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3d2b2d40
编写于
12月 27, 2017
作者:
C
chengduoZH
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python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
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python/paddle/v2/fluid/layers/nn.py
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3d2b2d40
...
...
@@ -478,8 +478,7 @@ def conv2d(input,
groups
=
None
,
param_attr
=
None
,
bias_attr
=
None
,
act
=
None
,
name
=
None
):
act
=
None
):
"""
**Convlution2D Layer**
...
...
@@ -498,46 +497,51 @@ def conv2d(input,
Out = \sigma (W
\\
ast X + b)
In the above equation:
In the above equation:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`W`: Filter value, a tensor with MCHW format.
* :math:
\\
ast
: Convolution operation.
* :math:
`
\\
ast`
: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:
\\
sigma
: Activation function.
* :math:
`
\\
sigma`
: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
-
Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
-
Output:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Output:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
.. math::
H_{out}=
\\
frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1
W_{out}=
\\
frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
.. math::
All the input variables are passed in as local variables to the LayerHelper
constructor.
H_{out}&=
\\
frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1
\\\\
W_{out}&=
\\
frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Args:
input(Variable): Input tensors. The format of input tensor is NCHW.
num_filters(int): Number of filters
filter_size(list/int): Filter size of Conv2d Layer
stride(list/int, optional): Strides(h_s, w_s) of Conv2d Layer. Default: 1
padding(list/int, optional): Paddings(h_pad, w_pad) of Conv2d Layer. Default: 0
groups(int, optional): The groups number of the Conv2d Layer. Default: 1
param_attr(ParamAttr): The parameters to the Conv2d Layer. Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
act(str): Activation type. Default: None
name(str): Name/alias of the function
input(Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
groups(int): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1
param_attr(ParamAttr): The parameters to the Conv2d Layer. Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
act(str): Activation type. Default: None
Returns:
Variable: The tensor variable storing the convolution and
\
...
...
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