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体验新版 GitCode,发现更多精彩内容 >>
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e902c36c
编写于
12月 21, 2017
作者:
C
chengduoZH
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add conv2d_python doc
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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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@@ -481,11 +481,67 @@ def conv2d(input,
act
=
None
,
name
=
None
):
"""
This function creates the op for a 2-dimensional Convolution.
This is performed using the parameters of filters(size, dimensionality etc)
, stride and other configurations for a Convolution operation.
This funciton can also append an activation on top of the
conv-2d output, if mentioned in the input parameters.
**Convlution2D Layer**
The convolution2D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and Output(Output)
are in NCHW format. Where N is batch size, C is the number of channels, H is the height
of the feature, and W is the width of the feature.
The details of convolution layer, please refer UFLDL's `convolution,
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_ .
If bias_attr and activation type are provided, bias is added to the output of the convolution,
and the corresponding activation function is applied to the final result.
For each input :math:`X`, the equation is:
.. math::
Out = \sigma (W
\a
st X + b)
In the above equation:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`W`: Filter value, a tensor with MCHW format.
* :math:`b`: Bias, .
* :math:\sigma : Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
Output:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
$$
H_{out}=
\\
frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1
\\
W_{out}=
\\
frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
$$
All the input variables are passed in as local variables to the LayerHelper
constructor.
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
Returns:
Variable: The tensor variable storing the convolution and
\
non-linearity activation result.
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[3,32, 32], dtype='float32')
conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
"""
if
stride
is
None
:
...
...
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