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体验新版 GitCode,发现更多精彩内容 >>
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d1a8498b
编写于
6月 17, 2018
作者:
Q
qiaolongfei
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Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into update-api-reference-1
上级
a4ee0d0d
ea03a228
变更
2
隐藏空白更改
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并排
Showing
2 changed file
with
56 addition
and
53 deletion
+56
-53
paddle/fluid/operators/slice_op.cc
paddle/fluid/operators/slice_op.cc
+20
-17
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+36
-36
未找到文件。
paddle/fluid/operators/slice_op.cc
浏览文件 @
d1a8498b
...
...
@@ -95,23 +95,26 @@ of that dimension. If the value passed to start or end is larger than
the n (the number of elements in this dimension), it represents n.
For slicing to the end of a dimension with unknown size, it is recommended
to pass in INT_MAX. If axes are omitted, they are set to [0, ..., ndim-1].
Example 1:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
axes = [0, 1]
starts = [1, 0]
ends = [2, 3]
Then:
result = [ [5, 6, 7], ]
Example 2:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
starts = [0, 1]
ends = [-1, 1000]
Then:
result = [ [2, 3, 4], ]
Following examples will explain how slice works:
.. code-block:: text
Cast1:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
axes = [0, 1]
starts = [1, 0]
ends = [2, 3]
Then:
result = [ [5, 6, 7], ]
Cast2:
Given:
data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
starts = [0, 1]
ends = [-1, 1000]
Then:
result = [ [2, 3, 4], ]
)DOC"
);
}
};
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
d1a8498b
...
...
@@ -1373,10 +1373,8 @@ def conv2d(input,
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")
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")
"""
num_channels
=
input
.
shape
[
1
]
...
...
@@ -1478,8 +1476,7 @@ def conv3d(input,
* :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.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
...
...
@@ -1541,10 +1538,8 @@ def conv3d(input,
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 12, 32, 32], dtype='float32')
conv2d = fluid.layers.conv3d(
input=data, num_filters=2, filter_size=3, act="relu")
data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32')
conv3d = fluid.layers.conv3d(input=data, num_filters=2, filter_size=3, act="relu")
"""
l_type
=
'conv3d'
...
...
@@ -2182,32 +2177,36 @@ def conv2d_transpose(input,
represent height and width, respectively. The details of convolution transpose
layer, please refer to the following explanation and references
`therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
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 =
W
\\
ast X
Out =
\sigma (W
\\
ast X + b)
In the above equation
:
Where
:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`W`: Filter value, a tensor with MCHW format.
* :math:`
\\
ast` : Convolution transpose operation.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
* :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 shape:
$(N, C_{in}, H_{in}, W_{in})$
Input shape:
:math:`(N, C_{in}, H_{in}, W_{in})`
Filter shape:
$(C_{in}, C_{out}, H_f, W_f)$
Filter shape:
:math:`(C_{in}, C_{out}, H_f, W_f)`
- Output:
Output shape:
$(N, C_{out}, H_{out}, W_{out})$
Output shape:
:math:`(N, C_{out}, H_{out}, W_{out})`
Where
...
...
@@ -2261,10 +2260,8 @@ def conv2d_transpose(input,
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv2d_transpose(
input=data, num_filters=2, filter_size=3)
data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3)
"""
helper
=
LayerHelper
(
"conv2d_transpose"
,
**
locals
())
if
not
isinstance
(
input
,
Variable
):
...
...
@@ -2344,32 +2341,36 @@ def conv3d_transpose(input,
two elements. These two elements represent height and width, respectively.
The details of convolution transpose layer, please refer to the following
explanation and references `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
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 =
W
\\
ast X
Out =
\sigma (W
\\
ast X + b)
In the above equation:
* :math:`X`: Input value, a tensor with NCDHW format.
* :math:`W`: Filter value, a tensor with MCDHW format.
* :math:`
\\
ast` : Convolution transpose operation.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
* :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 shape:
$(N, C_{in}, D_{in}, H_{in}, W_{in})$
Input shape:
:math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
Filter shape:
$(C_{in}, C_{out}, D_f, H_f, W_f)$
Filter shape:
:math:`(C_{in}, C_{out}, D_f, H_f, W_f)`
- Output:
Output shape:
$(N, C_{out}, D_{out}, H_{out}, W_{out})$
Output shape:
:math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
Where
...
...
@@ -2424,10 +2425,8 @@ def conv3d_transpose(input,
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 12, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv3d_transpose(
input=data, num_filters=2, filter_size=3)
data = fluid.layers.data(name='data', shape=[3, 12, 32, 32], dtype='float32')
conv3d_transpose = fluid.layers.conv3d_transpose(input=data, num_filters=2, filter_size=3)
"""
l_type
=
"conv3d_transpose"
helper
=
LayerHelper
(
l_type
,
**
locals
())
...
...
@@ -4778,8 +4777,8 @@ def mean_iou(input, label, num_classes):
IOU is defined as follows:
.. math::
IOU =
true_positive / (true_positive + false_positive + false_negative).
IOU =
\\
frac{true\_positiv}{(true\_positive + false\_positive + false\_negative)}.
The predictions are accumulated in a confusion matrix and mean-IOU
is then calculated from it.
...
...
@@ -4787,8 +4786,9 @@ def mean_iou(input, label, num_classes):
Args:
input (Variable): A Tensor of prediction results for semantic labels with type int32 or int64.
label (Variable):
A Tensor of ground truth labels with type int32 or int64.
label (Variable):
A Tensor of ground truth labels with type int32 or int64.
Its shape should be the same as input.
num_classes (int): The possible number of labels.
Returns:
mean_iou (Variable): A Tensor representing the mean intersection-over-union with shape [1].
...
...
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