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32ae8e81
编写于
4年前
作者:
Z
zhupengyang
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GitHub
4年前
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leaky_relu, log_softmax, hardshrink formula format (#26720)
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-58
python/paddle/nn/functional/activation.py
python/paddle/nn/functional/activation.py
+17
-17
python/paddle/nn/layer/activation.py
python/paddle/nn/layer/activation.py
+41
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python/paddle/nn/functional/activation.py
浏览文件 @
32ae8e81
...
...
@@ -168,13 +168,13 @@ def hardshrink(x, threshold=0.5, name=None):
.. math::
hardshrink(x)=
\
left
\{
\b
egin{aligned}
&x, & & if \
x > threshold
\\
&x, & & if \
x < -threshold
\\
&0, & & if \ others
\end{aligned}
\r
ight.
\
\
left
\
\
{
\
\
begin{aligned}
&x, & & if
\
\
x > threshold
\\
\\
&x, & & if
\
\
x < -threshold
\\
\\
&0, & & if
\
\
others
\
\
end{aligned}
\
\
right.
Args:
x (Tensor): The input Tensor with data type float32, float64.
...
...
@@ -391,14 +391,14 @@ def leaky_relu(x, negative_slope=0.01, name=None):
"""
leaky_relu activation
.. math:
leaky_relu(x)=
\
left
\{
\b
egin{aligned}
&x, & & if \
x >= 0
\\
&negative\_slope * x, & & otherwise
\\
\end{aligned}
\
r
ight.
\\
.. math:
:
leaky
\\
_relu(x)=
\
\
left
\
\
{
\
\
begin{aligned}
&x, & & if
\
\
x >= 0
\\
\\
&negative\_slope * x, & & otherwise
\\
\\
\
\
end{aligned}
\
\
right.
\\
\\
Args:
x (Tensor): The input Tensor with data type float32, float64.
...
...
@@ -1033,8 +1033,8 @@ def log_softmax(x, axis=-1, dtype=None, name=None):
.. math::
Out[i, j] = log(softmax(x))
= log(
\f
rac{\exp(X[i, j])}{
\sum_j(exp(X[i, j])})
log
\\
_softmax[i, j] = log(softmax(x))
= log(
\\
frac{\exp(X[i, j])}{
\
\
sum_j(exp(X[i, j])})
Parameters:
x (Tensor): The input Tensor with data type float32, float64.
...
...
This diff is collapsed.
Click to expand it.
python/paddle/nn/layer/activation.py
浏览文件 @
32ae8e81
...
...
@@ -144,13 +144,13 @@ class Hardshrink(layers.Layer):
.. math::
hardshrink(x)=
\
left
\{
\b
egin{aligned}
&x, & & if \
x > threshold
\\
&x, & & if \
x < -threshold
\\
&0, & & if \ others
\end{aligned}
\r
ight.
\
\
left
\
\
{
\
\
begin{aligned}
&x, & & if
\
\
x > threshold
\\
\\
&x, & & if
\
\
x < -threshold
\\
\\
&0, & & if
\
\
others
\
\
end{aligned}
\
\
right.
Parameters:
threshold (float, optional): The value of threshold for hardthrink. Default is 0.5
...
...
@@ -165,14 +165,14 @@ class Hardshrink(layers.Layer):
.. code-block:: python
import paddle
import numpy as np
import paddle
import numpy as np
paddle.disable_static()
paddle.disable_static()
x = paddle.to_tensor(np.array([-1, 0.3, 2.5]))
m = paddle.nn.Hardshrink()
out = m(x) # [-1., 0., 2.5]
x = paddle.to_tensor(np.array([-1, 0.3, 2.5]))
m = paddle.nn.Hardshrink()
out = m(x) # [-1., 0., 2.5]
"""
def
__init__
(
self
,
threshold
=
0.5
,
name
=
None
):
...
...
@@ -598,15 +598,15 @@ class LeakyReLU(layers.Layer):
"""
Leaky ReLU Activation.
.. math:
.. math:
:
LeakyReLU(x)=
\
left
\{
\b
egin{aligned}
&x, & & if \
x >= 0
\\
&negative\_slope * x, & & otherwise
\\
\end{aligned}
\
r
ight.
\\
\
\
left
\
\
{
\
\
begin{aligned}
&x, & & if
\
\
x >= 0
\\
\\
&negative\_slope * x, & & otherwise
\\
\\
\
\
end{aligned}
\
\
right.
\\
\\
Parameters:
negative_slope (float, optional): Slope of the activation function at
...
...
@@ -1015,7 +1015,7 @@ class LogSoftmax(layers.Layer):
.. math::
Out[i, j] = log(softmax(x))
= log(
\
f
rac{\exp(X[i, j])}{
\sum_j(exp(X[i, j])})
= log(
\
\
frac{\exp(X[i, j])}{
\
\
sum_j(exp(X[i, j])})
Parameters:
axis (int, optional): The axis along which to perform log_softmax
...
...
@@ -1032,26 +1032,26 @@ class LogSoftmax(layers.Layer):
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
x = np.array([[[-2.0, 3.0, -4.0, 5.0],
[3.0, -4.0, 5.0, -6.0],
[-7.0, -8.0, 8.0, 9.0]],
[[1.0, -2.0, -3.0, 4.0],
[-5.0, 6.0, 7.0, -8.0],
[6.0, 7.0, 8.0, 9.0]]])
m = paddle.nn.LogSoftmax()
x = paddle.to_tensor(x)
out = m(x)
# [[[ -7.1278396 -2.1278396 -9.127839 -0.12783948]
# [ -2.1270514 -9.127051 -0.12705144 -11.127051 ]
# [-16.313261 -17.313261 -1.3132617 -0.31326184]]
# [[ -3.0518122 -6.051812 -7.051812 -0.051812 ]
# [-12.313267 -1.3132664 -0.3132665 -15.313267 ]
# [ -3.4401896 -2.4401896 -1.4401896 -0.44018966]]]
import paddle
import numpy as np
paddle.disable_static()
x = np.array([[[-2.0, 3.0, -4.0, 5.0],
[3.0, -4.0, 5.0, -6.0],
[-7.0, -8.0, 8.0, 9.0]],
[[1.0, -2.0, -3.0, 4.0],
[-5.0, 6.0, 7.0, -8.0],
[6.0, 7.0, 8.0, 9.0]]])
m = paddle.nn.LogSoftmax()
x = paddle.to_tensor(x)
out = m(x)
# [[[ -7.1278396 -2.1278396 -9.127839 -0.12783948]
# [ -2.1270514 -9.127051 -0.12705144 -11.127051 ]
# [-16.313261 -17.313261 -1.3132617 -0.31326184]]
# [[ -3.0518122 -6.051812 -7.051812 -0.051812 ]
# [-12.313267 -1.3132664 -0.3132665 -15.313267 ]
# [ -3.4401896 -2.4401896 -1.4401896 -0.44018966]]]
"""
def
__init__
(
self
,
axis
=-
1
,
name
=
None
):
...
...
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Click to expand it.
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