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体验新版 GitCode,发现更多精彩内容 >>
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25029254
编写于
7月 14, 2020
作者:
Z
zhupengyang
提交者:
GitHub
7月 14, 2020
浏览文件
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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
隐藏空白更改
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,
...
@@ -10416,20 +10416,28 @@ def uniform_random_batch_size_like(input,
@templatedoc()
@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.
Generate a random tensor whose data is drawn from a Gaussian distribution.
Args:
Args:
shape (tuple[int] | list[int] | Variable | list[Variable]): Shape of the generated random tensor.
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,
mean (float): Mean of the random tensor, defaults to 0.0.
the elements of it should be integers or Tensors with shape [1]. If
``shape`` is a Variable, it should be an 1-D Tensor .
std (float): Standard deviation of the random tensor, defaults to 1.0.
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}
seed(int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output
dtype(np.dtype | core.VarDesc.VarType | str): Output data type, float32 or float64.
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:
Returns:
Variable: Random tensor whose data is drawn from a Gaussian distribution, dtype: flaot32 or float64 as specified.
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'):
...
@@ -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 ],
# array([[2.3060477 , 2.676496 , 3.9911983 , 0.9990833 ],
# [2.8675377 , 2.2279181 , 0.79029655, 2.8447366 ]], dtype=float32)
# [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):
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'], '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 = {}
inputs = {}
attrs = {
attrs = {
...
@@ -10507,7 +10520,10 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
...
@@ -10507,7 +10520,10 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
'use_mkldnn': False
'use_mkldnn': False
}
}
utils._get_shape_tensor_inputs(
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())
helper = LayerHelper('gaussian_random', **locals())
out = helper.create_variable_for_type_inference(dtype)
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,
...
@@ -15011,13 +15027,13 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0,
float(min), 'max',
float(min), 'max',
float(max), 'seed', seed, 'dtype', dtype)
float(max), 'seed', seed, 'dtype', dtype)
check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random')
check_type(shape, 'shape', (list, tuple, Variable), 'uniform_random
/rand
')
check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random')
check_dtype(dtype, 'dtype', ('float32', 'float64'), 'uniform_random
/rand
')
inputs = dict()
inputs = dict()
attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}
attrs = {'seed': seed, 'min': min, 'max': max, 'dtype': dtype}
utils._get_shape_tensor_inputs(
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())
helper = LayerHelper("uniform_random", **locals())
out = helper.create_variable_for_type_inference(dtype)
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
...
@@ -17,92 +17,71 @@ from __future__ import print_function
import
unittest
import
unittest
import
numpy
as
np
import
numpy
as
np
import
paddle
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
import
paddle.fluid.core
as
core
from
paddle
.fluid
import
Program
,
program_guard
from
paddle
import
Program
,
program_guard
class
TestRandnOp
(
unittest
.
TestCase
):
class
TestRandnOp
(
unittest
.
TestCase
):
def
test_api
(
self
):
def
test_api
(
self
):
x1
=
paddle
.
randn
(
shape
=
[
1000
,
784
],
dtype
=
'float32'
)
shape
=
[
1000
,
784
]
x2
=
paddle
.
randn
(
shape
=
[
1000
,
784
],
dtype
=
'float64'
)
train_program
=
Program
()
x3
=
fluid
.
layers
.
fill_constant
(
startup_program
=
Program
()
shape
=
[
1000
,
784
],
dtype
=
'float32'
,
value
=
0
)
with
program_guard
(
train_program
,
startup_program
):
paddle
.
randn
(
shape
=
[
1000
,
784
],
out
=
x3
,
dtype
=
'float32'
)
x1
=
paddle
.
randn
(
shape
,
'float32'
)
x4
=
paddle
.
randn
(
shape
=
[
1000
,
784
],
dtype
=
'float32'
,
device
=
'cpu'
)
x2
=
paddle
.
randn
(
shape
,
'float64'
)
x5
=
paddle
.
randn
(
shape
=
[
1000
,
784
],
dtype
=
'float32'
,
device
=
'gpu'
)
x6
=
paddle
.
randn
(
dim_1
=
paddle
.
fill_constant
([
1
],
"int64"
,
20
)
shape
=
[
1000
,
784
],
dim_2
=
paddle
.
fill_constant
([
1
],
"int32"
,
50
)
dtype
=
'float32'
,
x3
=
paddle
.
randn
([
dim_1
,
dim_2
,
784
])
device
=
'gpu'
,
stop_gradient
=
False
)
var_shape
=
paddle
.
nn
.
data
(
'X'
,
[
2
],
'int32'
)
x4
=
paddle
.
randn
(
var_shape
)
place
=
fluid
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
place
=
paddle
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
exe
=
fluid
.
Executor
(
place
)
)
else
paddle
.
CPUPlace
()
res
=
exe
.
run
(
fluid
.
default_main_program
(),
exe
=
paddle
.
Executor
(
place
)
feed
=
{},
res
=
exe
.
run
(
train_program
,
fetch_list
=
[
x1
,
x2
,
x3
,
x4
,
x5
,
x6
])
feed
=
{
'X'
:
np
.
array
(
shape
,
dtype
=
'int32'
)},
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
0
]),
.
0
,
delta
=
0.1
)
fetch_list
=
[
x1
,
x2
,
x3
,
x4
])
self
.
assertAlmostEqual
(
np
.
std
(
res
[
0
]),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
1
]),
.
0
,
delta
=
0.1
)
for
out
in
res
:
self
.
assertAlmostEqual
(
np
.
std
(
res
[
1
]),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
out
),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
2
]),
.
0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
out
),
1.
,
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
)
class
TestRandnOpForDygraph
(
unittest
.
TestCase
):
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
4
]),
.
0
,
delta
=
0.1
)
def
test_api
(
self
):
self
.
assertAlmostEqual
(
np
.
std
(
res
[
4
]),
1.
,
delta
=
0.1
)
shape
=
[
1000
,
784
]
self
.
assertAlmostEqual
(
np
.
mean
(
res
[
5
]),
.
0
,
delta
=
0.1
)
place
=
paddle
.
CUDAPlace
(
0
)
if
core
.
is_compiled_with_cuda
(
self
.
assertAlmostEqual
(
np
.
std
(
res
[
5
]),
1.
,
delta
=
0.1
)
)
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
):
class
TestRandnOpError
(
unittest
.
TestCase
):
def
test_error
(
self
):
def
test_error
(
self
):
with
program_guard
(
Program
(),
Program
()):
with
program_guard
(
Program
(),
Program
()):
# The argument shape's size of randn_op should not be 0.
# The argument shape's size of randn_op should not be 0.
def
test_shape_size
():
self
.
assertRaises
(
AssertionError
,
paddle
.
randn
,
[])
out
=
paddle
.
randn
(
shape
=
[])
self
.
assertRaises
(
AssertionError
,
test_shape_size
)
# The argument shape's type of randn_op should be list or tuple.
# The argument shape's type of randn_op should be list or tuple.
def
test_shape_type
():
self
.
assertRaises
(
TypeError
,
paddle
.
randn
,
1
)
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
)
# The argument dtype of randn_op should be float32 or float64.
# The argument dtype of randn_op should be float32 or float64.
def
test_dtype_int32
():
self
.
assertRaises
(
TypeError
,
paddle
.
randn
,
[
1
,
2
],
'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
)
if
__name__
==
"__main__"
:
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
...
@@ -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.layers.layer_function_generator
import
templatedoc
from
..fluid.layer_helper
import
LayerHelper
from
..fluid.layer_helper
import
LayerHelper
from
..fluid.data_feeder
import
convert_dtype
,
check_variable_and_dtype
,
check_type
,
check_dtype
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.layers.tensor
import
fill_constant
from
..fluid.io
import
shuffle
#DEFINE_ALIAS
from
..fluid.io
import
shuffle
#DEFINE_ALIAS
...
@@ -206,36 +206,23 @@ def randint(low,
...
@@ -206,36 +206,23 @@ def randint(low,
return
out
return
out
def
randn
(
shape
,
def
randn
(
shape
,
dtype
=
None
,
name
=
None
):
out
=
None
,
dtype
=
None
,
device
=
None
,
stop_gradient
=
True
,
name
=
None
):
"""
"""
:alias_main: paddle.randn
:alias_main: paddle.randn
:alias: paddle.randn,paddle.tensor.randn,paddle.tensor.random.randn
:alias: paddle.randn,paddle.tensor.randn,paddle.tensor.random.randn
This function returns a tensor filled with random numbers from a normal
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).
distribution).
Args:
Args:
shape(list|tuple): Shape of the generated random tensor.
shape(list|tuple|Variable): Shape of the Tensor to be created. The data
out(Variable, optional): Optional output which can be any created Variable
type is ``int32`` or ``int64`` . If ``shape`` is a list or tuple,
that meets the requirements to store the result of operation. If the
the elements of it should be integers or Tensors with shape [1]. If
out is `None`, a new Variable will be returned to store the result.
``shape`` is a Variable, it should be an 1-D Tensor .
Default is None.
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the output
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
tensor, which can be float32, float64. if dtype is `None` , the data
type of output tensor is `float32` . Default is None.
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.
name(str, optional): Normally there is no need for user to set this property.
name(str, optional): Normally there is no need for user to set this property.
For more information, please refer to :ref:`api_guide_Name` .
For more information, please refer to :ref:`api_guide_Name` .
Default is None.
Default is None.
...
@@ -244,75 +231,50 @@ def randn(shape,
...
@@ -244,75 +231,50 @@ def randn(shape,
Random tensor whose data is drawn from a standard normal distribution,
Random tensor whose data is drawn from a standard normal distribution,
dtype: flaot32 or float64 as specified.
dtype: flaot32 or float64 as specified.
Return type:
Return type: Variable
Variable
Raises:
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.
TypeError: If the data type of `dtype` is not float32 or float64.
ValueError: If the length of `shape` is not bigger than 0.
ValueError: If the length of `shape` is not bigger than 0.
Examples:
Examples:
.. code-block:: python
.. code-block:: python
# declarative mode
import paddle
import paddle
import numpy as np
import paddle.fluid as fluid
data = paddle.randn([2, 4])
paddle.enable_imperative()
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]]
.. 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
# example 2: attr shape is a list which contains tensor Variable.
import paddle
dim_1 = paddle.fill_constant([1], "int64", 2)
import paddle.fluid as fluid
dim_2 = paddle.fill_constant([1], "int32", 3)
import paddle.fluid.dygraph as dg
result_2 = paddle.randn(shape=[dim_1, dim_2, 2])
# [[[-2.8852394 -0.25898588]
place = fluid.CPUPlace()
# [-0.47420555 0.17683524]
with dg.guard(place) as g:
# [-0.7989969 0.00754541]]
x = paddle.randn([2, 4])
# [[ 0.85201347 0.32320443]
x_np = x.numpy()
# [ 1.1399018 0.48336947]
print(x_np)
# [ 0.8086993 0.6868893 ]]]
# [[ 1.5149173 -0.26234224 -0.592486 1.4523455 ]
# [ 0.04581212 -0.85345626 1.1687907 -0.02512913]]
# 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]))
helper
=
LayerHelper
(
"randn"
,
**
locals
()
)
result_3 = paddle.randn(var_shape
)
check_type
(
shape
,
'shape'
,
(
list
,
tuple
),
'randn'
)
# [[-2.878077 0.17099959 0.05111201]
assert
len
(
shape
)
>
0
,
(
"The size of argument(shape) can't be zero."
)
# [-0.3761474 -1.044801 1.1870178 ]]
"""
if
dtype
is
None
:
if
dtype
is
None
:
dtype
=
'float32'
dtype
=
'float32'
check_dtype
(
dtype
,
'create data type'
,
[
'float32'
,
'float64'
],
'randn'
)
out
=
gaussian_random
(
shape
=
shape
,
mean
=
0.0
,
std
=
1.0
,
seed
=
0
,
dtype
=
dtype
,
name
=
name
)
if
out
is
None
:
out
.
stop_gradient
=
True
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
})
return
out
return
out
...
@@ -369,6 +331,7 @@ def randperm(n, dtype="int64", name=None):
...
@@ -369,6 +331,7 @@ def randperm(n, dtype="int64", name=None):
attrs
=
{
'n'
:
n
,
'dtype'
:
dtype
,
'seed'
:
0
}
attrs
=
{
'n'
:
n
,
'dtype'
:
dtype
,
'seed'
:
0
}
helper
.
append_op
(
helper
.
append_op
(
type
=
'randperm'
,
inputs
=
{},
outputs
=
{
'Out'
:
out
},
attrs
=
attrs
)
type
=
'randperm'
,
inputs
=
{},
outputs
=
{
'Out'
:
out
},
attrs
=
attrs
)
out
.
stop_gradient
=
True
return
out
return
out
...
@@ -439,4 +402,7 @@ def rand(shape, dtype=None, name=None):
...
@@ -439,4 +402,7 @@ def rand(shape, dtype=None, name=None):
"""
"""
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
)
out
=
uniform_random
(
shape
,
dtype
,
min
=
0.0
,
max
=
1.0
,
name
=
name
)
out
.
stop_gradient
=
True
return
out
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