def _testParameterizedTruncatedNormal(self,
means,
stddevs,
minvals,
maxvals,
variance_rtol=None):
for dtype in self._random_types():
with self.session() as sess, self.test_scope():
seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
n = int(10e7)
x = stateless.stateless_parameterized_truncated_normal(
shape=[n],
seed=seed_t,
means=means,
stddevs=stddevs,
minvals=minvals,
maxvals=maxvals)
y = sess.run(x, {seed_t: [0x12345678, 0xabcdef1]})
if variance_rtol is None:
variance_rtol = 6e-3 if dtype == dtypes.bfloat16 else 1e-3
random_test_util.test_truncated_normal(
self.assertEqual,
self.assertAllClose,
n,
y,
means=means,
stddevs=stddevs,
minvals=minvals,
maxvals=maxvals,
mean_atol=1e-3,
median_atol=1e-3,
variance_rtol=variance_rtol)
def testTruncatedNormal(self):
for dtype in self._floats:
gen = random.Generator(seed=123)
n = 10000000
y = gen.truncated_normal(shape=[n], dtype=dtype).numpy()
random_test_util.test_truncated_normal(
self.assertEqual, self.assertAllClose, dtype, n, y)
def testTruncatedNormal(self):
for dtype in self._floats:
gen = random.Generator(seed=123)
n = 10000000
y = gen.truncated_normal(shape=[n], dtype=dtype).numpy()
random_test_util.test_truncated_normal(self.assertEqual,
self.assertAllClose, dtype,
n, y)
def testTruncatedNormal(self, alg):
with ops.device(xla_device_name()):
for dtype in self._floats:
gen = random.Generator.from_seed(seed=123, alg=alg)
n = 10000000
y = gen.truncated_normal(shape=[n], dtype=dtype).numpy()
random_test_util.test_truncated_normal(
self.assertEqual, self.assertAllClose, dtype, n, y)
def testTruncatedNormal(self):
for dtype in self._random_types():
with self.cached_session() as sess, self.test_scope():
seed_t = array_ops.placeholder(dtypes.int32, shape=[2])
n = 10000000
x = stateless.stateless_truncated_normal(
shape=[n], seed=seed_t, dtype=dtype)
y = sess.run(x, {seed_t: [0x12345678, 0xabcdef12]})
random_test_util.test_truncated_normal(
self.assertEqual, self.assertAllClose, dtype, n, y)
def testTruncatedNormal(self, alg, dtype):
self.check_dtype(dtype)
with ops.device(xla_device_name()):
gen = random.Generator.from_seed(seed=123, alg=alg)
n = 100000
y = gen.truncated_normal(shape=[n], dtype=dtype).numpy()
random_test_util.test_truncated_normal(
self.assertEqual, self.assertAllClose, n, y,
mean_atol=2e-3, median_atol=4e-3,
variance_rtol=1e-2 if dtype == dtypes.bfloat16 else 5e-3)
