def testSuccessAfterError(self):
# Force an error on the TruncatedNormal kernel.
config.enable_op_determinism()
with self.assertRaisesRegex(
errors.InvalidArgumentError,
"When determinism is enabled, random ops must have a seed specified"
):
self.evaluate(
gen_random_ops.truncated_normal((1, ), dtypes.float32))
config.disable_op_determinism()
# Ensure the StdDev of the TruncatedNormal works as intended.
self.testStdDev()
def truncated_normal(shape,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a truncated normal distribution.
The values are drawn from a normal distribution with specified mean and
standard deviation, discarding and re-drawing any samples that are more than
two standard deviations from the mean.
Examples:
>>> tf.random.truncated_normal(shape=[2])
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>
>>> tf.random.truncated_normal(shape=[2], mean=3, stddev=1, dtype=tf.float32)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
mean: A 0-D Tensor or Python value of type `dtype`. The mean of the
truncated normal distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
of the normal distribution, before truncation.
dtype: The type of the output. Restricted to floating-point types:
`tf.half`, `tf.float`, `tf.double`, etc.
seed: A Python integer. Used to create a random seed for the distribution.
See `tf.random.set_seed` for more information.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random truncated normal values.
"""
with ops.name_scope(name, "truncated_normal",
[shape, mean, stddev]) as name:
shape_tensor = tensor_util.shape_tensor(shape)
mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev_tensor = ops.convert_to_tensor(stddev,
dtype=dtype,
name="stddev")
seed1, seed2 = random_seed.get_seed(seed)
rnd = gen_random_ops.truncated_normal(shape_tensor,
dtype,
seed=seed1,
seed2=seed2)
mul = rnd * stddev_tensor
value = math_ops.add(mul, mean_tensor, name=name)
tensor_util.maybe_set_static_shape(value, shape)
return value
def truncated_normal(shape,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a truncated normal distribution.
The generated values follow a normal distribution with specified mean and
standard deviation, except that values whose magnitude is more than 2 standard
deviations from the mean are dropped and re-picked.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
mean: A 0-D Tensor or Python value of type `dtype`. The mean of the
truncated normal distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
of the normal distribution, before truncation.
dtype: The type of the output.
seed: A Python integer. Used to create a random seed for the distribution.
See
`tf.random.set_seed`
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random truncated normal values.
"""
with ops.name_scope(name, "truncated_normal",
[shape, mean, stddev]) as name:
shape_tensor = tensor_util.shape_tensor(shape)
mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev_tensor = ops.convert_to_tensor(stddev,
dtype=dtype,
name="stddev")
seed1, seed2 = random_seed.get_seed(seed)
rnd = gen_random_ops.truncated_normal(shape_tensor,
dtype,
seed=seed1,
seed2=seed2)
mul = rnd * stddev_tensor
value = math_ops.add(mul, mean_tensor, name=name)
tensor_util.maybe_set_static_shape(value, shape)
return value
def truncated_normal(shape,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a truncated normal distribution.
The generated values follow a normal distribution with specified mean and
standard deviation, except that values whose magnitude is more than 2 standard
deviations from the mean are dropped and re-picked.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
mean: A 0-D Tensor or Python value of type `dtype`. The mean of the
truncated normal distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
of the normal distribution, before truncation.
dtype: The type of the output.
seed: A Python integer. Used to create a random seed for the distribution.
See
`tf.set_random_seed`
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random truncated normal values.
"""
with ops.name_scope(name, "truncated_normal", [shape, mean, stddev]) as name:
shape_tensor = _ShapeTensor(shape)
mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
seed1, seed2 = random_seed.get_seed(seed)
rnd = gen_random_ops.truncated_normal(
shape_tensor, dtype, seed=seed1, seed2=seed2)
mul = rnd * stddev_tensor
value = math_ops.add(mul, mean_tensor, name=name)
return value
def old_truncated_normal(dtype, seed1, seed2):
return gen_random_ops.truncated_normal(
shape, dtype=dtype, seed=seed1, seed2=seed2)