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体验新版 GitCode,发现更多精彩内容 >>
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eb3173e2
编写于
7月 07, 2020
作者:
Z
zhupengyang
提交者:
GitHub
7月 07, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
rand API: remove out, device, stop_gradient; add name (#25246)
上级
22720a15
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
134 addition
and
182 deletion
+134
-182
paddle/fluid/operators/gaussian_random_op.cc
paddle/fluid/operators/gaussian_random_op.cc
+1
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+41
-71
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+3
-12
python/paddle/fluid/layers/utils.py
python/paddle/fluid/layers/utils.py
+20
-7
python/paddle/fluid/tests/unittests/test_rand_op.py
python/paddle/fluid/tests/unittests/test_rand_op.py
+30
-28
python/paddle/tensor/random.py
python/paddle/tensor/random.py
+39
-63
未找到文件。
paddle/fluid/operators/gaussian_random_op.cc
浏览文件 @
eb3173e2
...
@@ -98,7 +98,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
...
@@ -98,7 +98,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
return
;
return
;
}
}
if
(
!
(
ctx
->
HasInput
(
"ShapeTensor"
)
&&
!
ctx
->
HasInputs
(
"ShapeTensorList"
)
))
{
if
(
!
ctx
->
HasInput
(
"ShapeTensor"
)
&&
!
ctx
->
HasInputs
(
"ShapeTensorList"
))
{
PADDLE_ENFORCE_GT
(
PADDLE_ENFORCE_GT
(
shape
.
size
(),
0UL
,
shape
.
size
(),
0UL
,
platform
::
errors
::
InvalidArgument
(
platform
::
errors
::
InvalidArgument
(
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
eb3173e2
...
@@ -10487,29 +10487,24 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
...
@@ -10487,29 +10487,24 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
# [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
# [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
"""
"""
helper = LayerHelper('gaussian_random', **locals())
check_type(shape, 'shape', (list, tuple, Variable), 'gaussian_random')
out = helper.create_variable_for_type_inference(dtype)
if not isinstance(dtype, core.VarDesc.VarType):
if not isinstance(shape, (list, tuple, Variable)):
dtype = convert_np_dtype_to_dtype_(dtype)
raise TypeError(
check_dtype(dtype, 'dtype', ['float32', 'float64'], 'gaussian_random')
"The type of 'shape' in fill_constant must be Variable, list or tuple, but "
"received %s." % (type(shape)))
inputs = {}
c_dtype = convert_np_dtype_to_dtype_(dtype)
attrs = {
attrs = {
'mean': mean,
'mean': mean,
'std': std,
'std': std,
'seed': seed,
'seed': seed,
'dtype':
c_
dtype,
'dtype': dtype,
'use_mkldnn': False
'use_mkldnn': False
}
}
inputs = {}
utils._get_shape_tensor_inputs(
utils._get_shape_tensor_inputs(
inputs=inputs,
inputs=inputs, attrs=attrs, shape=shape, op_type='gaussian_random')
helper=helper,
attrs=attrs,
shape=shape,
op_type='gaussian_random')
helper = LayerHelper('gaussian_random', **locals())
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
helper.append_op(
type='gaussian_random',
type='gaussian_random',
inputs=inputs,
inputs=inputs,
...
@@ -14937,7 +14932,8 @@ def gather_tree(ids, parents):
...
@@ -14937,7 +14932,8 @@ def gather_tree(ids, parents):
@templatedoc()
@templatedoc()
def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0,
name=None):
"""
"""
This OP initializes a variable with random values sampled from a
This OP initializes a variable with random values sampled from a
uniform distribution in the range [min, max).
uniform distribution in the range [min, max).
...
@@ -14952,18 +14948,24 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
...
@@ -14952,18 +14948,24 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
result=[[0.8505902, 0.8397286]]
result=[[0.8505902, 0.8397286]]
Args:
Args:
shape (list|tuple|Variable): The shape of the output Tensor, if the shape is a list or tuple,
shape (list|tuple|Variable): The shape of the output Tensor, if the
its elements can be an integer
shape is a list or tuple, its elements can be an integer or a
or a Tensor with the shape [1], and the type of the Tensor must be int32 or int64.
Tensor with the shape [1], and the type of the Tensor must be
If the shape is a Variable, it is a 1-D Tensor, and the type of the Tensor must be int32 or int64.
int32 or int64. If the shape is a Variable, it is a 1-D Tensor, and
dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the output Tensor. Supported data types: float32, float64.
the type of the Tensor must be int32 or int64.
Default: float32.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the
min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
output Tensor. Supported data types: float32, float64. Default: float32.
max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
min (float, optional): The lower bound on the range of random values
seed (int, optional): Random seed used for generating samples. 0 means use a
to generate, the min is included in the range. Default -1.0.
seed generated by the system. Note that if seed is not 0, this
max (float, optional): The upper bound on the range of random values
operator will always generate the same random numbers every time.
to generate, the max is excluded in the range. Default 1.0.
Default 0.
seed (int, optional): Random seed used for generating samples. 0 means
use a seed generated by the system. Note that if seed is not 0,
this operator will always generate the same random numbers every
time. Default 0.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Returns:
Variable: A Tensor of the specified shape filled with uniform_random values.
Variable: A Tensor of the specified shape filled with uniform_random values.
...
@@ -14993,62 +14995,30 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
...
@@ -14993,62 +14995,30 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
result_4 = fluid.layers.uniform_random(var_shape_int32)
result_4 = fluid.layers.uniform_random(var_shape_int32)
"""
"""
check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random')
if not isinstance(dtype, core.VarDesc.VarType):
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
dtype = convert_np_dtype_to_dtype_(dtype)
check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random')
def get_new_shape_tensor(list_shape):
if in_dygraph_mode():
new_shape_tensor = []
shape = utils._convert_shape_to_list(shape)
for dim in list_shape:
return core.ops.uniform_random('shape', shape, 'min',
if isinstance(dim, Variable):
float(min), 'max',
dim.stop_gradient = True
float(max), 'seed', seed, 'dtype', dtype)
new_shape_tensor.append(dim)
else:
assert (isinstance(dim, int))
temp_out = helper.create_variable_for_type_inference('int64')
fill_constant([1], 'int64', dim, force_cpu=True, out=temp_out)
new_shape_tensor.append(temp_out)
return new_shape_tensor
def get_attr_shape(list_shape):
check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random')
unk_dim_idx = -1
check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random')
attrs_shape = []
for dim_idx, dim_size in enumerate(list_shape):
if isinstance(dim_size, Variable):
attrs_shape.append(-1)
else:
attrs_shape.append(dim_size)
assert dim_size > 0, (
"Each dimension size given in shape must not be negative "
"except one unknown dimension.")
return attrs_shape
helper = LayerHelper("uniform_random", **locals())
inputs = dict()
inputs = dict()
attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}
attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}
if in_dygraph_mode():
utils._get_shape_tensor_inputs(
attrs['shape'] = shape
inputs=inputs, attrs=attrs, shape=shape, op_type='uniform_random')
else:
if isinstance(shape, Variable):
shape.stop_gradient = True
inputs["ShapeTensor"] = shape
elif isinstance(shape, (list, tuple)):
assert len(shape) > 0, (
"The size of argument(shape) can't be zero.")
attrs["shape"] = get_attr_shape(shape)
if utils._contain_var(shape):
inputs['ShapeTensorList'] = get_new_shape_tensor(shape)
helper = LayerHelper("uniform_random", **locals())
out = helper.create_variable_for_type_inference(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
helper.append_op(
type="uniform_random", inputs=inputs, attrs=attrs,
type="uniform_random", inputs=inputs, attrs=attrs,
outputs={"Out": out})
outputs={"Out": out})
return out
return helper.append_activation(out)
def unbind(input, axis=0):
def unbind(input, axis=0):
...
...
python/paddle/fluid/layers/tensor.py
浏览文件 @
eb3173e2
...
@@ -685,12 +685,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
...
@@ -685,12 +685,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
attrs
[
'str_value'
]
=
str
(
float
(
value
))
attrs
[
'str_value'
]
=
str
(
float
(
value
))
if
in_dygraph_mode
():
if
in_dygraph_mode
():
if
isinstance
(
shape
,
(
list
,
tuple
)):
shape
=
utils
.
_convert_shape_to_list
(
shape
)
shape
=
list
(
map
(
lambda
x
:
x
.
numpy
()[
0
]
if
isinstance
(
x
,
Variable
)
else
x
,
shape
))
else
:
shape
=
list
(
shape
.
numpy
().
astype
(
int
))
if
out
is
None
:
if
out
is
None
:
out
=
_varbase_creator
(
dtype
=
dtype
)
out
=
_varbase_creator
(
dtype
=
dtype
)
...
@@ -719,12 +714,8 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
...
@@ -719,12 +714,8 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
'fill_constant'
)
'fill_constant'
)
helper
=
LayerHelper
(
"fill_constant"
,
**
locals
())
helper
=
LayerHelper
(
"fill_constant"
,
**
locals
())
inputs
=
utils
.
_get_shape_tensor_inputs
(
utils
.
_get_shape_tensor_inputs
(
inputs
=
inputs
,
inputs
=
inputs
,
attrs
=
attrs
,
shape
=
shape
,
op_type
=
'fill_constant'
)
helper
=
helper
,
attrs
=
attrs
,
shape
=
shape
,
op_type
=
'fill_constant'
)
if
out
is
None
:
if
out
is
None
:
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
...
...
python/paddle/fluid/layers/utils.py
浏览文件 @
eb3173e2
...
@@ -282,7 +282,7 @@ def _contain_var(list_or_tuple):
...
@@ -282,7 +282,7 @@ def _contain_var(list_or_tuple):
return
False
return
False
def
_get_shape_tensor_inputs
(
inputs
,
helper
,
attrs
,
shape
,
op_type
):
def
_get_shape_tensor_inputs
(
inputs
,
attrs
,
shape
,
op_type
):
from
.tensor
import
fill_constant
,
cast
from
.tensor
import
fill_constant
,
cast
def
_get_attr_shape
(
list_shape
):
def
_get_attr_shape
(
list_shape
):
...
@@ -295,7 +295,7 @@ def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
...
@@ -295,7 +295,7 @@ def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
return
attr_shape
return
attr_shape
def
_get_shape_tensor
(
list_shape
):
def
_get_shape_tensor
(
list_shape
):
new_shape_tensor
=
[]
shape_tensor_list
=
[]
for
idx
,
dim
in
enumerate
(
list_shape
):
for
idx
,
dim
in
enumerate
(
list_shape
):
if
isinstance
(
dim
,
Variable
):
if
isinstance
(
dim
,
Variable
):
dim
.
stop_gradient
=
True
dim
.
stop_gradient
=
True
...
@@ -305,11 +305,11 @@ def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
...
@@ -305,11 +305,11 @@ def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
'(When type of shape in'
+
op_type
+
'is list or tuple.)'
)
'(When type of shape in'
+
op_type
+
'is list or tuple.)'
)
if
convert_dtype
(
dim
.
dtype
)
==
'int64'
:
if
convert_dtype
(
dim
.
dtype
)
==
'int64'
:
dim
=
cast
(
x
=
dim
,
dtype
=
'int32'
)
dim
=
cast
(
x
=
dim
,
dtype
=
'int32'
)
new_shape_tensor
.
append
(
dim
)
shape_tensor_list
.
append
(
dim
)
else
:
else
:
temp_out
=
fill_constant
([
1
],
'int32'
,
dim
,
force_cpu
=
True
)
temp_out
=
fill_constant
([
1
],
'int32'
,
dim
,
force_cpu
=
True
)
new_shape_tensor
.
append
(
temp_out
)
shape_tensor_list
.
append
(
temp_out
)
return
new_shape_tensor
return
shape_tensor_list
if
isinstance
(
shape
,
Variable
):
if
isinstance
(
shape
,
Variable
):
shape
.
stop_gradient
=
True
shape
.
stop_gradient
=
True
...
@@ -325,8 +325,8 @@ def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
...
@@ -325,8 +325,8 @@ def _get_shape_tensor_inputs(inputs, helper, attrs, shape, op_type):
attrs
[
"shape"
]
=
_get_attr_shape
(
shape
)
attrs
[
"shape"
]
=
_get_attr_shape
(
shape
)
if
_contain_var
(
shape
):
if
_contain_var
(
shape
):
inputs
[
'ShapeTensorList'
]
=
_get_shape_tensor
(
shape
)
inputs
[
'ShapeTensorList'
]
=
_get_shape_tensor
(
shape
)
else
:
return
inputs
raise
TypeError
(
"Shape only supports Variable, or list, or tuple."
)
def
_convert_to_tensor_list
(
old_list
,
dtype
=
"int32"
):
def
_convert_to_tensor_list
(
old_list
,
dtype
=
"int32"
):
...
@@ -345,3 +345,16 @@ def _convert_to_tensor_list(old_list, dtype="int32"):
...
@@ -345,3 +345,16 @@ def _convert_to_tensor_list(old_list, dtype="int32"):
temp_out
=
fill_constant
([
1
],
dtype
,
ele
,
force_cpu
=
True
)
temp_out
=
fill_constant
([
1
],
dtype
,
ele
,
force_cpu
=
True
)
new_list_tensor
.
append
(
temp_out
)
new_list_tensor
.
append
(
temp_out
)
return
new_list_tensor
return
new_list_tensor
def
_convert_shape_to_list
(
shape
):
"""
Convert shape(list, tuple, variable) to list in imperative mode
"""
if
isinstance
(
shape
,
(
list
,
tuple
)):
shape
=
list
(
map
(
lambda
x
:
x
.
numpy
()[
0
]
if
isinstance
(
x
,
Variable
)
else
x
,
shape
))
else
:
shape
=
list
(
shape
.
numpy
().
astype
(
int
))
return
shape
python/paddle/fluid/tests/unittests/test_rand_op.py
浏览文件 @
eb3173e2
...
@@ -47,71 +47,73 @@ class TestRandOpError(unittest.TestCase):
...
@@ -47,71 +47,73 @@ class TestRandOpError(unittest.TestCase):
self
.
assertRaises
(
TypeError
,
test_dtype
)
self
.
assertRaises
(
TypeError
,
test_dtype
)
def
test_shape_list
():
rand
(
shape
=
[
2.
])
self
.
assertRaises
(
TypeError
,
test_shape_list
)
def
test_shape_list2
():
rand
(
shape
=
[
2
,
3.
])
self
.
assertRaises
(
TypeError
,
test_shape_list2
)
def
test_device
():
rand
(
shape
=
[
3
,
4
],
device
=
'device'
)
self
.
assertRaises
(
ValueError
,
test_device
)
class
TestRandOp
(
unittest
.
TestCase
):
class
TestRandOp
(
unittest
.
TestCase
):
"""
"""
This class test the common usages of randop.
This class test the common usages of randop.
"""
"""
def
test_run
(
self
):
def
run_net
(
self
,
use_cuda
=
False
):
use_cuda
=
False
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
exe
=
fluid
.
Executor
(
place
)
train_program
=
fluid
.
Program
()
train_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
startup_program
=
fluid
.
Program
()
with
fluid
.
program_guard
(
train_program
,
startup_program
):
with
fluid
.
program_guard
(
train_program
,
startup_program
):
result_1
=
rand
(
shape
=
[
3
,
4
])
result_0
=
rand
([
3
,
4
])
result_1
=
rand
([
3
,
4
],
'float64'
)
dim_1
=
fluid
.
layers
.
fill_constant
([
1
],
"int64"
,
3
)
dim_1
=
fluid
.
layers
.
fill_constant
([
1
],
"int64"
,
3
)
dim_2
=
fluid
.
layers
.
fill_constant
([
1
],
"int32"
,
5
)
dim_2
=
fluid
.
layers
.
fill_constant
([
1
],
"int32"
,
5
)
result_2
=
rand
(
shape
=
[
dim_1
,
dim_2
])
result_2
=
rand
(
shape
=
[
dim_1
,
dim_2
])
var_shape
=
fluid
.
data
(
name
=
'var_shape'
,
shape
=
[
2
],
dtype
=
"int64"
)
var_shape
=
fluid
.
data
(
name
=
'var_shape'
,
shape
=
[
2
],
dtype
=
"int64"
)
result_3
=
rand
(
var_shape
)
result_3
=
rand
(
var_shape
)
var_shape_int32
=
fluid
.
data
(
var_shape_int32
=
fluid
.
data
(
name
=
'var_shape_int32'
,
shape
=
[
2
],
dtype
=
"int32"
)
name
=
'var_shape_int32'
,
shape
=
[
2
],
dtype
=
"int32"
)
result_4
=
rand
(
var_shape_int32
)
result_4
=
rand
(
var_shape_int32
)
exe
.
run
(
startup_program
)
exe
.
run
(
startup_program
)
x1
=
np
.
array
([
3
,
2
]).
astype
(
'int64'
)
x1
=
np
.
array
([
3
,
2
]).
astype
(
'int64'
)
x2
=
np
.
array
([
4
,
3
]).
astype
(
'int32'
)
x2
=
np
.
array
([
4
,
3
]).
astype
(
'int32'
)
ret
=
exe
.
run
(
train_program
,
ret
=
exe
.
run
(
feed
=
{
"var_shape"
:
x1
,
train_program
,
"var_shape_int32"
:
x2
},
feed
=
{
"var_shape"
:
x1
,
fetch_list
=
[
result_1
,
result_2
,
result_3
,
result_4
])
"var_shape_int32"
:
x2
},
fetch_list
=
[
result_1
,
result_1
,
result_2
,
result_3
,
result_4
])
def
test_run
(
self
):
self
.
run_net
(
False
)
if
core
.
is_compiled_with_cuda
():
self
.
run_net
(
True
)
class
TestRandOpForDygraph
(
unittest
.
TestCase
):
class
TestRandOpForDygraph
(
unittest
.
TestCase
):
"""
"""
This class test the common usages of randop.
This class test the common usages of randop.
"""
"""
def
test_run
(
self
):
def
run_net
(
self
,
use_cuda
=
False
):
use_cuda
=
False
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
with
fluid
.
dygraph
.
guard
():
with
fluid
.
dygraph
.
guard
(
place
):
rand
(
shape
=
[
3
,
4
])
rand
([
3
,
4
])
rand
([
3
,
4
],
'float64'
)
dim_1
=
fluid
.
layers
.
fill_constant
([
1
],
"int64"
,
3
)
dim_1
=
fluid
.
layers
.
fill_constant
([
1
],
"int64"
,
3
)
dim_2
=
fluid
.
layers
.
fill_constant
([
1
],
"int32"
,
5
)
dim_2
=
fluid
.
layers
.
fill_constant
([
1
],
"int32"
,
5
)
rand
(
shape
=
[
dim_1
,
dim_2
])
rand
(
shape
=
[
dim_1
,
dim_2
])
var_shape
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([
3
,
4
]))
var_shape
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([
3
,
4
]))
rand
(
var_shape
)
rand
(
var_shape
)
def
test_run
(
self
):
self
.
run_net
(
False
)
if
core
.
is_compiled_with_cuda
():
self
.
run_net
(
True
)
if
__name__
==
"__main__"
:
if
__name__
==
"__main__"
:
unittest
.
main
()
unittest
.
main
()
python/paddle/tensor/random.py
浏览文件 @
eb3173e2
...
@@ -406,7 +406,7 @@ def randperm(n,
...
@@ -406,7 +406,7 @@ def randperm(n,
return
out
return
out
def
rand
(
shape
,
out
=
None
,
dtype
=
None
,
device
=
None
,
stop_gradient
=
Tru
e
):
def
rand
(
shape
,
dtype
=
None
,
name
=
Non
e
):
"""
"""
:alias_main: paddle.rand
:alias_main: paddle.rand
:alias: paddle.rand,paddle.tensor.rand,paddle.tensor.random.rand
:alias: paddle.rand,paddle.tensor.rand,paddle.tensor.random.rand
...
@@ -424,22 +424,19 @@ def rand(shape, out=None, dtype=None, device=None, stop_gradient=True):
...
@@ -424,22 +424,19 @@ def rand(shape, out=None, dtype=None, device=None, stop_gradient=True):
result=[[0.8505902, 0.8397286]]
result=[[0.8505902, 0.8397286]]
Args:
Args:
shape(list|tuple|Variable): Shape of the Tensor to be created.
shape(list|tuple|Variable): Shape of the Tensor to be created. The data
The data type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
the elements of it should be integers or Tensors with shape [1].
the elements of it should be integers or Tensors with shape [1]. If
If ``shape`` is a Variable, it should be an 1-D Tensor .
``shape`` is a Variable, it should be an 1-D Tensor .
out(Variable, optional): Optional output which can be any created
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the
Variable that meets the requirements to store the result of operation.
output tensor which can be float32, float64, if dytpe is `None`,
if out is None, a new Varibale will be create to store the result.
the data type of created tensor is `float32`
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output tensor
name(str, optional): The default value is None. Normally there is no
which can be float32, float64, if dytpe is `None`, the data
need for user to set this property. For more information, please
type of created tensor is `float32`
refer to :ref:`api_guide_Name`.
device(str, optional): This parameter specifies that the Tensor is created
on the GPU or CPU.
stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable,
default value is True.
Returns:
Returns:
Variable: A Tensor of the specified shape filled with random numbers from a uniform distribution on the interval [0, 1).
Variable: A Tensor of the specified shape filled with random numbers
from a uniform distribution on the interval [0, 1).
Raises:
Raises:
TypeError: The shape type should be list or tupple or Variable.
TypeError: The shape type should be list or tupple or Variable.
...
@@ -447,54 +444,33 @@ def rand(shape, out=None, dtype=None, device=None, stop_gradient=True):
...
@@ -447,54 +444,33 @@ def rand(shape, out=None, dtype=None, device=None, stop_gradient=True):
Examples:
Examples:
.. code-block:: python
.. code-block:: python
import paddle
import paddle
import paddle.fluid as fluid
import numpy as np
# example 1:
paddle.enable_imperative()
# attr shape is a list which doesn't contain tensor Variable.
# example 1: attr shape is a list which doesn't contain tensor Variable.
result_1 = paddle.rand(shape=[3, 4])
result_1 = paddle.rand(shape=[2, 3])
# [[0.451152 , 0.55825245, 0.403311 ],
# example 2:
# [0.22550228, 0.22106001, 0.7877319 ]]
# attr shape is a list which contains tensor Variable.
dim_1 = fluid.layers.fill_constant([1],"int64",3)
# example 2: attr shape is a list which contains tensor Variable.
dim_2 = fluid.layers.fill_constant([1],"int32",5)
dim_1 = paddle.fill_constant([1], "int64", 2)
result_2 = paddle.rand(shape=[dim_1, dim_2])
dim_2 = paddle.fill_constant([1], "int32", 3)
result_2 = paddle.rand(shape=[dim_1, dim_2, 2])
# [[[0.8879919 0.25788337]
# [0.28826773 0.9712097 ]
# [0.26438272 0.01796806]]
# [[0.33633623 0.28654453]
# [0.79109055 0.7305809 ]
# [0.870881 0.2984597 ]]]
# example 3: attr shape is a Variable, the data type must be int64 or int32.
var_shape = paddle.imperative.to_variable(np.array([2, 3]))
result_3 = paddle.rand(var_shape)
# [[0.22920267 0.841956 0.05981819]
# [0.4836288 0.24573246 0.7516129 ]]
# example 3:
# attr shape is a Variable, the data type must be int64 or int32.
var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
result_3 = paddle.rand(var_shape)
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
result_4 = paddle.rand(var_shape_int32)
"""
"""
if
dtype
is
None
:
if
dtype
is
None
:
dtype
=
'float32'
dtype
=
'float32'
return
uniform_random
(
shape
,
dtype
,
min
=
0.0
,
max
=
1.0
,
name
=
name
)
check_dtype
(
dtype
,
'dtype'
,
[
'float32'
,
'float64'
],
'rand'
)
check_type
(
shape
,
'shape'
,
(
Variable
,
list
,
tuple
),
'rand'
)
if
isinstance
(
shape
,
Variable
):
check_variable_and_dtype
(
shape
,
'shape'
,
[
'int32'
,
'int64'
],
'rand'
)
elif
isinstance
(
shape
,
(
list
,
tuple
)):
for
i
,
_shape
in
enumerate
(
shape
):
if
not
isinstance
(
_shape
,
Variable
):
check_type
(
_shape
,
'_shape'
,
(
int
),
'rand'
)
else
:
check_variable_and_dtype
(
_shape
,
'shape['
+
str
(
i
)
+
']'
,
[
'int32'
,
'int64'
],
'rand'
)
if
device
not
in
[
None
,
'cpu'
,
'gpu'
]:
raise
ValueError
(
"The input device should in [None, 'cpu', 'gpu'], but received {}"
.
format
(
device
))
helper
=
LayerHelper
(
"rand"
,
**
locals
())
if
out
is
None
:
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
else
:
check_variable_and_dtype
(
out
,
'out'
,
[
dtype
],
'rand'
)
out
.
stop_gradient
=
stop_gradient
with
device_guard
(
device
):
out
=
uniform_random
(
shape
,
dtype
,
min
=
0.
,
max
=
1.0
)
return
out
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