19 lines
679 B
Markdown
19 lines
679 B
Markdown
## Usage of regularizers
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Regularizers allow to apply penalties on network parameters during optimization.
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The keyword arguments used for passing penalties to parameters in a layer will depend on the layer.
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In the `Dense` layer it is simply `W_regularizer` for the main weights matrix, and `b_regularizer` for the bias.
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```python
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from keras.regularizers import l2
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model.add(Dense(64, 64, W_regularizer = l2(.01)))
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```
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## Available penalties
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- __l1__(l=0.01): L1 regularization penalty, also known as LASSO
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- __l2__(l=0.01): L2 regularization penalty, also known as weight decay, or Ridge
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- __l1l2__(l1=0.01, l2=0.01): L1-L2 regularization penalty, also known as ElasticNet
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