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modify some cn doc #2706

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16 changes: 10 additions & 6 deletions doc/fluid/api_cn/fluid_cn/in_dygraph_mode_cn.rst
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Expand Up @@ -3,14 +3,19 @@
in_dygraph_mode
-------------------------------

.. py:function:: paddle.fluid.in_dygraph_mode()
.. py:function:: paddle.in_dynamic_mode()




该接口检查程序是否在动态图模式中运行。
从paddle2.0开始，默认开启动态图模式。
可以通过 ``fluid.dygraph.guard`` 接口开启动态图模式。

注意：
``paddle.in_dynamic_mode`` 是 ``fluid.in_dygraph_mode``　的別名,
我们推荐使用　``paddle.in_dynamic_mode。

返回：如果程序是在动态图模式下运行的，则返回 ``True``。

返回类型：bool
Expand All @@ -19,11 +24,10 @@ in_dygraph_mode

.. code-block:: python

import paddle.fluid as fluid
import paddle

fluid.enable_dygraph() # 现在进入 dygragh 模式
print(fluid.in_dygraph_mode()) # True
fluid.disable_dygraph()
print(fluid.in_dygraph_mode()) # False
print(paddle.in_dynamic_mode()) # True
paddle.enable_static()
print(paddle.in_dynamic_mode()) # False


12 changes: 6 additions & 6 deletions doc/fluid/api_cn/layers_cn/bilinear_tensor_product_cn.rst
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Expand Up @@ -4,7 +4,7 @@ bilinear_tensor_product
-------------------------------


.. py:function:: paddle.fluid.layers.bilinear_tensor_product(x, y, size, act=None, name=None, param_attr=None, bias_attr=None)
.. py:function:: paddle.static.nn.bilinear_tensor_product(x, y, size, act=None, name=None, param_attr=None, bias_attr=None)

:api_attr: 声明式编程模式（静态图)

Expand Down Expand Up @@ -41,11 +41,11 @@ bilinear_tensor_product

.. code-block:: python

import paddle.fluid as fluid
layer1 = fluid.layers.data("t1", shape=[-1, 5], dtype="float32")
layer2 = fluid.layers.data("t2", shape=[-1, 4], dtype="float32")
tensor = fluid.layers.bilinear_tensor_product(x=layer1, y=layer2, size=1000)

import paddle
paddle.enable_static()
layer1 = paddle.static.data("t1", shape=[-1, 5], dtype="float32")
layer2 = paddle.static.data("t2", shape=[-1, 4], dtype="float32")
tensor = paddle.static.nn.bilinear_tensor_product(x=layer1, y=layer2, size=1000)



11 changes: 5 additions & 6 deletions doc/fluid/api_cn/unique_name_cn/generate_cn.rst
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Expand Up @@ -3,7 +3,7 @@
generate
-------------------------------

.. py:function:: paddle.fluid.unique_name.generate(key)
.. py:function:: paddle.util.unique_name.generate(key)



Expand All @@ -21,9 +21,8 @@ generate

.. code-block:: python

import paddle.fluid as fluid
name1 = fluid.unique_name.generate('fc')
name2 = fluid.unique_name.generate('fc')
print(name1, name2) # fc_0, fc_1

import paddle
name1 = paddle.utils.unique_name.generate('fc')
name2 = paddle.utils.unique_name.generate('fc')
print(name1, name2) # fc_0, fc_1

26 changes: 13 additions & 13 deletions doc/fluid/api_cn/unique_name_cn/guard_cn.rst
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Expand Up @@ -3,7 +3,7 @@
guard
-------------------------------

.. py:function:: paddle.fluid.unique_name.guard(new_generator=None)
.. py:function:: paddle.util.unique_name.guard(new_generator=None)



Expand All @@ -19,17 +19,17 @@ guard

.. code-block:: python

import paddle.fluid as fluid
with fluid.unique_name.guard():
name_1 = fluid.unique_name.generate('fc')
with fluid.unique_name.guard():
name_2 = fluid.unique_name.generate('fc')
print(name_1, name_2) # fc_0, fc_0
with fluid.unique_name.guard('A'):
name_1 = fluid.unique_name.generate('fc')
with fluid.unique_name.guard('B'):
name_2 = fluid.unique_name.generate('fc')
print(name_1, name_2) # Afc_0, Bfc_0
import paddle
with paddle.utils.unique_name.guard():
name_1 = paddle.utils.unique_name.generate('fc')
with paddle.utils.unique_name.guard():
name_2 = paddle.utils.unique_name.generate('fc')
print(name_1, name_2) # fc_0, fc_0

with paddle.utils.unique_name.guard('A'):
name_1 = paddle.utils.unique_name.generate('fc')
with paddle.utils.unique_name.guard('B'):
name_2 = paddle.utils.unique_name.generate('fc')
print(name_1, name_2) # Afc_0, Bfc_0


26 changes: 13 additions & 13 deletions doc/fluid/api_cn/unique_name_cn/switch_cn.rst
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Expand Up @@ -3,7 +3,7 @@
switch
-------------------------------

.. py:function:: paddle.fluid.unique_name.switch(new_generator=None)
.. py:function:: paddle.util.unique_name.switch(new_generator=None)



Expand All @@ -21,15 +21,15 @@ switch

.. code-block:: python

import paddle.fluid as fluid
name1 = fluid.unique_name.generate('fc')
name2 = fluid.unique_name.generate('fc')
print(name1, name2) # fc_0, fc_1
pre_generator = fluid.unique_name.switch() # 切换到新命名空间
name2 = fluid.unique_name.generate('fc')
print(name2) # fc_0

fluid.unique_name.switch(pre_generator) # 切换回原命名空间
name3 = fluid.unique_name.generate('fc')
print(name3) # fc_2, 因为原命名空间已生成fc_0, fc_1
import paddle
name1 = paddle.utils.unique_name.generate('fc')
name2 = paddle.utils.unique_name.generate('fc')
print(name1, name2) # fc_0, fc_1

pre_generator, pre_dygraph_name_checker = paddle.utils.unique_name.switch() # switch to a new anonymous namespace.
name2 = paddle.utils.unique_name.generate('fc')
print(name2) # fc_0

paddle.utils.unique_name.switch(pre_generator, pre_dygraph_name_checker) # switch back to pre_generator.
name3 = paddle.utils.unique_name.generate('fc')
print(name3) # fc_2, since pre_generator has generated fc_0, fc_1
13 changes: 6 additions & 7 deletions doc/paddle/api/paddle/fluid/dygraph/container/LayerList_cn.rst
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Expand Up @@ -19,18 +19,17 @@ LayerList用于保存子层列表，它包含的子层将被正确地注册和

.. code-block:: python

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

class MyLayer(fluid.Layer):
class MyLayer(paddle.nn.Layer):
def __init__(self):
super(MyLayer, self).__init__()
self.linears = fluid.dygraph.LayerList(
[fluid.dygraph.Linear(10, 10) for i in range(10)])
self.linears = paddle.nn.LayerList(
[paddle.nn.Linear(10, 10) for i in range(10)])

def forward(self, x):
# LayerList可以像iterable一样迭代，也可以使用int索引
# LayerList can act as an iterable, or be indexed using ints
for i, l in enumerate(self.linears):
x = self.linears[i // 2](x) + l(x)
return x


44 changes: 22 additions & 22 deletions doc/paddle/api/paddle/fluid/dygraph/container/ParameterList_cn.rst
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Expand Up @@ -19,16 +19,17 @@ ParameterList

.. code-block:: python

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

class MyLayer(fluid.Layer):
class MyLayer(paddle.nn.Layer):
def __init__(self, num_stacked_param):
super(MyLayer, self).__init__()
# 使用可迭代的 Parameters 创建 ParameterList
self.params = fluid.dygraph.ParameterList(
[fluid.layers.create_parameter(
# create ParameterList with iterable Parameters
self.params = paddle.nn.ParameterList(
[paddle.create_parameter(
shape=[2, 2], dtype='float32')] * num_stacked_param)

def forward(self, x):
for i, p in enumerate(self.params):
tmp = self._helper.create_variable_for_type_inference('float32')
Expand All @@ -38,25 +39,24 @@ ParameterList
"Y": p},
outputs={"Out": tmp},
attrs={"x_num_col_dims": 1,
"y_num_col_dims": 1})
"y_num_col_dims": 1})
x = tmp
return x

data_np = np.random.uniform(-1, 1, [5, 2]).astype('float32')
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(data_np)
num_stacked_param = 4
model = MyLayer(num_stacked_param)
print(len(model.params)) # 4
res = model(x)
print(res.shape) # [5, 2]
replaced_param = fluid.layers.create_parameter(shape=[2, 3], dtype='float32')
model.params[num_stacked_param - 1] = replaced_param # 替换最后一个参数
res = model(x)
print(res.shape) # [5, 3]
model.params.append(fluid.layers.create_parameter(shape=[3, 4], dtype='float32')) # 添加参数
print(len(model.params)) # 5
res = model(x)
print(res.shape) # [5, 4]

x = paddle.to_tensor(data_np)
num_stacked_param = 4
model = MyLayer(num_stacked_param)
print(len(model.params)) # 4
res = model(x)
print(res.shape) # [5, 2]

replaced_param = paddle.create_parameter(shape=[2, 3], dtype='float32')
model.params[num_stacked_param - 1] = replaced_param # replace last param
res = model(x)
print(res.shape) # [5, 3]
model.params.append(paddle.create_parameter(shape=[3, 4], dtype='float32')) # append param
print(len(model.params)) # 5
res = model(x)
print(res.shape) # [5, 4]

38 changes: 19 additions & 19 deletions doc/paddle/api/paddle/fluid/dygraph/container/Sequential_cn.rst
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Expand Up @@ -3,7 +3,7 @@
Sequential
-------------------------------

.. py:class:: paddle.fluid.dygraph.Sequential(*layers)
.. py:class:: paddle.nn.Sequential(*layers)



Expand All @@ -19,24 +19,24 @@ Sequential

.. code-block:: python

import paddle.fluid as fluid
import paddle
import numpy as np
data = np.random.uniform(-1, 1, [30, 10]).astype('float32')
with fluid.dygraph.guard():
data = fluid.dygraph.to_variable(data)
# 使用 iterable Layers 创建 Sequential 容器
model1 = fluid.dygraph.Sequential(
fluid.Linear(10, 1), fluid.Linear(1, 2)
)
model1[0] # 访问第一个子层
res1 = model1(data) # 顺序执行
# 使用 iterable name Layer 对创建 Sequential 容器
model2 = fluid.dygraph.Sequential(
('l1', fluid.Linear(10, 2)),
('l2', fluid.Linear(2, 3))
)
model2['l1'] # 访问 l1 子层
model2.add_sublayer('l3', fluid.Linear(3, 3)) # 添加子层
res2 = model2(data) # 顺序执行

data = np.random.uniform(-1, 1, [30, 10]).astype('float32')
data = paddle.to_tensor(data)
# create Sequential with iterable Layers
model1 = paddle.nn.Sequential(
paddle.nn.Linear(10, 1), paddle.nn.Linear(1, 2)
)
model1[0] # access the first layer
res1 = model1(data) # sequential execution

# create Sequential with name Layer pairs
model2 = paddle.nn.Sequential(
('l1', paddle.nn.Linear(10, 2)),
('l2', paddle.nn.Linear(2, 3))
)
model2['l1'] # access l1 layer
model2.add_sublayer('l3', paddle.nn.Linear(3, 3)) # add sublayer
res2 = model2(data) # sequential execution