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Update api doc std and fc doc

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shanyi15-patch-2
ranqiu 8 years ago
parent a78b760218
commit fc0f92c24f
3 changed files with 30 additions and 46 deletions
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5
doc/fluid/dev/api_doc_std_cn.md
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@ -40,7 +40,7 @@ API文档须包含以下几个模块（排列顺序为文档撰写顺序）：
## 格式及示例
API文档须使用rst格式撰写，该格式详情请参考[链接](http://sphinx-doc-zh.readthedocs.io/en/latest/rest.html)。API文档各模块的内容格式及示例如下（以下以fc为例进行说明）：
API文档须使用reStructuredText格式撰写，该格式详情请参考[链接](http://sphinx-doc-zh.readthedocs.io/en/latest/rest.html)。API文档各模块的内容格式及示例如下（以下以fc为例进行说明）：
- Python API Definition
@ -137,7 +137,8 @@ API文档须使用rst格式撰写，该格式详情请参考[链接](http://sphi
```
Args:
input (Variable|list of Variable): This layer's input tensor(s) which is at least 2-dimensional.
input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
the input tensor(s) is at least 2.
param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
parameters/weights of this layer.
name (str, default None): The name of this layer.

3
doc/fluid/dev/src/fc.py
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@ -48,7 +48,8 @@ def fc(input,
* :math:`Out`: The output tensor.
Args:
input (Variable|list of Variable): This layer's input tensor(s) which is at least 2-dimensional.
input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
the input tensor(s) is at least 2.
size(int): The number of output units in this layer.
num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
two dimensions. If this happens, the multidimensional tensor will first be flattened

68
python/paddle/fluid/layers/nn.py
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@ -85,13 +85,12 @@ def fc(input,
**Fully Connected Layer**
The fully connected layer can take multiple tensors as its inputs. It
creates a variable (one for each input tensor) called weights for each
input tensor, which represents a fully connected weight matrix from
each input unit to each output unit. The fully connected layer
multiplies each input tensor with its coresponding weight to produce
an output Tensor. If multiple input tensors are given, the results of
multiple multiplications will be sumed up. If bias_attr is not None,
a biases variable will be created and added to the output. Finally,
creates a variable called weights for each input tensor, which represents
a fully connected weight matrix from each input unit to each output unit.
The fully connected layer multiplies each input tensor with its coresponding
weight to produce an output Tensor. If multiple input tensors are given,
the results of multiple multiplications will be sumed up. If bias_attr is
not None, a bias variable will be created and added to the output. Finally,
if activation is not None, it will be applied to the output as well.
This process can be formulated as follows:
@ -110,44 +109,27 @@ def fc(input,
* :math:`Out`: The output tensor.
Args:
input(Variable|list): The input tensor(s) to the fully connected layer.
size(int): The number of output units in the fully connected layer.
num_flatten_dims(int): The fc layer can accept an input tensor with more
than two dimensions. If this happens, the
multidimensional tensor will first be flattened
into a 2-dimensional matrix. The parameter
`num_flatten_dims` determines how the input tensor
is flattened: the first `num_flatten_dims`
(inclusive, index starts from 1) dimensions will
be flatten to form the first dimension of the
final matrix (height of the matrix), and the rest
`rank(X) - num_flatten_dims` dimensions are
flattened to form the second dimension of the
final matrix (width of the matrix). For example,
suppose `X` is a 6-dimensional tensor with a shape
[2, 3, 4, 5, 6], and `num_flatten_dims` = 3. Then,
the flattened matrix will have a shape
[2 x 3 x 4, 5 x 6] = [24, 30]. By default,
`num_flatten_dims` is set to 1.
param_attr(ParamAttr|list): The parameter attribute for learnable
parameters/weights of the fully connected
layer.
param_initializer(ParamAttr|list): The initializer used for the
weight/parameter. If set None,
XavierInitializer() will be used.
bias_attr(ParamAttr|list): The parameter attribute for the bias parameter
for this layer. If set None, no bias will be
added to the output units.
bias_initializer(ParamAttr|list): The initializer used for the bias.
If set None, then ConstantInitializer()
will be used.
act(str): Activation to be applied to the output of the fully connected
layer.
name(str): Name/alias of the fully connected layer.
input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
the input tensor(s) is at least 2.
size(int): The number of output units in this layer.
num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
two dimensions. If this happens, the multidimensional tensor will first be flattened
into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
dimensions will be flatten to form the first dimension of the final matrix (height of
the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
form the second dimension of the final matrix (width of the matrix). For example, suppose
`X` is a 6-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
parameters/weights of this layer.
bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
of this layer. If it is set to None, no bias will be added to the output units.
act (str, default None): Activation to be applied to the output of this layer.
name (str, default None): The name of this layer.
Returns:
Variable: The output tensor variable.
A tensor variable storing the transformation result.
Raises:
ValueError: If rank of the input tensor is less than 2.

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