ret
return func
# 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 tf.float16, tf.float32, tf.float64:
sampler = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu)
x = sampler()
y = sampler()
# Number of different samples.
count = (x == y).sum()
if count >= 10:
print("x = ", x)
print("y = ", y)
print("count = ", count)
self.assertTrue(count < 10)
# Checks that the CPU and GPU implementation returns the same results,
# given the same random seed
def testCPUGPUMatch(self):
for dt in tf.float16, tf.float32, tf.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 == tf.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 tf.float16, tf.float32, tf.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):
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
shape = [2, 3, 4]
rnd1 = tf.random_normal(shape, 0.0, 1.0, tf.float32)
rnd2 = tf.random_normal(shape, 0.0, 1.0, tf.float32)
diff = rnd2 - rnd1
self.assertTrue(np.linalg.norm(diff.eval()) > 0.1)
class TruncatedNormalTest(tf.test.TestCase):
def _Sampler(self, num, mu, sigma, dtype, use_gpu, seed=None):
def func():
with self.test_session(use_gpu=use_gpu, graph=tf.Graph()) as sess:
rng = tf.truncated_normal(
[num], mean=mu, stddev=sigma, dtype=dtype, seed=seed)
ret = np.empty([10, num])
for i in xrange(10):
ret[i, :] = sess.run(rng)
return ret
return func
# 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):
# NOTE: TruncatedNormal on GPU is not supported.
for use_gpu in [False]:
for dt in tf.float16, tf.float32, tf.float64:
sampler = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu)
x = sampler()
y = sampler()
# Number of different samples.
count = (x == y).sum()
if count >= 10:
print("x = ", x)
print("y = ", y)
print("count = ", count)
self.assertTrue(count < 10)
# Checks that the CPU and GPU implementation returns the same results,
# given the same random seed
def testCPUGPUMatch(self):
for dt in tf.float16, tf.float32, tf.float64:
results = {}
for use_gpu in [False, True]:
# We need a particular larger number of samples to test multiple rounds
# on GPU
sampler = self._Sampler(1000000, 0.0, 1.0, dt, use_gpu=use_gpu,
seed=12345)
results[use_gpu] = sampler()
if dt == tf.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 tf.float16, tf.float32, tf.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())
# The effective standard deviation of truncated normal is 85% of the
# requested one.
def testStdDev(self):
for use_gpu in [False, True]:
for dt in tf.float16, tf.float32, tf.float64:
stddev = 3.0
sampler = self._Sampler(100000, 0.0, stddev, dt, use_gpu=use_gpu)
x = sampler()
print("std(x)", np.std(x), abs(np.std(x) / stddev - 0.85))
self.assertTrue(abs(np.std(x) / stddev - 0.85) < 0.04)
def testNoCSE(self):
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
shape = [2, 3, 4]
rnd1 = tf.truncated_normal(shape, 0.0, 1.0, tf.float32)
rnd2 = tf.truncated_normal(shape, 0.0, 1.0, tf.float32)
diff = rnd2 - rnd1
self.assertTrue(np.linalg.norm(diff.eval()) > 0.1)
class RandomUniformTest(tf.test.TestCase):
def _Sampler(self, num, minv, maxv, dtype, use_gpu, seed=None):
def func():
with self.test_session(use_gpu=use_gpu, graph=tf.Graph()) as sess:
rng = tf.random_uniform(
[num], minval=minv, maxval=maxv, dtype=dtype, seed=seed)
ret = np.empty([10, num])
for i in xrange(10):
ret[i, :] = sess.run(rng)
return ret
return func
def testRange(self):
for use_gpu in False, True:
for dt in tf.float16, tf.float32, tf.float64, tf.int32, tf.int64:
sampler = self._Sampler(1000, minv=-2, maxv=8, dtype=dt,
use_gpu=use_gpu)
x = sampler()
self.assertTrue(-2 <= np.min(x))
self.assertTrue(np.max(x) < 8)
# 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 tf.float16, tf.float32, tf.float64, tf.int32, tf.int64:
maxv = 1.0 if dt.is_floating else 1 << 30
sampler = self._Sampler(1000, minv=0, maxv=maxv, dtype=dt,
use_gpu=use_gpu)
x = sampler()
y = sampler()
count = (x == y).sum()
count_limit = 50 if dt == tf.float16 else 10
if count >= count_limit:
print("x = ", x)
print("y = ", y)
print("count = ", count)
self.assertTrue(count < count_limit)
# Check that uniform ints actually follow a uniform distribution.
def testUniformInts(self):
minv = -2
maxv = 15
n = 100000
p = 1 / (maxv - minv)
# The counts should follow an (n, p) binomial distribution.
mean = p * n
std = np.sqrt(n * p * (1 - p))
for use_gpu in False, True:
for dt in tf.int32, tf.int64:
# Use a fixed seed here to make the test deterministic.
# Without the fixed seed, the 5 * std bound will (very rarely) fail.
sampler = self._Sampler(n // 10, minv=minv, maxv=maxv, dtype=dt,
use_gpu=use_gpu, seed=17)
x = sampler().ravel()
self.assertEqual(x.shape, (n,))
counts, _ = np.histogram(x, bins=maxv - minv)
self.assertEqual(counts.shape, (maxv - minv,))
self.assertEqual(counts.sum(), n)
error = np.abs(counts - mean)
self.assertLess(error.max(), 5 * std)
# Checks that the CPU and GPU implementation returns the same results,
# given the same random seed
def testCPUGPUMatch(self):
for dt in tf.float16, tf.float32, tf.float64, tf.int32, tf.int64:
maxv = 1.0 if dt.is_floating else 17
results = {}
for use_gpu in False, True:
sampler = self._Sampler(1000, minv=0, maxv=maxv, dtype=dt,
use_gpu=use_gpu, seed=12345)
results[use_gpu] = sampler()
self.assertAllEqual(results[False], results[True])
def testSeed(self):
for use_gpu in False, True:
for dt in tf.float16, tf.float32, tf.float64, tf.int32, tf.int64:
for seed in [345, 2**100, -2**100]:
sx = self._Sampler(1000, 0, 17, dtype=dt, use_gpu=use_gpu, seed=seed)
sy = self._Sampler(1000, 0, 17, dtype=dt, use_gpu=use_gpu, seed=seed)
self.assertAllEqual(sx(), sy())
def testNoCSE(self):
shape = [2, 3, 4]
for use_gpu in False, True:
for dtype in tf.float16, tf.float32, tf.int32:
with self.test_session(use_gpu=use_gpu):
rnd1 = tf.random_uniform(shape, 0, 17, dtype=dtype)
rnd2 = tf.random_uniform(shape, 0, 17, dtype=dtype)
diff = (rnd2 - rnd1).eval()
self.assertTrue(np.linalg.norm(diff) > 0.1)
class RandomShapeTest(tf.test.TestCase):
def testTruncatedNormal(self):
# Fully known shape.
rnd1 = tf.truncated_normal([1, 2, 3])
self.assertEqual([1, 2, 3], rnd1.get_shape())
# Partially known shape.
rnd2 = tf.truncated_normal(tf.placeholder(tf.int32, shape=(3,)))
self.assertEqual([None, None, None], rnd2.get_shape().as_list())
# Unknown shape.
rnd3 = tf.truncated_normal(tf.placeholder(tf.int32))
self.assertIs(None, rnd3.get_shape().ndims)
def testRandomNormal(self):
# Fully known shape.
rnd1 = tf.random_normal([1, 2, 3])
self.assertEqual([1, 2, 3], rnd1.get_shape())
# Partially known shape.
rnd2 = tf.random_normal(tf.placeholder(tf.int32, shape=(3,)))
self.assertEqual([None, None, None], rnd2.get_shape().as_list())
# Unknown shape.
rnd3 = tf.random_normal(tf.placeholder(tf.int32))
self.assertIs(None, rnd3.get_shape().ndims)
def testRandomUniform(self):
# Fully known shape.
rnd1 = tf.random_uniform([1, 2, 3])
self.assertEqual([1, 2, 3], rnd1.get_shape())
# Partially known shape.
rnd2 = tf.random_uniform(
tf.placeholder(tf.int32, shape=(3,)))
self.assertEqual([None, None, None], rnd2.get_shape().as_list())
# Unknown shape.
rnd3 = tf.random_uniform(tf.placeholder(tf.int32))
self.assertIs(None, rnd3.get_shape().ndims)
if __name__ == "__main__":
tf.test.main()
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