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randn API: remove out, devive, stop_gradient; add name (#25409)

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fix_copy_if_different
zhupengyang 5 years ago committed by GitHub
parent 41d2247275
commit 25029254bc
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3 changed files with 127 additions and 166 deletions
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44
python/paddle/fluid/layers/nn.py
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@ -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.
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): Output data type, float32 or float64.
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)

121
python/paddle/fluid/tests/unittests/test_randn_op.py
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@ -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__":

118
python/paddle/tensor/random.py
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@ -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 uniform_random, utils
from ..fluid.layers import utils, 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

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