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fix docs (#27637)

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my_2.0rc
yaoxuefeng 4 years ago committed by GitHub
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5 changed files with 34 additions and 73 deletions
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10
python/paddle/fluid/layers/nn.py
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@ -3246,7 +3246,7 @@ def data_norm(input,
y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
Args: Args:
input(variable): The input variable which is a LoDTensor. input(Tensor): The input Tensor.
act(string, Default None): Activation type, linear|relu|prelu|... act(string, Default None): Activation type, linear|relu|prelu|...
epsilon(float, Default 1e-05): epsilon(float, Default 1e-05):
param_attr(ParamAttr): The parameter attribute for Parameter `scale`. param_attr(ParamAttr): The parameter attribute for Parameter `scale`.
@ -3274,16 +3274,16 @@ def data_norm(input,
enable_scale_and_shift(bool, Default False): do scale&shift after normalization. enable_scale_and_shift(bool, Default False): do scale&shift after normalization.
Returns: Returns:
Variable: A tensor variable which is the result after applying data normalization on the input. Tensor: A tensor which is the result after applying data normalization on the input.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle
hidden1 = fluid.data(name="hidden1", shape=[64, 200]) x = paddle.randn(shape=[32,100])
hidden2 = fluid.layers.data_norm(name="hidden2", input=hidden1) hidden2 = paddle.static.nn.data_norm(input=x)
""" """
helper = LayerHelper('data_norm', **locals()) helper = LayerHelper('data_norm', **locals())
dtype = helper.input_dtype() dtype = helper.input_dtype()

41
python/paddle/tensor/creation.py
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@ -807,56 +807,31 @@ def meshgrid(*args, **kwargs):
vector, and creates N-dimensional grids. vector, and creates N-dimensional grids.
Args: Args:
*args(Variable|list of Variable) : tensors (tuple(list) of tensor): the shapes of input k tensors are (N1,), *args(Tensor|list of Tensor) : tensors (tuple(list) of tensor): the shapes of input k tensors are (N1,),
(N2,),..., (Nk,). Support data types: ``float64``, ``float32``, ``int32``, ``int64``. (N2,),..., (Nk,). Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
**kwargs (optional): Currently, we only accept name in **kwargs **kwargs (optional): Currently, we only accept name in **kwargs
The default value is None. Normally there is no need for 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`. user to set this property. For more information, please refer to :ref:`api_guide_Name`.
Returns: Returns:
Variable: k tensors. The shape of each tensor is (N1, N2, ..., Nk) Tensor: k tensors. The shape of each tensor is (N1, N2, ..., Nk)
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
import paddle.fluid as fluid
import numpy as np
x = fluid.data(name='x', shape=[100], dtype='int32') x = paddle.randint(low=0, high=100, shape=[100])
y = fluid.data(name='y', shape=[200], dtype='int32') y = paddle.randint(low=0, high=100, shape=[200])
grid_x, grid_y = paddle.meshgrid(x, y)
input_1 = np.random.randint(0, 100, [100, ]).astype('int32') print(grid_x.shape)
input_2 = np.random.randint(0, 100, [200, ]).astype('int32') print(grid_y.shape)
exe = fluid.Executor(place=fluid.CPUPlace())
grid_x, grid_y = paddle.tensor.meshgrid(x, y)
res_1, res_2 = exe.run(fluid.default_main_program(),
feed={'x': input_1,
'y': input_2},
fetch_list=[grid_x, grid_y])
#the shape of res_1 is (100, 200) #the shape of res_1 is (100, 200)
#the shape of res_2 is (100, 200) #the shape of res_2 is (100, 200)
.. code-block:: python
#example 2: in dygraph mode
import paddle
import numpy as np
paddle.disable_static()
input_3 = np.random.randint(0, 100, [100, ]).astype('int32')
input_4 = np.random.randint(0, 100, [200, ]).astype('int32')
tensor_3 = paddle.to_tensor(input_3)
tensor_4 = paddle.to_tensor(input_4)
grid_x, grid_y = paddle.tensor.meshgrid(tensor_3, tensor_4)
#the shape of grid_x is (100, 200)
#the shape of grid_y is (100, 200)
""" """
if len(args) == 1 and isinstance(args[0], (list, tuple)): if len(args) == 1 and isinstance(args[0], (list, tuple)):

8
python/paddle/tensor/linalg.py
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@ -813,19 +813,17 @@ def bmm(x, y, name=None):
if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor. if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.
Args: Args:
x (Variable): The input variable which is a Tensor or LoDTensor. x (Tensor): The input Tensor.
y (Variable): The input variable which is a Tensor or LoDTensor. y (Tensor): The input Tensor.
name(str|None): A name for this layer(optional). If set None, the layer name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically. will be named automatically.
Returns: Returns:
Variable: The product Tensor (or LoDTensor) variable. Tensor: The product Tensor.
Examples: Examples:
import paddle import paddle
paddle.disable_static()
# In imperative mode: # In imperative mode:
# size x: (2, 2, 3) and y: (2, 3, 2) # size x: (2, 2, 3) and y: (2, 3, 2)
x = paddle.to_tensor([[[1.0, 1.0, 1.0], x = paddle.to_tensor([[[1.0, 1.0, 1.0],

23
python/paddle/tensor/manipulation.py
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@ -210,7 +210,7 @@ def flatten(x, start_axis=0, stop_axis=-1, name=None):
Out.shape = (3 * 100 * 100 * 4) Out.shape = (3 * 100 * 100 * 4)
Args: Args:
x (Variable): A tensor of number of dimentions >= axis. A tensor with data type float32, x (Tensor): A tensor of number of dimentions >= axis. A tensor with data type float32,
float64, int8, int32, int64. float64, int8, int32, int64.
start_axis (int): the start axis to flatten start_axis (int): the start axis to flatten
stop_axis (int): the stop axis to flatten stop_axis (int): the stop axis to flatten
@ -218,12 +218,12 @@ def flatten(x, start_axis=0, stop_axis=-1, name=None):
Generally, no setting is required. Default: None. Generally, no setting is required. Default: None.
Returns: Returns:
Variable: A tensor with the contents of the input tensor, with input \ Tensor: A tensor with the contents of the input tensor, with input \
axes flattened by indicated start axis and end axis. \ axes flattened by indicated start axis and end axis. \
A Tensor with data type same as input x. A Tensor with data type same as input x.
Raises: Raises:
ValueError: If x is not a Variable. ValueError: If x is not a Tensor.
ValueError: If start_axis or stop_axis is illegal. ValueError: If start_axis or stop_axis is illegal.
Examples: Examples:
@ -231,20 +231,17 @@ def flatten(x, start_axis=0, stop_axis=-1, name=None):
.. code-block:: python .. code-block:: python
import paddle import paddle
import numpy as np
paddle.disable_static()
image_shape=(2, 3, 4, 4) image_shape=(2, 3, 4, 4)
x = np.arange(image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3]).reshape(image_shape) / 100.
x = x.astype('float32')
img = paddle.to_tensor(x) x = paddle.arange(end=image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3])
img = paddle.reshape(x, image_shape)
out = paddle.flatten(img, start_axis=1, stop_axis=2) out = paddle.flatten(img, start_axis=1, stop_axis=2)
# out shape is [2, 12, 4] # out shape is [2, 12, 4]
""" """
if not (isinstance(x, Variable)): if not (isinstance(x, Variable)):
raise ValueError("The input x should be a Variable") raise ValueError("The input x should be a Tensor")
check_variable_and_dtype( check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int8', 'int32', 'int64'], 'flatten') x, 'x', ['float32', 'float64', 'int8', 'int32', 'int64'], 'flatten')
@ -294,20 +291,18 @@ def roll(x, shifts, axis=None, name=None):
the tensor will be flattened before rolling and then restored to the original shape. the tensor will be flattened before rolling and then restored to the original shape.
Args: Args:
x (Variable): The x tensor variable as input. x (Tensor): The x tensor variable as input.
shifts (int|list|tuple): The number of places by which the elements shifts (int|list|tuple): The number of places by which the elements
of the `x` tensor are shifted. of the `x` tensor are shifted.
axis (int|list|tuple|None): axis(axes) along which to roll. axis (int|list|tuple|None): axis(axes) along which to roll.
Returns: Returns:
Variable: A Tensor with same data type as `x`. Tensor: A Tensor with same data type as `x`.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle import paddle
import paddle.fluid as fluid
paddle.disable_static()
x = paddle.to_tensor([[1.0, 2.0, 3.0], x = paddle.to_tensor([[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0], [4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]]) [7.0, 8.0, 9.0]])

25
python/paddle/tensor/math.py
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@ -931,33 +931,26 @@ def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
$Input$, $x$ and $y$ can carry the LoD (Level of Details) information, or not. But the output only shares the LoD information with input $input$. $Input$, $x$ and $y$ can carry the LoD (Level of Details) information, or not. But the output only shares the LoD information with input $input$.
Args: Args:
input (Variable): The input Tensor/LoDTensor to be added to the final result. input (Tensor): The input Tensor to be added to the final result.
x (Variable): The first input Tensor/LoDTensor for matrix multiplication. x (Tensor): The first input Tensor for matrix multiplication.
y (Variable): The second input Tensor/LoDTensor for matrix multiplication. y (Tensor): The second input Tensor for matrix multiplication.
beta (float): Coefficient of $input$. beta (float): Coefficient of $input$.
alpha (float): Coefficient of $x*y$. alpha (float): Coefficient of $x*y$.
name (str, optional): Name of the output. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None. name (str, optional): Name of the output. 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(Tensor/LoDTensor): The output Tensor/LoDTensor of addmm op. Tensor: The output Tensor of addmm op.
Examples: Examples:
.. code-block:: python .. code-block:: python
import numpy as np
import paddle import paddle
data_x = np.ones((2, 2)).astype(np.float32) x = paddle.ones([2,2])
data_y = np.ones((2, 2)).astype(np.float32) y = paddle.ones([2,2])
data_input = np.ones((2, 2)).astype(np.float32) input = paddle.ones([2,2])
paddle.disable_static()
x = paddle.to_tensor(data_x)
y = paddle.to_tensor(data_y)
input = paddle.to_tensor(data_input)
out = paddle.tensor.addmm( input=input, x=x, y=y, beta=0.5, alpha=5.0 ) out = paddle.addmm( input=input, x=x, y=y, beta=0.5, alpha=5.0 )
print( out.numpy() ) print( out.numpy() )
# [[10.5 10.5] # [[10.5 10.5]

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