提交 dfa50c3c 编写于 作者: littletomatodonkey's avatar littletomatodonkey

fix reg

上级 7c516240
...@@ -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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