def uniform(self,
shape,
minval=0,
maxval=None,
dtype=dtypes.float32,
name=None):
dtype = dtypes.as_dtype(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, "stateful_uniform",
[shape, minval, maxval]) as name:
shape = _shape_tensor(shape)
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_stateful_random_ops.stateful_uniform_int(
self.state.handle,
self.algorithm,
shape=shape,
minval=minval,
maxval=maxval,
name=name)
else:
# TODO(wangpeng): implement uniform for floats
raise ValueError("uniform for floats not implemented yet")
def uniform(self, shape, minval=0, maxval=None,
dtype=dtypes.float32, name=None):
"""Outputs random values from a uniform distribution.
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 float numbers especially
low-precision types like bfloat16, because of
rounding, the result may sometimes include `maxval`.)
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.
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.
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 maxval is None:
if dtype.is_integer:
raise ValueError("Must specify maxval for integer dtype %r" % dtype)
maxval = 1
with ops.name_scope(name, "stateful_uniform",
[shape, minval, maxval]) as name:
shape = _shape_tensor(shape)
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_stateful_random_ops.stateful_uniform_int(
self.state.handle, self.algorithm, shape=shape,
minval=minval, maxval=maxval, name=name)
else:
rnd = self._uniform(shape=shape, dtype=dtype)
return math_ops.add(rnd * (maxval - minval), minval, name=name)
def uniform(self,
shape,
minval=0,
maxval=None,
dtype=dtypes.float32,
name=None):
"""Outputs random values from a uniform distribution.
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 float numbers especially
low-precision types like bfloat16, because of
rounding, the result may sometimes include `maxval`.)
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 random integers, pass `minval=None` and `maxval=None` with an
integer `dtype` (for integer dtypes, `minval` and `maxval` must be both
`None` or both not `None`).
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output
tensor.
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 (included) 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 (excluded) on the range of random
values to generate. Pass `None` for full-range integers. Defaults to 1
if `dtype` is floating point.
dtype: The type of the output.
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.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`; got minval={} and "
"maxval={}".format(dtype, minval, maxval))
elif maxval is None:
maxval = 1
with ops.name_scope(name, "stateful_uniform",
[shape, minval, maxval]) as name:
shape = _shape_tensor(shape)
if dtype.is_integer and minval is None:
return self._uniform_full_int(shape=shape,
dtype=dtype,
name=name)
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 = self._prepare_key_counter(shape)
return gen_stateless_random_ops_v2.stateless_random_uniform_int_v2(
shape=shape,
key=key,
counter=counter,
minval=minval,
maxval=maxval,
alg=self.algorithm,
name=name)
return gen_stateful_random_ops.stateful_uniform_int(
self.state.handle,
self.algorithm,
shape=shape,
minval=minval,
maxval=maxval,
name=name)
else:
rnd = self._uniform(shape=shape, dtype=dtype)
return math_ops.add(rnd * (maxval - minval), minval, name=name)
