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"""Tests for tensorflow.ops.random_ops."""
import tensorflow.python.platform
import numpy as np
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.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.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()
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.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: RandomParameters on GPU is not supported.
for use_gpu in [False]:
for dt in 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.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()
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.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.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.float32, tf.float64:
sampler = self._Sampler(1000, -2., 8., 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.float32, tf.float64:
sampler = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu)
x = sampler()
y = sampler()
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.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()
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.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_uniform(shape, 0.0, 1.0,
dtype=tf.float32)
rnd2 = tf.random_uniform(shape, 0.0, 1.0,
dtype=tf.float32)
diff = (rnd2 - rnd1).eval()
self.assertTrue(np.linalg.norm(diff) > 0.1)
class RandomShapeTest(tf.test.TestCase):
def testRandomParameters(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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