m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Add new API : randn (#23211)

Browse Source 
* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* aAdd new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop

* Add new API : randn

test=develop
revert-23830-2.0-beta
guofei 5 years ago committed by GitHub
parent 8188d83be3
commit 73f421f782
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 222 additions and 3 deletions
Show Stats Download Patch File Download Diff File

2
python/paddle/__init__.py
Unescape View File

@ -87,7 +87,7 @@ from .tensor.logic import elementwise_equal #DEFINE_ALIAS
# from .tensor.random import gaussin #DEFINE_ALIAS
# from .tensor.random import uniform #DEFINE_ALIAS
# from .tensor.random import shuffle #DEFINE_ALIAS
# from .tensor.random import randn #DEFINE_ALIAS
from .tensor.random import randn #DEFINE_ALIAS
from .tensor.random import randperm
# from .tensor.random import rand #DEFINE_ALIAS
from .tensor.random import randint #DEFINE_ALIAS

109
python/paddle/fluid/tests/unittests/test_randn_op.py
Unescape View File

@ -0,0 +1,109 @@
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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
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)
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)
# 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)
# 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)
if __name__ == "__main__":
unittest.main()

2
python/paddle/tensor/__init__.py
Unescape View File

@ -62,7 +62,7 @@ from .logic import elementwise_equal #DEFINE_ALIAS
# from .random import gaussin #DEFINE_ALIAS
# from .random import uniform #DEFINE_ALIAS
# from .random import shuffle #DEFINE_ALIAS
# from .random import randn #DEFINE_ALIAS
from .random import randn #DEFINE_ALIAS
# from .random import rand #DEFINE_ALIAS
from .random import randint #DEFINE_ALIAS
from .random import randperm

111
python/paddle/tensor/random.py
Unescape View File

@ -21,8 +21,10 @@
# 'rand',
# 'randint']
import numpy as np
from ..fluid import core
from ..fluid.framework import device_guard, in_dygraph_mode, _varbase_creator, Variable
from ..fluid.framework import device_guard, in_dygraph_mode, _varbase_creator, Variable, convert_np_dtype_to_dtype_
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
@ -191,6 +193,113 @@ def randint(low,
return out
def randn(shape,
out=None,
dtype=None,
device=None,
stop_gradient=True,
name=None):
"""
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).
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 wiil be returned to store the result.
Default is None.
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.
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:
Random tensor whose data is drawn from a Gaussian distribution,
dtype: flaot32 or float64 as specified.
Return type:
Variable
Raises:
TypeError: If the type of `shape` is not 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
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]]
.. code-block:: python
# 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.")
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
})
return out
@templatedoc()
def randperm(n,
out=None,

1
python/setup.py.in
Unescape View File

@ -113,6 +113,7 @@ packages=['paddle',
'paddle.fluid',
'paddle.tensor',
'paddle.fluid.dygraph',
'paddle.tensor',
'paddle.fluid.dygraph.dygraph_to_static',
'paddle.fluid.proto',
'paddle.fluid.proto.profiler',

Write Preview
Loading…
Cancel
Save