def stateless_random_normal(shape,
seed,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
name=None):
"""Outputs deterministic pseudorandom values from a normal distribution.
This is a stateless version of `tf.random.normal`: if run twice with the
same seeds, it will produce the same pseudorandom numbers. The output is
consistent across multiple runs on the same hardware (and between CPU
and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] integer Tensor of seeds to the random number generator.
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.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random normal values.
"""
with ops.name_scope(name, "stateless_random_normal",
[shape, seed, mean, stddev]) as name:
shape = random_ops._ShapeTensor(shape) # pylint: disable=protected-access
mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
rnd = gen_stateless_random_ops.stateless_random_normal(shape, seed, dtype)
return math_ops.add(rnd * stddev, mean, name=name)
def stateless_random_normal(shape,
seed,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
name=None):
"""Outputs deterministic pseudorandom values from a normal distribution.
This is a stateless version of `tf.random.normal`: if run twice with the
same seeds, it will produce the same pseudorandom numbers. The output is
consistent across multiple runs on the same hardware (and between CPU
and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] integer Tensor of seeds to the random number generator.
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.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random normal values.
"""
with ops.name_scope(name, "stateless_random_normal",
[shape, seed, mean, stddev]) as name:
shape = random_ops._ShapeTensor(shape) # pylint: disable=protected-access
mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
rnd = gen_stateless_random_ops.stateless_random_normal(shape, seed, dtype)
return math_ops.add(rnd * stddev, mean, name=name)
def rtt_random_uniform(shape,
minval=0,
maxval=None,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a uniform distribution."""
dtype = dtypes.as_dtype(dtype)
if dtype not in (dtypes.float16, dtypes.bfloat16, dtypes.float32,
dtypes.float64, dtypes.int32, dtypes.int64,
dtypes.string):
raise ValueError("Invalid dtype %r" % dtype)
bk_dtype = dtype
if (dtype == dtypes.string):
dtype = dtypes.float32
if maxval is None:
if dtype.is_integer:
raise ValueError("Must specify maxval for integer dtype %r" %
dtype)
maxval = 1
with ops.name_scope(name, "random_uniform",
[shape, minval, maxval]) as name:
shape = random_ops._ShapeTensor(shape)
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
seed1, seed2 = random_seed.get_seed(seed)
if dtype.is_integer:
rv = gen_random_ops.random_uniform_int(shape,
minval,
maxval,
seed=seed1,
seed2=seed2,
name=name)
else:
rnd = gen_random_ops.random_uniform(shape,
dtypes.float32,
seed=seed1,
seed2=seed2)
rv = math_ops.add(rnd * (maxval - minval), minval, name=name)
if (bk_dtype == dtypes.string):
return tf.as_string(rv)
else:
return rv
def stateless_random_uniform(shape,
seed,
minval=0,
maxval=None,
dtype=dtypes.float32,
name=None):
"""Outputs deterministic pseudorandom values from a uniform distribution.
This is a stateless version of `tf.random.uniform`: if run twice with the
same seeds, it will produce the same pseudorandom numbers. The output is
consistent across multiple runs on the same hardware (and between CPU
and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded.
For floats, the default range is `[0, 1)`. For ints, at least `maxval` must
be specified explicitly.
In the integer case, the random integers are slightly biased unless
`maxval - minval` is an exact power of two. The bias is small for values of
`maxval - minval` significantly smaller than the range of the output (either
`2**32` or `2**64`).
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] integer Tensor of seeds to the random number generator.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
range of random values to generate. Defaults to 0.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on the
range of random values to generate. Defaults to 1 if `dtype` is floating
point.
dtype: The type of the output: `float16`, `float32`, `float64`, `int32`, or
`int64`.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and `maxval` is not specified.
"""
dtype = dtypes.as_dtype(dtype)
if dtype not in (dtypes.float16, dtypes.bfloat16, dtypes.float32,
dtypes.float64, dtypes.int32, dtypes.int64):
raise ValueError("Invalid dtype %r" % dtype)
if maxval is None:
if dtype.is_integer:
raise ValueError("Must specify maxval for integer dtype %r" % dtype)
maxval = 1
with ops.name_scope(name, "stateless_random_uniform",
[shape, seed, minval, maxval]) as name:
shape = random_ops._ShapeTensor(shape) # pylint: disable=protected-access
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
if dtype.is_integer:
return gen_stateless_random_ops.stateless_random_uniform_int(
shape, seed=seed, minval=minval, maxval=maxval, name=name)
else:
rnd = gen_stateless_random_ops.stateless_random_uniform(
shape, seed=seed, dtype=dtype)
return math_ops.add(rnd * (maxval - minval), minval, name=name)
def stateless_random_uniform(shape,
seed,
minval=0,
maxval=None,
dtype=dtypes.float32,
name=None):
"""Outputs deterministic pseudorandom values from a uniform distribution.
This is a stateless version of `tf.random.uniform`: if run twice with the
same seeds, it will produce the same pseudorandom numbers. The output is
consistent across multiple runs on the same hardware (and between CPU
and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded.
For floats, the default range is `[0, 1)`. For ints, at least `maxval` must
be specified explicitly.
In the integer case, the random integers are slightly biased unless
`maxval - minval` is an exact power of two. The bias is small for values of
`maxval - minval` significantly smaller than the range of the output (either
`2**32` or `2**64`).
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] integer Tensor of seeds to the random number generator.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
range of random values to generate. Defaults to 0.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on the
range of random values to generate. Defaults to 1 if `dtype` is floating
point.
dtype: The type of the output: `float16`, `float32`, `float64`, `int32`, or
`int64`.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and `maxval` is not specified.
"""
dtype = dtypes.as_dtype(dtype)
if dtype not in (dtypes.float16, dtypes.bfloat16, dtypes.float32,
dtypes.float64, dtypes.int32, dtypes.int64):
raise ValueError("Invalid dtype %r" % dtype)
if maxval is None:
if dtype.is_integer:
raise ValueError("Must specify maxval for integer dtype %r" % dtype)
maxval = 1
with ops.name_scope(name, "stateless_random_uniform",
[shape, seed, minval, maxval]) as name:
shape = random_ops._ShapeTensor(shape) # pylint: disable=protected-access
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
if dtype.is_integer:
return gen_stateless_random_ops.stateless_random_uniform_int(
shape, seed=seed, minval=minval, maxval=maxval, name=name)
else:
rnd = gen_stateless_random_ops.stateless_random_uniform(
shape, seed=seed, dtype=dtype)
return math_ops.add(rnd * (maxval - minval), minval, name=name)