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dfa50c3c
编写于
9月 28, 2020
作者:
littletomatodonkey
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电子邮件补丁
差异文件
fix reg
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7c516240
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1
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python/paddle/regularizer.py
python/paddle/regularizer.py
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未找到文件。
python/paddle/regularizer.py
浏览文件 @
dfa50c3c
...
@@ -21,7 +21,7 @@ class L1Decay(fluid.regularizer.L1Decay):
...
@@ -21,7 +21,7 @@ class L1Decay(fluid.regularizer.L1Decay):
"""
"""
Implement the L1 Weight Decay Regularization, which encourages the weights to be sparse.
Implement the L1 Weight Decay Regularization, which encourages the weights to be sparse.
It can be set in :ref:`api_
fluid
_ParamAttr` or ``optimizer`` (such as :ref:`api_paddle_optimizer_Momentum` ).
It can be set in :ref:`api_
paddle
_ParamAttr` or ``optimizer`` (such as :ref:`api_paddle_optimizer_Momentum` ).
When set in ``ParamAttr`` , it only takes effect for trainable parameters in this layer. When set in
When set in ``ParamAttr`` , it only takes effect for trainable parameters in this layer. When set in
``optimizer`` , it takes effect for all trainable parameters. When set together, ``ParamAttr`` has
``optimizer`` , it takes effect for all trainable parameters. When set together, ``ParamAttr`` has
higher priority than ``optimizer`` , which means that for a trainable parameter, if regularizer is defined
higher priority than ``optimizer`` , which means that for a trainable parameter, if regularizer is defined
...
@@ -85,7 +85,7 @@ class L2Decay(fluid.regularizer.L2Decay):
...
@@ -85,7 +85,7 @@ class L2Decay(fluid.regularizer.L2Decay):
"""
"""
Implement the L2 Weight Decay Regularization, which helps to prevent the model over-fitting.
Implement the L2 Weight Decay Regularization, which helps to prevent the model over-fitting.
It can be set in :ref:`api_
fluid
_ParamAttr` or ``optimizer`` (such as :ref:`api_paddle_optimizer_Momentum` ).
It can be set in :ref:`api_
paddle
_ParamAttr` or ``optimizer`` (such as :ref:`api_paddle_optimizer_Momentum` ).
When set in ``ParamAttr`` , it only takes effect for trainable parameters in this layer. When set in
When set in ``ParamAttr`` , it only takes effect for trainable parameters in this layer. When set in
``optimizer`` , it takes effect for all trainable parameters. When set together, ``ParamAttr`` has
``optimizer`` , it takes effect for all trainable parameters. When set together, ``ParamAttr`` has
higher priority than ``optimizer`` , which means that for a trainable parameter, if regularizer is defined
higher priority than ``optimizer`` , which means that for a trainable parameter, if regularizer is defined
...
...
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