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fix multiple documentation errors, test=document_fix (#29210)

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* fix multiple documentation error, test=document_fix

* fix more rst syntax errors, test=document_fix

* fix format issues in docstring, test=document_fix
revert-31562-mean
Feiyu Chan 4 years ago committed by GitHub
parent 074065e5de
commit f7cdcefa65
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3 changed files with 146 additions and 317 deletions
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101
python/paddle/nn/functional/extension.py
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@ -27,9 +27,6 @@ from ...fluid.layers.layer_function_generator import templatedoc
def diag_embed(input, offset=0, dim1=-2, dim2=-1):
"""
:alias_main: paddle.nn.functional.diag_embed
:alias: paddle.nn.functional.diag_embed,paddle.nn.functional.extension.diag_embed
This OP creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2)
are filled by ``input``. By default, a 2D plane formed by the last two dimensions
of the returned tensor will be selected.
@ -41,60 +38,59 @@ def diag_embed(input, offset=0, dim1=-2, dim2=-1):
- If offset < 0, it is below the main diagonal.
Args:
input(Variable|numpy.ndarray): The input tensor. Must be at least 1-dimensional. The input data type should be float32, float64, int32, int64.
input(Tensor|numpy.ndarray): The input tensor. Must be at least 1-dimensional. The input data type should be float32, float64, int32, int64.
offset(int, optional): Which diagonal to consider. Default: 0 (main diagonal).
dim1(int, optional): The first dimension with respect to which to take diagonal. Default: -2.
dim2(int, optional): The second dimension with respect to which to take diagonal. Default: -1.
Returns:
Variable, the output data type is the same as input data type.
Tensor, the output data type is the same as input data type.
Examples:
.. code-block:: python
import paddle.nn.functional as F
import paddle.fluid.dygraph as dg
import numpy as np
diag_embed = np.random.randn(2, 3).astype('float32')
# [[ 0.7545889 , -0.25074545, 0.5929117 ],
# [-0.6097662 , -0.01753256, 0.619769 ]]
with dg.guard():
data1 = F.diag_embed(diag_embed)
data1.numpy()
# [[[ 0.7545889 , 0. , 0. ],
# [ 0. , -0.25074545, 0. ],
# [ 0. , 0. , 0.5929117 ]],
# [[-0.6097662 , 0. , 0. ],
# [ 0. , -0.01753256, 0. ],
# [ 0. , 0. , 0.619769 ]]]
data2 = F.diag_embed(diag_embed, offset=-1, dim1=0, dim2=2)
data2.numpy()
# [[[ 0. , 0. , 0. , 0. ],
# [ 0.7545889 , 0. , 0. , 0. ],
# [ 0. , -0.25074545, 0. , 0. ],
# [ 0. , 0. , 0.5929117 , 0. ]],
#
# [[ 0. , 0. , 0. , 0. ],
# [-0.6097662 , 0. , 0. , 0. ],
# [ 0. , -0.01753256, 0. , 0. ],
# [ 0. , 0. , 0.619769 , 0. ]]]
data3 = F.diag_embed(diag_embed, offset=1, dim1=0, dim2=2)
data3.numpy()
# [[[ 0. , 0.7545889 , 0. , 0. ],
# [ 0. , -0.6097662 , 0. , 0. ]],
#
# [[ 0. , 0. , -0.25074545, 0. ],
# [ 0. , 0. , -0.01753256, 0. ]],
#
# [[ 0. , 0. , 0. , 0.5929117 ],
# [ 0. , 0. , 0. , 0.619769 ]],
#
# [[ 0. , 0. , 0. , 0. ],
# [ 0. , 0. , 0. , 0. ]]]
data1 = F.diag_embed(diag_embed)
data1.numpy()
# [[[ 0.7545889 , 0. , 0. ],
# [ 0. , -0.25074545, 0. ],
# [ 0. , 0. , 0.5929117 ]],
# [[-0.6097662 , 0. , 0. ],
# [ 0. , -0.01753256, 0. ],
# [ 0. , 0. , 0.619769 ]]]
data2 = F.diag_embed(diag_embed, offset=-1, dim1=0, dim2=2)
data2.numpy()
# [[[ 0. , 0. , 0. , 0. ],
# [ 0.7545889 , 0. , 0. , 0. ],
# [ 0. , -0.25074545, 0. , 0. ],
# [ 0. , 0. , 0.5929117 , 0. ]],
#
# [[ 0. , 0. , 0. , 0. ],
# [-0.6097662 , 0. , 0. , 0. ],
# [ 0. , -0.01753256, 0. , 0. ],
# [ 0. , 0. , 0.619769 , 0. ]]]
data3 = F.diag_embed(diag_embed, offset=1, dim1=0, dim2=2)
data3.numpy()
# [[[ 0. , 0.7545889 , 0. , 0. ],
# [ 0. , -0.6097662 , 0. , 0. ]],
#
# [[ 0. , 0. , -0.25074545, 0. ],
# [ 0. , 0. , -0.01753256, 0. ]],
#
# [[ 0. , 0. , 0. , 0.5929117 ],
# [ 0. , 0. , 0. , 0.619769 ]],
#
# [[ 0. , 0. , 0. , 0. ],
# [ 0. , 0. , 0. , 0. ]]]
"""
inputs = {'Input': [input]}
attrs = {'offset': offset, 'dim1': dim1, 'dim2': dim2}
@ -151,15 +147,15 @@ def row_conv(input, weight, act=None):
${comment}
Args:
input (Variable): the input(X) is a LodTensor or tensor, LodTensor(X)
supports variable time-length input sequences. The underlying
input (Tensor): the input(X) is a LodTensor or tensor, LodTensor(X)
supports variable time-length input sequences. The underlying
tensor in this LoDTensor is a matrix with shape (T, D), where
T is the total time steps in this mini-batch and D is the input
data dimension.
If the input is a padded minibatch, the shape of the input is
(N, T, D), N is batch size, T is the max time steps in the batch,
D is the input data dimension.
weight (Variable): The weight. A Tensor with shape
weight (Tensor): The weight. A Tensor with shape
(future_context_size + 1, D), where future_context_size is the
context size of the RowConv operator.
act (str): Non-linear activation to be applied to output variable.
@ -171,7 +167,6 @@ def row_conv(input, weight, act=None):
.. code-block:: python
from paddle import fluid, nn
import paddle.fluid.dygraph as dg
import paddle.nn.functional as F
import numpy as np
@ -182,16 +177,12 @@ def row_conv(input, weight, act=None):
x = np.random.randn(batch_size, time_steps, feature_size).astype(np.float32)
weight = np.random.randn(context_size + 1, feature_size).astype(np.float32)
place = fluid.CPUPlace()
with dg.guard(place):
x_var = dg.to_variable(x)
w_var = dg.to_variable(weight)
y_var = F.extension.row_conv(x_var, w_var)
y_np = y_var.numpy()
print(y_np.shape)
x_var = paddle.to_tensor(x)
w_var = paddle.to_tensor(weight)
y_var = F.extension.row_conv(x_var, w_var)
print(y_var.shape)
# (4, 8, 6)
# [4, 8, 6]
"""
if in_dygraph_mode():

21
python/paddle/nn/layer/extension.py
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@ -20,9 +20,6 @@ from .. import functional as F
class RowConv(layers.Layer):
"""
:alias_main: paddle.nn.RowConv
:alias: paddle.nn.RowConv,paddle.nn.layer.RowConv,paddle.nn.layer.extension.RowConv
**Row-convolution operator**
The row convolution is called lookahead convolution. This operator was
@ -50,7 +47,7 @@ class RowConv(layers.Layer):
of convolution kernel is [future_context_size + 1, D].
param_attr (ParamAttr): Attributes of parameters, including
name, initializer etc. Default: None.
act (str): Non-linear activation to be applied to output variable. Default: None.
act (str): Non-linear activation to be applied to output tensor. Default: None.
dtype (str, optional): Data type, it can be "float32". Default: "float32".
Attributes:
@ -63,8 +60,7 @@ class RowConv(layers.Layer):
Examples:
.. code-block:: python
from paddle import fluid, nn
import paddle.fluid.dygraph as dg
from paddle import nn
import paddle.nn.functional as F
import numpy as np
@ -75,15 +71,12 @@ class RowConv(layers.Layer):
x = np.random.randn(batch_size, time_steps, feature_size).astype(np.float32)
place = fluid.CPUPlace()
with dg.guard(place):
x_var = dg.to_variable(x)
conv = nn.RowConv(feature_size, context_size)
y_var = conv(x_var)
y_np = y_var.numpy()
print(y_np.shape)
x = paddle.to_tensor(x)
conv = nn.RowConv(feature_size, context_size)
y = conv(x)
print(y.shape)
# (4, 8, 6)
# [4, 8, 6]
"""
def __init__(self,

341
python/paddle/nn/layer/rnn.py
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