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未验证 提交 0ca1bb56 编写于 作者: Z zhupengyang 提交者： GitHub
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refine docs of rand, randn, randint, randperm, zeros_like, arange (#25633)

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python/paddle/fluid/layers/nn.py
浏览文件 @ 0ca1bb56
......@@ -10427,47 +10427,58 @@ def gaussian_random(shape,
dtype='float32',
name=None):
"""
Generate a random tensor whose data is drawn from a Gaussian distribution.
This OP returns a Tensor filled with random values sampled from a Gaussian
distribution, with ``shape`` and ``dtype``.
Args:
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.
shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape``
is a list or tuple, the elements of it should be integers or Tensors
(with the shape [1], and the data type int32 or int64). If ``shape``
is a Tensor, it should be a 1-D Tensor(with the data type int32 or
int64).
mean(float|int, optional): Mean of the output tensor, default is 0.0.
std(float|int, optional): Standard deviation of the output tensor, default
is 1.0.
seed(int, optional): ${seed_comment}
dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of
the output Tensor. Supported data types: float32, float64.
Default is float32.
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:
Variable: Random tensor whose data is drawn from a Gaussian distribution, dtype: flaot32 or float64 as specified.
Tensor: A Tensor filled with random values sampled from a Gaussian
distribution, with ``shape`` and ``dtype``.
Examples:
.. code-block:: python
import paddle.fluid as fluid
# example 1:
# attr shape is a list which doesn't contain tensor Variable.
# attr shape is a list which doesn't contain Tensor.
result_1 = fluid.layers.gaussian_random(shape=[3, 4])
# [[-0.31261674, 1.8736548, -0.6274357, 0.96988016],
# [-0.12294637, 0.9554768, 1.5690808, -1.2894802 ],
# [-0.60082096, -0.61138713, 1.5345167, -0.21834975]]
# example 2:
# attr shape is a list which contains tensor Variable.
dim_1 = fluid.layers.fill_constant([1],"int64",3)
dim_2 = fluid.layers.fill_constant([1],"int32",5)
# attr shape is a list which contains Tensor.
dim_1 = fluid.layers.fill_constant([1], "int64", 2)
dim_2 = fluid.layers.fill_constant([1], "int32", 3)
result_2 = fluid.layers.gaussian_random(shape=[dim_1, dim_2])
# [[ 0.51398206, -0.3389769, 0.23597084],
# [ 1.0388143, -1.2015356, -1.0499583 ]]
# example 3:
# attr shape is a Variable, the data type must be int64 or int32.
# attr shape is a Tensor, the data type must be int64 or int32.
var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
result_3 = fluid.layers.gaussian_random(var_shape)
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
result_4 = fluid.layers.gaussian_random(var_shape_int32)
# if var_shape's value is [2, 3]
# result_3 is:
# [[-0.12310527, 0.8187662, 1.923219 ]
# [ 0.70721835, 0.5210541, -0.03214082]]
.. code-block:: python
......@@ -10509,8 +10520,10 @@ def gaussian_random(shape,
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)
return core.ops.gaussian_random('shape', shape, 'mean',
float(mean), 'std',
float(std), 'seed', seed, 'dtype',
dtype)
check_type(shape, 'shape', (list, tuple, Variable), 'gaussian_random/randn')
check_dtype(dtype, 'dtype', ['float32', 'float64'], 'gaussian_random/randn')
......@@ -14917,8 +14930,8 @@ def gather_tree(ids, parents):
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
uniform distribution in the range [min, max).
This OP returns a Tensor filled with random values sampled from a uniform
distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``.
Examples:
::
......@@ -14930,30 +14943,33 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0,
result=[[0.8505902, 0.8397286]]
Args:
shape (list|tuple|Variable): The shape of the output Tensor, if the
shape is a list or tuple, its elements can be an integer or a
Tensor with the shape [1], and the type of the Tensor must be
int32 or int64. If the shape is a Variable, it is a 1-D Tensor, and
the type of the Tensor must be int32 or int64.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the
output Tensor. Supported data types: float32, float64. Default: float32.
min (float, optional): The lower bound on the range of random values
to generate, the min is included in the range. Default -1.0.
max (float, optional): The upper bound on the range of random values
to generate, the max is excluded in the range. Default 1.0.
seed (int, optional): Random seed used for generating samples. 0 means
shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape``
is a list or tuple, the elements of it should be integers or Tensors
(with the shape [1], and the data type int32 or int64). If ``shape``
is a Tensor, it should be a 1-D Tensor(with the data type int32 or
int64).
dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of
the output Tensor. Supported data types: float32, float64.
Default is float32.
min(float|int, optional): The lower bound on the range of random values
to generate, ``min`` is included in the range. Default is -1.0.
max(float|int, optional): The upper bound on the range of random values
to generate, ``max`` is excluded in the range. Default is 1.0.
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.
time. Default is 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:
Variable: A Tensor of the specified shape filled with uniform_random values.
Tensor: A Tensor filled with random values sampled from a uniform
distribution in the range [``min``, ``max``), with ``shape`` and ``dtype``.
Raises:
TypeError: The shape type should be list or tuple or variable.
TypeError: If ``shape`` is not list, tuple, Tensor.
TypeError: If ``dtype`` is not float32, float64.
Examples:
.. code-block:: python
......@@ -14961,21 +14977,28 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0,
import paddle.fluid as fluid
# example 1:
# attr shape is a list which doesn't contain tensor Variable.
# attr shape is a list which doesn't contain Tensor.
result_1 = fluid.layers.uniform_random(shape=[3, 4])
# [[ 0.84524226, 0.6921872, 0.56528175, 0.71690357],
# [-0.34646994, -0.45116323, -0.09902662, -0.11397249],
# [ 0.433519, 0.39483607, -0.8660099, 0.83664286]]
# example 2:
# attr shape is a list which contains tensor Variable.
dim_1 = fluid.layers.fill_constant([1],"int64",3)
dim_2 = fluid.layers.fill_constant([1],"int32",5)
# attr shape is a list which contains Tensor.
dim_1 = fluid.layers.fill_constant([1], "int64", 2)
dim_2 = fluid.layers.fill_constant([1], "int32", 3)
result_2 = fluid.layers.uniform_random(shape=[dim_1, dim_2])
# [[-0.9951253, 0.30757582, 0.9899647 ],
# [ 0.5864527, 0.6607096, -0.8886161 ]]
# example 3:
# attr shape is a Variable, the data type must be int64 or int32.
# attr shape is a Tensor, the data type must be int64 or int32.
var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
result_3 = fluid.layers.uniform_random(var_shape)
var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32")
result_4 = fluid.layers.uniform_random(var_shape_int32)
# if var_shape's value is [2, 3]
# result_3 is:
# [[-0.8517412, -0.4006908, 0.2551912 ],
# [ 0.3364414, 0.36278176, -0.16085452]]
"""
if not isinstance(dtype, core.VarDesc.VarType):
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 0ca1bb56
......@@ -1335,35 +1335,38 @@ def isfinite(x):
def range(start, end, step, dtype, name=None):
"""
Return evenly spaced values within a given interval.
This OP returns a 1-D Tensor with spaced values within a given interval.
Values are generated within the half-open interval [start, stop) (in other
words, the interval including start but excluding stop).
Values are generated into the half-open interval [``start``, ``end``) with
the ``step``. (the interval including ``start`` but excluding ``end``).
If dtype is float32 or float64, we advise adding a small epsilon to end to
avoid floating point rounding errors when comparing against end.
If ``dtype`` is float32 or float64, we advise adding a small epsilon to
``end`` to avoid floating point rounding errors when comparing against ``end``.
Parameters:
start(float|int|Variable): Start of interval. The interval includes
this value. If start is Variable, it is a 1-D Tensor with shape [1],
and it's data type should be one of int32, int64, float32, float64.
end(float|int|Variable): End of interval. The interval does not include
this value. When end is Variable, it is a 1-D Tensor with shape [1],
and it's data type should be int32, int64, float32, float64.
step(float|int|Variable): Spacing between values. For any out, this is
the istance between two adjacent values, out[i+1] - out[i].
When end is Variable, it is a 1-D Tensor with shape [1], and it's
data type should be one of int32, int64, float32, float64.
dtype(str|np.dtype|core.VarDesc.VarType): The data type of the output
tensor, can be float32, float64, int32, int64.
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: a 1-D Tensor which is evenly spaced values within a given interval.
Its data type is set by dtype.
Return type: Variable
start(float|int|Tensor): Start of interval. The interval includes this
value. If ``start`` is a Tensor, it is a 1-D Tensor with shape [1],
with data type int32, int64, float32, float64.
end(float|int|Tensor): End of interval. The interval does not include
this value. If ``end`` is a Tensor, it is a 1-D Tensor with shape
[1], with data type int32, int64, float32, float64.
step(float|int|Tensor): Spacing between values. For any out, it is
the istance between two adjacent values, out[i+1] - out[i]. If
``step`` is a Tensor, it is a 1-D Tensor with shape [1], with data
type int32, int64, float32, float64.
dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of the
output tensor. Supported data types: int32, int64, float32, float64.
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:
Tensor: A 1-D Tensor with values from the interval [``start``, ``end``)
taken with common difference ``step`` beginning from ``start``. Its
data type is set by ``dtype``.
Raises:
TypeError: If ``dtype`` is not int32, int64, float32, float64.
examples:
......
python/paddle/fluid/tests/unittests/test_gaussian_random_op.py
浏览文件 @ 0ca1bb56
......@@ -27,18 +27,23 @@ from op_test import OpTest
class TestGaussianRandomOp(OpTest):
def setUp(self):
self.op_type = "gaussian_random"
self.set_attrs()
self.inputs = {}
self.use_mkldnn = False
self.attrs = {
"shape": [123, 92],
"mean": 1.0,
"std": 2.,
"mean": self.mean,
"std": self.std,
"seed": 10,
"use_mkldnn": self.use_mkldnn
}
self.outputs = {'Out': np.zeros((123, 92), dtype='float32')}
def set_attrs(self):
self.mean = 1.0
self.std = 2.
def test_check_output(self):
self.check_output_customized(self.verify_output)
......@@ -57,6 +62,12 @@ class TestGaussianRandomOp(OpTest):
"hist: " + str(hist) + " hist2: " + str(hist2))
class TestMeanStdAreInt(TestGaussianRandomOp):
def set_attrs(self):
self.mean = 1
self.std = 2
# Situation 2: Attr(shape) is a list(with tensor)
class TestGaussianRandomOp_ShapeTensorList(TestGaussianRandomOp):
def setUp(self):
......
python/paddle/fluid/tests/unittests/test_ones_op.py
浏览文件 @ 0ca1bb56
......@@ -28,11 +28,11 @@ import numpy as np
class ApiOnesTest(unittest.TestCase):
def test_paddle_ones(self):
with paddle.program_guard(paddle.Program()):
ones = paddle.ones(shape=[10], dtype="float64")
ones = paddle.ones(shape=[10])
place = paddle.CPUPlace()
exe = paddle.Executor(place)
result, = exe.run(fetch_list=[ones])
expected_result = np.ones(10, dtype="float64")
expected_result = np.ones(10, dtype="float32")
self.assertEqual((result == expected_result).all(), True)
with paddle.program_guard(paddle.Program()):
......
python/paddle/fluid/tests/unittests/test_uniform_random_op.py
浏览文件 @ 0ca1bb56
......@@ -70,6 +70,12 @@ class TestUniformRandomOp_attr_tensorlist(OpTest):
hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))
class TestMaxMinAreInt(TestUniformRandomOp_attr_tensorlist):
def init_attrs(self):
self.attrs = {"min": -5, "max": 10, "seed": 10}
self.output_hist = output_hist
class TestUniformRandomOp_attr_tensorlist_int32(OpTest):
def setUp(self):
self.op_type = "uniform_random"
......
python/paddle/tensor/creation.py
浏览文件 @ 0ca1bb56
......@@ -241,26 +241,29 @@ def zeros(shape, dtype=None, name=None):
def zeros_like(x, dtype=None, name=None):
"""
:alias_main: paddle.zeros_like
:alias: paddle.zeros_like, paddle.tensor.zeros_like, paddle.tensor.creation.zeros_like
:alias: paddle.tensor.zeros_like, paddle.tensor.creation.zeros_like
This function creates a zeros tensor which has identical shape and dtype
with `input`.
This OP returns a Tensor filled with the value 0, with the same shape and
data type (use ``dtype`` if ``dtype`` is not None) as ``x``.
Args:
x(Variable): The input tensor which specifies shape and dtype. The
dtype of input can be bool, float16, float32, float64, int32, int64.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type can
be set bool, float16, float32, float64, int32, int64. The default
value is None, the dtype is the same as input.
x(Tensor): The input tensor which specifies shape and dtype. The
dtype of ``x`` can be bool, float16, float32, float64, int32, int64.
dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of the
output tensor. Supported data types: bool, float16, float32, float64,
int32, int64. If ``dtype`` is None, the data type is the same as ``x``.
Default is None.
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:
out(Variable): The tensor variable storing the output.
Tensor: A Tensor filled with the value 0, with the same shape and
data type (use ``dtype`` if ``dtype`` is not None) as ``x``.
Raise:
TypeError: If dtype is not bool, float16, float32, float64, int32 or int64.
TypeError: If ``dtype`` is not None and is not bool, float16, float32,
float64, int32 or int64.
Examples:
.. code-block:: python
......@@ -271,8 +274,8 @@ def zeros_like(x, dtype=None, name=None):
paddle.enable_imperative()
x = paddle.imperative.to_variable(np.array([1,2,3], dtype='float32'))
out1 = paddle.zeros_like(x) # [1.0, 1.0, 1.0]
out2 = paddle.zeros_like(x, dtype='int32') # [1, 1, 1]
out1 = paddle.zeros_like(x) # [0., 0., 0.]
out2 = paddle.zeros_like(x, dtype='int32') # [0, 0, 0]
"""
return full_like(x=x, fill_value=0, dtype=dtype, name=name)
......@@ -388,45 +391,43 @@ def full(shape, fill_value, dtype=None, name=None):
def arange(start=0, end=None, step=1, dtype=None, name=None):
"""
:alias_main: paddle.arange
:alias: paddle.arange,paddle.tensor.arange,paddle.tensor.creation.arange
:alias: paddle.tensor.arange, paddle.tensor.creation.arange
Return evenly spaced values within a given interval.
This OP returns a 1-D Tensor with spaced values within a given interval.
Values are generated into the half-open interval [start, stop) with the step.
(the interval including start but excluding stop).
Values are generated into the half-open interval [``start``, ``end``) with
the ``step``. (the interval including ``start`` but excluding ``end``).
If dtype is float32 or float64, we advise adding a small epsilon to end to
avoid floating point rounding errors when comparing against end.
If ``dtype`` is float32 or float64, we advise adding a small epsilon to
``end`` to avoid floating point rounding errors when comparing against ``end``.
Parameters:
start(float|int|Variable): Start of interval. The interval includes
this value. If end is None, the half-open interval is [0, start).
If start is Variable, it is a 1-D Tensor with shape [1], and it's
data type should be one of int32, int64, float32, float64. Default
is 0.
end(float|int|Variable, optional): End of interval. The interval does
not include this value. When end is Variable, it is a 1-D Tensor
with shape [1], and it's data type should be one of int32, int64,
float32, float64. If end is None, the half-open interval is [0, start).
Default is None.
step(float|int|Variable, optional): Spacing between values. For any
out, this is the istance between two adjacent values, out[i+1] - out[i].
When end is Variable, it is a 1-D Tensor with shape [1], and it's
data type should be one of int32, int64, float32, float64. Default is 1.
dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of
the output tensor, can be float32, float64, int32, int64. If dtype
is `None` , the data type of out tensor is `int64` . Defaule 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.
start(float|int|Tensor): Start of interval. The interval includes this
value. If ``end`` is None, the half-open interval is [0, ``start``).
If ``start`` is a Tensor, it is a 1-D Tensor with shape [1], with
data type int32, int64, float32, float64. Default is 0.
end(float|int|Tensor, optional): End of interval. The interval does not
include this value. If ``end`` is a Tensor, it is a 1-D Tensor with
shape [1], with data type int32, int64, float32, float64. If ``end``
is None, the half-open interval is [0, ``start``). Default is None.
step(float|int|Tensor, optional): Spacing between values. For any out,
it is the istance between two adjacent values, out[i+1] - out[i].
If ``step`` is a Tensor, it is a 1-D Tensor with shape [1], with
data type int32, int64, float32, float64. Default is 1.
dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of the
output tensor. Supported data types: int32, int64, float32, float64.
If ``dytpe`` is None, the data type is float32. Default is None.
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: a 1-D Tensor which is evenly spaced values within a given interval.
Its data type is set by dtype.
Return type: Variable
Returns:
Tensor: A 1-D Tensor with values from the interval [``start``, ``end``)
taken with common difference ``step`` beginning from ``start``. Its
data type is set by ``dtype``.
Raises:
TypeError: If dtype is not float32, float64, int32 or int64.
TypeError: If ``dtype`` is not int32, int64, float32, float64.
examples:
......
python/paddle/tensor/random.py
浏览文件 @ 0ca1bb56
......@@ -40,38 +40,40 @@ __all__ = [
def randint(low=0, high=None, shape=[1], dtype=None, name=None):
"""
:alias_main: paddle.randint
:alias: paddle.randint,paddle.tensor.randint,paddle.tensor.random.randint
:alias: paddle.tensor.randint, paddle.tensor.random.randint
This function returns a Tensor filled with random integers from the
"discrete uniform" distribution of the specified data type in the interval
[low, high). If high is None (the default), then results are from [0, low).
This OP returns a Tensor filled with random integers from a discrete uniform
distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``.
If ``high`` is None (the default), the range is [0, ``low``).
Args:
low (int): The lower bound on the range of random values to generate,
the low is included in the range.(unless high=None, in which case
this parameter is one above the highest such integer). Default is 0.
high (int, optional): The upper bound on the range of random values to
generate, the high is excluded in the range. Default is None(see
above for behavior if high=None).
shape (list|tuple|Variable, optional): The shape of the output Tensor,
if the shape is a list or tuple, its elements can be an integer or
a Tensor with the shape [1], and the type of the Tensor must be
int32 or int64. If the shape is a Variable, it is a 1-D Tensor,
and the type of the Tensor must be int32 or int64. Default is None.
dtype(np.dtype|core.VarDesc.VarType|str, optional): Data type of the
output Tensor which can be int32, int64. If dtype is `None`, the
data type of created Tensor is `int64`
low(int): The lower bound on the range of random values to generate.
The ``low`` is included in the range. If ``high`` is None, the
range is [0, ``low``). Default is 0.
high(int, optional): The upper bound on the range of random values to
generate, the ``high`` is excluded in the range. Default is None
(see above for behavior if high = None). Default is None.
shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape``
is a list or tuple, the elements of it should be integers or Tensors
(with the shape [1], and the data type int32 or int64). If ``shape``
is a Tensor, it should be a 1-D Tensor(with the data type int32 or
int64). Default is [1].
dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of the
output tensor. Supported data types: int32, int64. If ``dytpe``
is None, the data type is int64. Default is None.
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:
Variable: A Tensor of the specified shape filled with random integers.
Tensor: A Tensor filled with random integers from a discrete uniform
distribution in the range [``low``, ``high``), with ``shape`` and ``dtype``.
Raises:
TypeError: If shape's type is not list, tuple or Variable.
TypeError: If dtype is not int32 or int64.
ValueError: If low is not large then high; If low is 0, and high is None.
TypeError: If ``shape`` is not list, tuple, Tensor.
TypeError: If ``dtype`` is not int32, int64.
ValueError: If ``high`` is not greater then ``low``; If ``high`` is
None, and ``low`` is not greater than 0.
Examples:
.. code-block:: python
......@@ -82,29 +84,28 @@ def randint(low=0, high=None, shape=[1], dtype=None, name=None):
paddle.enable_imperative()
# example 1:
# attr shape is a list which doesn't contain tensor Variable.
# attr shape is a list which doesn't contain Tensor.
result_1 = paddle.randint(low=-5, high=5, shape=[3])
# [0 -3 2]
# [0, -3, 2]
# 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)
# attr shape is a list which contains Tensor.
dim_1 = paddle.fill_constant([1], "int64", 2)
dim_2 = paddle.fill_constant([1], "int32", 3)
result_2 = paddle.randint(low=-5, high=5, shape=[dim_1, dim_2], dtype="int32")
print(result_2.numpy())
# [[ 0 -1 -3]
# [ 4 -2 0]]
# [[0, -1, -3],
# [4, -2, 0]]
# example 3:
# attr shape is a Variable
# attr shape is a Tensor
var_shape = paddle.imperative.to_variable(np.array([3]))
result_3 = paddle.randint(low=-5, high=5, shape=var_shape)
# [-2 2 3]
# [-2, 2, 3]
# example 4:
# data type is int32
result_4 = paddle.randint(low=-5, high=5, shape=[3], dtype='int32')
# [-5 4 -4]
# [-5, 4, -4]
# example 5:
# Input only one parameter
......@@ -152,34 +153,33 @@ def randint(low=0, high=None, shape=[1], dtype=None, name=None):
def randn(shape, dtype=None, name=None):
"""
:alias_main: paddle.randn
:alias: paddle.randn,paddle.tensor.randn,paddle.tensor.random.randn
:alias: paddle.tensor.randn, paddle.tensor.random.randn
This function returns a tensor filled with random numbers from a normal
distribution with mean 0 and standard deviation 1 (also called the standard normal
distribution).
This OP returns a Tensor filled with random values sampled from a normal
distribution with mean 0 and standard deviation 1 (also called the standard
normal distribution), with ``shape`` and ``dtype``.
Args:
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.
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.
shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape``
is a list or tuple, the elements of it should be integers or Tensors
(with the shape [1], and the data type int32 or int64). If ``shape``
is a Tensor, it should be a 1-D Tensor(with the data type int32 or
int64).
dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of the
output tensor. Supported data types: float32, float64. If ``dytpe``
is None, the data type is float32. Default is None.
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:
Random tensor whose data is drawn from a standard normal distribution,
dtype: flaot32 or float64 as specified.
Return type: Variable
Tensor: A Tensor filled with random values sampled from a normal
distribution with mean 0 and standard deviation 1 (also called the
standard normal distribution), with ``shape`` and ``dtype``.
Raises:
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.
TypeError: If ``shape`` is not list, tuple, Tensor.
TypeError: If ``dtype`` is not float32, float64.
Examples:
.. code-block:: python
......@@ -189,27 +189,27 @@ def randn(shape, dtype=None, name=None):
paddle.enable_imperative()
# example 1: attr shape is a list which doesn't contain tensor Variable.
# example 1: attr shape is a list which doesn't contain Tensor.
result_1 = paddle.randn(shape=[2, 3])
# [[-2.923464 0.11934398 -0.51249987]
# [ 0.39632758 0.08177969 0.2692008 ]]
# [[-2.923464 , 0.11934398, -0.51249987],
# [ 0.39632758, 0.08177969, 0.2692008 ]]
# example 2: attr shape is a list which contains tensor Variable.
# example 2: attr shape is a list which contains Tensor.
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.
# [[[-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 Tensor, 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 ]]
# [[-2.878077 , 0.17099959, 0.05111201]
# [-0.3761474, -1.044801 , 1.1870178 ]]
"""
if dtype is None:
......@@ -225,24 +225,27 @@ def randn(shape, dtype=None, name=None):
def randperm(n, dtype="int64", name=None):
"""
:alias_main: paddle.randperm
:alias: paddle.randperm,paddle.tensor.randperm,paddle.tensor.random.randperm
:alias: paddle.tensor.randperm, paddle.tensor.random.randperm
${comment}
This OP returns a 1-D Tensor filled with random permutation values from 0
to n-1, with ``dtype``.
Args:
n(int): The upper bound (exclusive), and it should be greater than 0.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the
output Tensor. Supported data types: int32, int64, float32, float64.
Default: int32.
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.
dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of
the output Tensor. Supported data types: int32, int64, float32,
float64. Default is int64.
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:
${out_comment}.
Tensor: A 1-D Tensor filled with random permutation values from 0
to n-1, with ``dtype``.
Return Type:
${out_type}
Raises:
ValueError: If ``n`` is not greater than 0.
TypeError: If ``dtype`` is not int32, int64, float32, float64.
Examples:
.. code-block:: python
......@@ -252,10 +255,10 @@ def randperm(n, dtype="int64", name=None):
paddle.enable_imperative()
result_1 = paddle.randperm(5)
# [4 1 2 3 0]
# [4, 1, 2, 3, 0]
result_2 = paddle.randperm(7, 'int32')
# [1 6 2 0 4 3 5]
# [1, 6, 2, 0, 4, 3, 5]
"""
if not isinstance(dtype, core.VarDesc.VarType):
......@@ -281,10 +284,10 @@ def randperm(n, dtype="int64", name=None):
def rand(shape, dtype=None, name=None):
"""
:alias_main: paddle.rand
:alias: paddle.rand,paddle.tensor.rand,paddle.tensor.random.rand
:alias: paddle.tensor.rand, paddle.tensor.random.rand
This OP initializes a variable with random values sampled from a
uniform distribution in the range [0, 1).
This OP returns a Tensor filled with random values sampled from a uniform
distribution in the range [0, 1), with ``shape`` and ``dtype``.
Examples:
::
......@@ -296,22 +299,25 @@ def rand(shape, dtype=None, name=None):
result=[[0.8505902, 0.8397286]]
Args:
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 dytpe is `None`,
the data type of created tensor is `float32`
shape(list|tuple|Tensor): The shape of the output Tensor. If ``shape``
is a list or tuple, the elements of it should be integers or Tensors
(with the shape [1], and the data type int32 or int64). If ``shape``
is a Tensor, it should be a 1-D Tensor(with the data type int32 or
int64).
dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of the
output tensor. Supported data types: float32, float64. If ``dytpe``
is None, the data type is float32. Default is None.
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:
Variable: A Tensor of the specified shape filled with random numbers
from a uniform distribution on the interval [0, 1).
Tensor: A Tensor filled with random values sampled from a uniform
distribution in the range [0, 1), with ``shape`` and ``dtype``.
Raises:
TypeError: The shape type should be list or tupple or Variable.
TypeError: If ``shape`` is not list, tuple, Tensor.
ValueError: If ``dtype`` is not float32, float64.
Examples:
.. code-block:: python
......@@ -320,27 +326,27 @@ def rand(shape, dtype=None, name=None):
import numpy as np
paddle.enable_imperative()
# example 1: attr shape is a list which doesn't contain tensor Variable.
# example 1: attr shape is a list which doesn't contain Tensor.
result_1 = paddle.rand(shape=[2, 3])
# [[0.451152 , 0.55825245, 0.403311 ],
# [0.22550228, 0.22106001, 0.7877319 ]]
# example 2: attr shape is a list which contains tensor Variable.
# example 2: attr shape is a list which contains Tensor.
dim_1 = paddle.fill_constant([1], "int64", 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.
# [[[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 Tensor, 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 ]]
# [[0.22920267, 0.841956 , 0.05981819],
# [0.4836288 , 0.24573246, 0.7516129 ]]
"""
if dtype is None:
......
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