def _uniform(self, shape, dtype):
key, counter = self._prepare_key_counter(shape)
return gen_stateless_random_ops_v2.stateless_random_uniform_v2(
shape=shape,
key=key,
counter=counter,
dtype=dtype,
alg=self.algorithm)
def _uniform(self, shape, dtype):
if compat.forward_compatible(2020, 10, 25):
key, counter = self._prepare_key_counter(shape)
return gen_stateless_random_ops_v2.stateless_random_uniform_v2(
shape=shape,
key=key,
counter=counter,
dtype=dtype,
alg=self.algorithm)
return gen_stateful_random_ops.stateful_uniform(
self.state.handle, self.algorithm, shape=shape, dtype=dtype)
def stateless_random_uniform(shape,
seed,
minval=0,
maxval=None,
dtype=dtypes.float32,
name=None,
alg="auto_select"):
"""Outputs deterministic pseudorandom values from a uniform distribution.
This is a stateless version of `tf.random.uniform`: if run twice with the
same seeds and shapes, 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`).
For full-range (i.e. inclusive of both max and min) random integers, pass
`minval=None` and `maxval=None` with an integer `dtype`. For an integer dtype
either both `minval` and `maxval` must be `None` or neither may be `None`. For
example:
```python
ints = tf.random.stateless_uniform(
[10], seed=(2, 3), minval=None, maxval=None, dtype=tf.int32)
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
minval: A Tensor or Python value of type `dtype`, broadcastable with
`shape` (for integer types, broadcasting is not supported, so it needs to
be a scalar). The lower bound on the range of random values to
generate. Pass `None` for full-range integers. Defaults to 0.
maxval: A Tensor or Python value of type `dtype`, broadcastable with
`shape` (for integer types, broadcasting is not supported, so it needs to
be a scalar). The upper bound on the range of random values to generate.
Defaults to 1 if `dtype` is floating point. Pass `None` for full-range
integers.
dtype: The type of the output: `float16`, `bfloat16`, `float32`, `float64`,
`int32`, or `int64`. For unbounded uniform ints (`minval`, `maxval` both
`None`), `uint32` and `uint64` may be used. Defaults to `float32`.
name: A name for the operation (optional).
alg: The RNG algorithm used to generate the random numbers. Valid
choices are `"philox"` for [the Philox
algorithm](https://www.thesalmons.org/john/random123/papers/random123sc11.pdf),
`"threefry"` for [the ThreeFry
algorithm](https://www.thesalmons.org/john/random123/papers/random123sc11.pdf),
and `"auto_select"` (default) for the system to automatically
select an algorithm based the device type. Values of
`tf.random.Algorithm` can also be used. Note that with
`"auto_select"`, the outputs of this function may change when
it is running on a different device.
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and only one of `minval` or `maxval` is
specified.
"""
dtype = dtypes.as_dtype(dtype)
accepted_dtypes = (dtypes.float16, dtypes.bfloat16, dtypes.float32,
dtypes.float64, dtypes.int32, dtypes.int64,
dtypes.uint32, dtypes.uint64)
if dtype not in accepted_dtypes:
raise ValueError(
f"Argument `dtype` got invalid value {dtype}. Accepted dtypes are "
f"{accepted_dtypes}.")
if dtype.is_integer:
if (minval is None) != (maxval is None):
raise ValueError(
f"For integer `dtype` argument {dtype}, argument `minval` and "
f"`maxval` must be both None or not None. Got `minval`={minval} and "
f"`maxval`={maxval}.")
if minval is not None and dtype in (dtypes.uint32, dtypes.uint64):
raise ValueError(
f"Argument `dtype` got invalid value {dtype} when argument `minval` "
f"is not None. Please don't use unsigned integers in this case."
)
elif maxval is None:
maxval = 1
with ops.name_scope(name, "stateless_random_uniform",
[shape, seed, minval, maxval]) as name:
shape = tensor_util.shape_tensor(shape)
if dtype.is_integer and minval is None:
key, counter, alg = _get_key_counter_alg(seed, alg)
result = (gen_stateless_random_ops_v2.
stateless_random_uniform_full_int_v2(shape,
key=key,
counter=counter,
dtype=dtype,
alg=alg,
name=name))
else:
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
if dtype.is_integer:
key, counter, alg = _get_key_counter_alg(seed, alg)
result = gen_stateless_random_ops_v2.stateless_random_uniform_int_v2(
shape,
key=key,
counter=counter,
minval=minval,
maxval=maxval,
alg=alg,
name=name)
else:
key, counter, alg = _get_key_counter_alg(seed, alg)
rnd = gen_stateless_random_ops_v2.stateless_random_uniform_v2(
shape, key=key, counter=counter, dtype=dtype, alg=alg)
result = math_ops.add(rnd * (maxval - minval),
minval,
name=name)
tensor_util.maybe_set_static_shape(result, shape)
return result
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 and shapes, 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`).
For full-range (i.e. inclusive of both max and min) random integers, pass
`minval=None` and `maxval=None` with an integer `dtype`. For an integer dtype
either both `minval` and `maxval` must be `None` or neither may be `None`. For
example:
```python
ints = tf.random.stateless_uniform(
[10], seed=(2, 3), minval=None, maxval=None, dtype=tf.int32)
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] Tensor, the seed to the random number generator. Must have
dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.)
minval: A Tensor or Python value of type `dtype`, broadcastable with
`shape` (for integer types, broadcasting is not supported, so it needs to
be a scalar). The lower bound on the range of random values to
generate. Pass `None` for full-range integers. Defaults to 0.
maxval: A Tensor or Python value of type `dtype`, broadcastable with
`shape` (for integer types, broadcasting is not supported, so it needs to
be a scalar). The upper bound on the range of random values to generate.
Defaults to 1 if `dtype` is floating point. Pass `None` for full-range
integers.
dtype: The type of the output: `float16`, `float32`, `float64`, `int32`, or
`int64`. For unbounded uniform ints (`minval`, `maxval` both `None`),
`uint32` and `uint64` may be used.
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 only one of `minval` or `maxval` is
specified.
"""
dtype = dtypes.as_dtype(dtype)
if dtype not in (dtypes.float16, dtypes.bfloat16, dtypes.float32,
dtypes.float64, dtypes.int32, dtypes.int64, dtypes.uint32,
dtypes.uint64):
raise ValueError("Invalid dtype %r" % dtype)
if dtype.is_integer:
if (minval is None) != (maxval is None):
raise ValueError(
"For integer dtype {}, minval and maxval must be both "
"`None` or both non-`None`.".format(dtype))
if minval is not None and dtype in (dtypes.uint32, dtypes.uint64):
raise ValueError("Invalid dtype for bounded uniform integers: %r" %
dtype)
elif maxval is None:
maxval = 1
with ops.name_scope(name, "stateless_random_uniform",
[shape, seed, minval, maxval]) as name:
shape = tensor_util.shape_tensor(shape)
if dtype.is_integer and minval is None:
if compat.forward_compatible(2020, 10, 25):
key, counter, alg = _get_key_counter_alg(seed)
result = (gen_stateless_random_ops_v2.
stateless_random_uniform_full_int_v2(shape,
key=key,
counter=counter,
dtype=dtype,
alg=alg,
name=name))
else:
result = gen_stateless_random_ops.stateless_random_uniform_full_int(
shape, seed=seed, dtype=dtype, name=name)
else:
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
if dtype.is_integer:
if compat.forward_compatible(2020, 10, 25):
key, counter, alg = _get_key_counter_alg(seed)
result = gen_stateless_random_ops_v2.stateless_random_uniform_int_v2(
shape,
key=key,
counter=counter,
minval=minval,
maxval=maxval,
alg=alg,
name=name)
else:
result = gen_stateless_random_ops.stateless_random_uniform_int(
shape,
seed=seed,
minval=minval,
maxval=maxval,
name=name)
else:
if compat.forward_compatible(2020, 10, 25):
key, counter, alg = _get_key_counter_alg(seed)
rnd = gen_stateless_random_ops_v2.stateless_random_uniform_v2(
shape, key=key, counter=counter, dtype=dtype, alg=alg)
else:
rnd = gen_stateless_random_ops.stateless_random_uniform(
shape, seed=seed, dtype=dtype)
result = math_ops.add(rnd * (maxval - minval),
minval,
name=name)
tensor_util.maybe_set_static_shape(result, shape)
return result
