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yaoxuefeng6 merged 4 commits into from
Sep 29, 2020
Merged

fix docs #27637

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60643ed
fix docs
yaoxuefeng6 Sep 27, 2020
3d5304a
fix test=document_fix
yaoxuefeng6 Sep 28, 2020
8f991f3
fix demo of flatten and data_norm
yaoxuefeng6 Sep 28, 2020
8ddda24
fix test=document_fix
yaoxuefeng6 Sep 28, 2020
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10 changes: 5 additions & 5 deletions python/paddle/fluid/layers/nn.py
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Expand Up @@ -3246,7 +3246,7 @@ def data_norm(input,
y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift

Args:
input(variable): The input variable which is a LoDTensor.
input(Tensor): The input Tensor.
act(string, Default None): Activation type, linear|relu|prelu|...
epsilon(float, Default 1e-05):
param_attr(ParamAttr): The parameter attribute for Parameter `scale`.
Expand Down Expand Up @@ -3274,16 +3274,16 @@ def data_norm(input,
enable_scale_and_shift(bool, Default False): do scale&shift after normalization.

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:

.. code-block:: python

import paddle.fluid as fluid
import paddle

hidden1 = fluid.data(name="hidden1", shape=[64, 200])
hidden2 = fluid.layers.data_norm(name="hidden2", input=hidden1)
x = paddle.randn(shape=[32,100])
hidden2 = paddle.static.nn.data_norm(input=x)
"""
helper = LayerHelper('data_norm', **locals())
dtype = helper.input_dtype()
Expand Down
41 changes: 8 additions & 33 deletions python/paddle/tensor/creation.py
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Expand Up @@ -807,56 +807,31 @@ def meshgrid(*args, **kwargs):
vector, and creates N-dimensional grids.

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``.
**kwargs (optional): Currently, we only accept name in **kwargs
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: k tensors. The shape of each tensor is (N1, N2, ..., Nk)
Tensor: k tensors. The shape of each tensor is (N1, N2, ..., Nk)

Examples:
.. code-block:: python

import paddle
import paddle.fluid as fluid
import numpy as np

x = fluid.data(name='x', shape=[100], dtype='int32')
y = fluid.data(name='y', shape=[200], dtype='int32')
x = paddle.randint(low=0, high=100, shape=[100])
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')
input_2 = np.random.randint(0, 100, [200, ]).astype('int32')
print(grid_x.shape)
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_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)):
Expand Down
8 changes: 3 additions & 5 deletions python/paddle/tensor/linalg.py
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Expand Up @@ -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.

Args:
x (Variable): The input variable which is a Tensor or LoDTensor.
y (Variable): The input variable which is a Tensor or LoDTensor.
x (Tensor): The input Tensor.
y (Tensor): The input Tensor.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.

Returns:
Variable: The product Tensor (or LoDTensor) variable.
Tensor: The product Tensor.

Examples:
import paddle

paddle.disable_static()

# In imperative mode:
# size x: (2, 2, 3) and y: (2, 3, 2)
x = paddle.to_tensor([[[1.0, 1.0, 1.0],
Expand Down
23 changes: 9 additions & 14 deletions python/paddle/tensor/manipulation.py
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Expand Up @@ -213,41 +213,38 @@ def flatten(x, start_axis=0, stop_axis=-1, name=None):
Out.shape = (3 * 100 * 100 * 4)

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.
start_axis (int): the start axis to flatten
stop_axis (int): the stop axis to flatten
name(str, Optional): For details, please refer to :ref:`api_guide_Name`.
Generally, no setting is required. Default: None.

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. \
A Tensor with data type same as input x.

Raises:
ValueError: If x is not a Variable.
ValueError: If x is not a Tensor.
ValueError: If start_axis or stop_axis is illegal.

Examples:

.. code-block:: python

import paddle
import numpy as np

paddle.disable_static()

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 shape is [2, 12, 4]
"""
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(
x, 'x', ['float32', 'float64', 'int8', 'int32', 'int64'], 'flatten')
Expand Down Expand Up @@ -297,20 +294,18 @@ def roll(x, shifts, axis=None, name=None):
the tensor will be flattened before rolling and then restored to the original shape.

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
of the `x` tensor are shifted.
axis (int|list|tuple|None): axis(axes) along which to roll.

Returns:
Variable: A Tensor with same data type as `x`.
Tensor: A Tensor with same data type as `x`.

Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid

paddle.disable_static()
x = paddle.to_tensor([[1.0, 2.0, 3.0],
[4.0, 5.0, 6.0],
[7.0, 8.0, 9.0]])
Expand Down
25 changes: 9 additions & 16 deletions python/paddle/tensor/math.py
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Expand Up @@ -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 $input$.

Args:
input (Variable): The input Tensor/LoDTensor to be added to the final result.
x (Variable): The first input Tensor/LoDTensor for matrix multiplication.
y (Variable): The second input Tensor/LoDTensor for matrix multiplication.
input (Tensor): The input Tensor to be added to the final result.
x (Tensor): The first input Tensor for matrix multiplication.
y (Tensor): The second input Tensor for matrix multiplication.
beta (float): Coefficient of $input$.
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.

Returns:
Variable(Tensor/LoDTensor): The output Tensor/LoDTensor of addmm op.
Tensor: The output Tensor of addmm op.

Examples:
.. code-block:: python

import numpy as np

import paddle

data_x = np.ones((2, 2)).astype(np.float32)
data_y = np.ones((2, 2)).astype(np.float32)
data_input = np.ones((2, 2)).astype(np.float32)

paddle.disable_static()

x = paddle.to_tensor(data_x)
y = paddle.to_tensor(data_y)
input = paddle.to_tensor(data_input)
x = paddle.ones([2,2])
y = paddle.ones([2,2])
input = paddle.ones([2,2])

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() )
# [[10.5 10.5]
Expand Down