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1d936f1d
编写于
12月 23, 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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...
...
@@ -489,34 +489,40 @@ def conv2d(input,
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,
If bias
attribution
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)
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:`b`: Bias, .
* :math:\sigma : Activation function.
* :math:
\\
ast : Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:
\\
sigma : Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
Input:
-
Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
Output:
- Output:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
$$
H_{out}=
\\
frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1
\\
.. math::
H_{out}=
\\
frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1
W_{out}=
\\
frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
$$
All the input variables are passed in as local variables to the LayerHelper
constructor.
...
...
@@ -537,10 +543,13 @@ def conv2d(input,
Variable: The tensor variable storing the convolution and
\
non-linearity activation result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[3,32, 32], dtype='float32')
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")
"""
...
...
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