Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
wk1234123
Paddle
提交
25029254
P
Paddle
项目概览
wk1234123
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
4
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
25029254
编写于
7月 14, 2020
作者:
Z
zhupengyang
提交者:
GitHub
7月 14, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
randn API: remove out, devive, stop_gradient; add name (#25409)
上级
41d22472
变更
3
显示空白变更内容
内联
并排
Showing
3 changed file
with
127 addition
and
166 deletion
+127
-166
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+33
-17
python/paddle/fluid/tests/unittests/test_randn_op.py
python/paddle/fluid/tests/unittests/test_randn_op.py
+50
-71
python/paddle/tensor/random.py
python/paddle/tensor/random.py
+44
-78
未找到文件。
python/paddle/fluid/layers/nn.py
浏览文件 @
25029254
...
...
@@ -10416,20 +10416,28 @@ def uniform_random_batch_size_like(input,
@templatedoc()
def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
def gaussian_random(shape,
mean=0.0,
std=1.0,
seed=0,
dtype='float32',
name=None):
"""
Generate a random tensor whose data is drawn from a Gaussian distribution.
Args:
shape (tuple[int] | list[int] | Variable | list[Variable]): Shape of the generated random tensor.
mean (float): Mean of the random tensor, defaults to 0.0.
std (float): Standard deviation of the random tensor, defaults to 1.0.
seed (int): ${seed_comment}
dtype(np.dtype | core.VarDesc.VarType | str): Output data type, float32 or float64.
shape(list|tuple|Variable): Shape of the Tensor to be created. The data
type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
the elements of it should be integers or Tensors with shape [1]. If
``shape`` is a Variable, it should be an 1-D Tensor .
mean(float): Mean of the random tensor, defaults to 0.0.
std(float): Standard deviation of the random tensor, defaults to 1.0.
seed(int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output
tensor, which can be float32, float64. Default is float32.
name(str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Default is None.
Returns:
Variable: Random tensor whose data is drawn from a Gaussian distribution, dtype: flaot32 or float64 as specified.
...
...
@@ -10492,11 +10500,16 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
# array([[2.3060477 , 2.676496 , 3.9911983 , 0.9990833 ],
# [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
"""
check_type(shape, 'shape', (list, tuple, Variable), 'gaussian_random')
if not isinstance(dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(dtype)
check_dtype(dtype, 'dtype', ['float32', 'float64'], 'gaussian_random')
if in_dygraph_mode():
shape = utils._convert_shape_to_list(shape)
return core.ops.gaussian_random('shape', shape, 'mean', mean, 'std',
std, 'seed', seed, 'dtype', dtype)
check_type(shape, 'shape', (list, tuple, Variable), 'gaussian_random/randn')
check_dtype(dtype, 'dtype', ['float32', 'float64'], 'gaussian_random/randn')
inputs = {}
attrs = {
...
...
@@ -10507,7 +10520,10 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
'use_mkldnn': False
}
utils._get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=shape, op_type='gaussian_random')
inputs=inputs,
attrs=attrs,
shape=shape,
op_type='gaussian_random/randn')
helper = LayerHelper('gaussian_random', **locals())
out = helper.create_variable_for_type_inference(dtype)
...
...
@@ -15011,13 +15027,13 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0,
float(min), 'max',
float(max), 'seed', seed, 'dtype', dtype)
check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random')
check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random')
check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random
/rand
')
check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random
/rand
')
inputs = dict()
attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}
utils._get_shape_tensor_inputs(
inputs=inputs, attrs=attrs, shape=shape, op_type='uniform_random')
inputs=inputs, attrs=attrs, shape=shape, op_type='uniform_random
/rand
')
helper = LayerHelper("uniform_random", **locals())
out = helper.create_variable_for_type_inference(dtype)
...
...
python/paddle/fluid/tests/unittests/test_randn_op.py
浏览文件 @
25029254
...
...
@@ -17,92 +17,71 @@ from __future__ import print_function
import
unittest
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
from
paddle
.fluid
import
Program
,
program_guard
from
paddle
import
Program
,
program_guard
class
TestRandnOp
(
unittest
.
TestCase
):
def
test_api
(
self
):
x1
=
paddle
.
randn
(
shape
=
[
1000
,
784
],
dtype
=
'float32'
)
x2
=
paddle
.
randn
(
shape
=
[
1000
,
784
],
dtype
=
'float64'
)
x3
=
fluid
.
layers
.
fill_constant
(
shape
=
[
1000
,
784
],
dtype
=
'float32'
,
value
=
0
)
paddle
.
randn
(
shape
=
[
1000
,
784
],
out
=
x3
,
dtype
=
'float32'
)
x4
=
paddle
.
randn
(
shape
=
[
1000
,
784
],
dtype
=
'float32'
,
device
=
'cpu'
)
x5
=
paddle
.
randn
(
shape
=
[
1000
,
784
],
dtype
=
'float32'
,
device
=
'gpu'
)
x6
=
paddle
.
randn
(
shape
=
[
1000
,
784
],
dtype
=
'float32'
,
device
=
'gpu'
,
stop_gradient
=
False
)
place
=
fluid
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
res
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{},
fetch_list
=
[
x1
,
x2
,
x3
,
x4
,
x5
,
x6
])
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
0
]),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res
[
0
]),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
1
]),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res
[
1
]),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
2
]),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res
[
2
]),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
3
]),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res
[
3
]),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
4
]),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res
[
4
]),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
5
]),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res
[
5
]),
1.
,
delta
=
0.1
)
shape
=
[
1000
,
784
]
train_program
=
Program
()
startup_program
=
Program
()
with
program_guard
(
train_program
,
startup_program
):
x1
=
paddle
.
randn
(
shape
,
'float32'
)
x2
=
paddle
.
randn
(
shape
,
'float64'
)
dim_1
=
paddle
.
fill_constant
([
1
],
"int64"
,
20
)
dim_2
=
paddle
.
fill_constant
([
1
],
"int32"
,
50
)
x3
=
paddle
.
randn
([
dim_1
,
dim_2
,
784
])
var_shape
=
paddle
.
nn
.
data
(
'X'
,
[
2
],
'int32'
)
x4
=
paddle
.
randn
(
var_shape
)
place
=
paddle
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
)
else
paddle
.
CPUPlace
()
exe
=
paddle
.
Executor
(
place
)
res
=
exe
.
run
(
train_program
,
feed
=
{
'X'
:
np
.
array
(
shape
,
dtype
=
'int32'
)},
fetch_list
=
[
x1
,
x2
,
x3
,
x4
])
for
out
in
res
:
self
.
assertAlmostEqual
(
np
.
mean
(
out
),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
out
),
1.
,
delta
=
0.1
)
class
TestRandnOpForDygraph
(
unittest
.
TestCase
):
def
test_api
(
self
):
shape
=
[
1000
,
784
]
place
=
paddle
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
)
else
paddle
.
CPUPlace
()
with
paddle
.
imperative
.
guard
(
place
):
x1
=
paddle
.
randn
(
shape
,
'float32'
)
x2
=
paddle
.
randn
(
shape
,
'float64'
)
dim_1
=
paddle
.
fill_constant
([
1
],
"int64"
,
20
)
dim_2
=
paddle
.
fill_constant
([
1
],
"int32"
,
50
)
x3
=
paddle
.
randn
(
shape
=
[
dim_1
,
dim_2
,
784
])
var_shape
=
paddle
.
imperative
.
to_variable
(
np
.
array
(
shape
))
x4
=
paddle
.
randn
(
var_shape
)
for
out
in
[
x1
,
x2
,
x3
,
x4
]:
self
.
assertAlmostEqual
(
np
.
mean
(
out
.
numpy
()),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
out
.
numpy
()),
1.
,
delta
=
0.1
)
class
TestRandnOpError
(
unittest
.
TestCase
):
def
test_error
(
self
):
with
program_guard
(
Program
(),
Program
()):
# The argument shape's size of randn_op should not be 0.
def
test_shape_size
():
out
=
paddle
.
randn
(
shape
=
[])
self
.
assertRaises
(
AssertionError
,
test_shape_size
)
self
.
assertRaises
(
AssertionError
,
paddle
.
randn
,
[])
# The argument shape's type of randn_op should be list or tuple.
def
test_shape_type
():
out
=
paddle
.
randn
(
shape
=
1
)
self
.
assertRaises
(
TypeError
,
test_shape_type
)
# The argument dtype of randn_op should be float32 or float64.
def
test_dtype_float16
():
out
=
paddle
.
randn
(
shape
=
[
1
,
2
],
dtype
=
'float16'
)
self
.
assertRaises
(
TypeError
,
test_dtype_float16
)
self
.
assertRaises
(
TypeError
,
paddle
.
randn
,
1
)
# The argument dtype of randn_op should be float32 or float64.
def
test_dtype_int32
():
out
=
paddle
.
randn
(
shape
=
[
1
,
2
],
dtype
=
'int32'
)
self
.
assertRaises
(
TypeError
,
test_dtype_int32
)
# The argument dtype of randn_op should be float32 or float64.
def
test_dtype_int64
():
out
=
paddle
.
randn
(
shape
=
[
1
,
2
],
dtype
=
'int64'
)
self
.
assertRaises
(
TypeError
,
test_dtype_int64
)
# The argument dtype of randn_op should be float32 or float64.
def
test_dtype_uint8
():
out
=
paddle
.
randn
(
shape
=
[
1
,
2
],
dtype
=
'uint8'
)
self
.
assertRaises
(
TypeError
,
test_dtype_uint8
)
# The argument dtype of randn_op should be float32 or float64.
def
test_dtype_bool
():
out
=
paddle
.
randn
(
shape
=
[
1
,
2
],
dtype
=
'bool'
)
self
.
assertRaises
(
TypeError
,
test_dtype_bool
)
self
.
assertRaises
(
TypeError
,
paddle
.
randn
,
[
1
,
2
],
'int32'
)
if
__name__
==
"__main__"
:
...
...
python/paddle/tensor/random.py
浏览文件 @
25029254
...
...
@@ -21,7 +21,7 @@ from ..fluid.framework import device_guard, in_dygraph_mode, _varbase_creator, V
from
..fluid.layers.layer_function_generator
import
templatedoc
from
..fluid.layer_helper
import
LayerHelper
from
..fluid.data_feeder
import
convert_dtype
,
check_variable_and_dtype
,
check_type
,
check_dtype
from
..fluid.layers
import
u
niform_random
,
utils
from
..fluid.layers
import
u
tils
,
uniform_random
,
gaussian_random
from
..fluid.layers.tensor
import
fill_constant
from
..fluid.io
import
shuffle
#DEFINE_ALIAS
...
...
@@ -206,36 +206,23 @@ def randint(low,
return
out
def
randn
(
shape
,
out
=
None
,
dtype
=
None
,
device
=
None
,
stop_gradient
=
True
,
name
=
None
):
def
randn
(
shape
,
dtype
=
None
,
name
=
None
):
"""
:alias_main: paddle.randn
:alias: paddle.randn,paddle.tensor.randn,paddle.tensor.random.randn
This function returns a tensor filled with random numbers from a normal
distribution with mean 0 and
variance
1 (also called the standard normal
distribution with mean 0 and
standard deviation
1 (also called the standard normal
distribution).
Args:
shape(list|tuple): Shape of the generated random tensor.
out(Variable, optional): Optional output which can be any created Variable
that meets the requirements to store the result of operation. If the
out is `None`, a new Variable will be returned to store the result.
Default is None.
shape(list|tuple|Variable): Shape of the Tensor to be created. The data
type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
the elements of it should be integers or Tensors with shape [1]. If
``shape`` is a Variable, it should be an 1-D Tensor .
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output
tensor, which can be float32, float64. if dtype is `None` , the data
type of output tensor is `float32` .
Default is None.
device(str, optional): Specific the output variable to be saved in cpu
or gpu memory. Supported None, 'cpu', 'gpu'. If it is None, the output
variable will be automatically assigned devices.
Default: None.
stop_gradient(bool, optional): Indicating if we stop gradient from current(out)
Variable. Default is True.
tensor, which can be float32, float64. If dtype is `None` , the data
type of output tensor is `float32` . Default is None.
name(str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
Default is None.
...
...
@@ -244,75 +231,50 @@ def randn(shape,
Random tensor whose data is drawn from a standard normal distribution,
dtype: flaot32 or float64 as specified.
Return type:
Variable
Return type: Variable
Raises:
TypeError: If the type of `shape` is not list or tuple.
TypeError: If the type of `shape` is not
Variable,
list or tuple.
TypeError: If the data type of `dtype` is not float32 or float64.
ValueError: If the length of `shape` is not bigger than 0.
Examples:
.. code-block:: python
# declarative mode
import paddle
import paddle.fluid as fluid
import numpy as np
data = paddle.randn([2, 4])
place = fluid.CPUPlace()
exe = fluid.Executor(place)
res, = exe.run(fluid.default_main_program(), feed={}, fetch_list=[data])
print(res)
# [[-1.4187592 0.7368311 -0.53748125 -0.0146909 ]
# [-0.66294265 -1.3090698 0.1898754 -0.14065823]]
paddle.enable_imperative()
.. code-block:: python
# example 1: attr shape is a list which doesn't contain tensor Variable.
result_1 = paddle.randn(shape=[2, 3])
# [[-2.923464 0.11934398 -0.51249987]
# [ 0.39632758 0.08177969 0.2692008 ]]
# imperative mode
import paddle
import paddle.fluid as fluid
import paddle.fluid.dygraph as dg
place = fluid.CPUPlace()
with dg.guard(place) as g:
x = paddle.randn([2, 4])
x_np = x.numpy()
print(x_np)
# [[ 1.5149173 -0.26234224 -0.592486 1.4523455 ]
# [ 0.04581212 -0.85345626 1.1687907 -0.02512913]]
"""
helper
=
LayerHelper
(
"randn"
,
**
locals
()
)
check_type
(
shape
,
'shape'
,
(
list
,
tuple
),
'randn'
)
assert
len
(
shape
)
>
0
,
(
"The size of argument(shape) can't be zero."
)
# example 2: attr shape is a list which contains tensor Variable.
dim_1 = paddle.fill_constant([1], "int64", 2)
dim_2 = paddle.fill_constant([1], "int32", 3)
result_2 = paddle.randn(shape=[dim_1, dim_2, 2])
# [[[-2.8852394 -0.25898588]
# [-0.47420555 0.17683524]
# [-0.7989969 0.00754541]]
# [[ 0.85201347 0.32320443]
# [ 1.1399018 0.48336947]
# [ 0.8086993 0.6868893 ]]]
# 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.randn(var_shape
)
# [[-2.878077 0.17099959 0.05111201]
# [-0.3761474 -1.044801 1.1870178 ]]
"""
if
dtype
is
None
:
dtype
=
'float32'
check_dtype
(
dtype
,
'create data type'
,
[
'float32'
,
'float64'
],
'randn'
)
if
out
is
None
:
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
else
:
check_variable_and_dtype
(
out
,
'out'
,
[
dtype
],
'randn'
)
out
.
stop_gradient
=
stop_gradient
dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
seed
=
np
.
random
.
randint
(
0
,
100
)
with
device_guard
(
device
):
helper
.
append_op
(
type
=
'gaussian_random'
,
outputs
=
{
'Out'
:
out
},
attrs
=
{
'shape'
:
shape
,
'mean'
:
0.0
,
'std'
:
1.0
,
'seed'
:
seed
,
'dtype'
:
dtype
,
'use_mkldnn'
:
False
})
out
=
gaussian_random
(
shape
=
shape
,
mean
=
0.0
,
std
=
1.0
,
seed
=
0
,
dtype
=
dtype
,
name
=
name
)
out
.
stop_gradient
=
True
return
out
...
...
@@ -369,6 +331,7 @@ def randperm(n, dtype="int64", name=None):
attrs
=
{
'n'
:
n
,
'dtype'
:
dtype
,
'seed'
:
0
}
helper
.
append_op
(
type
=
'randperm'
,
inputs
=
{},
outputs
=
{
'Out'
:
out
},
attrs
=
attrs
)
out
.
stop_gradient
=
True
return
out
...
...
@@ -439,4 +402,7 @@ def rand(shape, dtype=None, name=None):
"""
if
dtype
is
None
:
dtype
=
'float32'
return
uniform_random
(
shape
,
dtype
,
min
=
0.0
,
max
=
1.0
,
name
=
name
)
out
=
uniform_random
(
shape
,
dtype
,
min
=
0.0
,
max
=
1.0
,
name
=
name
)
out
.
stop_gradient
=
True
return
out
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录