def testErrorNotThrownWithSeed(self):
random_ops.random_normal((1,), seed=0)
random_seed.set_random_seed(0)
random_ops.random_normal((1,))
self.evaluate(gen_random_ops.random_standard_normal((1,), dtypes.float32,
seed=1))
self.evaluate(gen_random_ops.random_standard_normal((1,), dtypes.float32,
seed2=1))
def random_normal(shape,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a normal distribution.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
mean: A Tensor or Python value of type `dtype`, broadcastable with `stddev`.
The mean of the normal distribution.
stddev: A Tensor or Python value of type `dtype`, broadcastable with `mean`.
The standard deviation of the normal distribution.
dtype: The type of the output.
seed: A Python integer. Used to create a random seed for the distribution.
See
`tf.compat.v1.set_random_seed`
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random normal values.
"""
with ops.name_scope(name, "random_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.random_standard_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 random_normal(shape,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a normal distribution.
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 normal
distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
of the normal distribution.
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 normal values.
"""
with ops.name_scope(name, "random_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.random_standard_normal(
shape_tensor, dtype, seed=seed1, seed2=seed2)
mul = rnd * stddev_tensor
value = math_ops.add(mul, mean_tensor, name=name)
return value
def random_normal(shape,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a normal distribution.
Example that generates a new set of random values every time:
>>> tf.random.set_seed(5);
>>> tf.random.normal([4], 0, 1, tf.float32)
<tf.Tensor: shape=(4,), dtype=float32, numpy=..., dtype=float32)>
Example that outputs a reproducible result:
>>> tf.random.set_seed(5);
>>> tf.random.normal([2,2], 0, 1, tf.float32, seed=1)
<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[-1.3768897 , -0.01258316],
[-0.169515 , 1.0824056 ]], dtype=float32)>
In this case, we are setting both the global and operation-level seed to
ensure this result is reproducible. See `tf.random.set_seed` for more
information.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
mean: A Tensor or Python value of type `dtype`, broadcastable with `stddev`.
The mean of the normal distribution.
stddev: A Tensor or Python value of type `dtype`, broadcastable with `mean`.
The standard deviation of the normal distribution.
dtype: The float type of the output: `float16`, `bfloat16`, `float32`,
`float64`. Defaults to `float32`.
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 normal values.
"""
with ops.name_scope(name, "random_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.random_standard_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 testDeterministicOpsErrors(self):
with self.assertRaisesRegex(
RuntimeError,
"Random ops require a seed to be set when determinism is enabled."):
random_ops.random_normal((1,))
with self.assertRaisesRegex(
RuntimeError,
"Random ops require a seed to be set when determinism is enabled."):
random_ops.truncated_normal((1,))
with self.assertRaisesRegex(
RuntimeError,
"Random ops require a seed to be set when determinism is enabled."):
random_ops.random_uniform((1,))
with self.assertRaisesRegex(
errors.InvalidArgumentError,
"When determinism is enabled, random ops must have a seed specified"):
self.evaluate(gen_random_ops.random_standard_normal((1,), dtypes.float32))
def old_normal(dtype, seed1, seed2):
return gen_random_ops.random_standard_normal(
shape, dtype=dtype, seed=seed1, seed2=seed2)
def old():
with ops.device(test_util.gpu_device_name()):
return gen_random_ops.random_standard_normal(shape,
dtype=dtype,
seed=seed1,
seed2=seed2)
def old():
with ops.device("/device:CPU:0"):
return gen_random_ops.random_standard_normal(shape,
dtype=dtype,
seed=seed1,
seed2=seed2)
