How to add a Weight Regularization (l2) to a Deep Learning Model in Keras

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In deep learning, weight regularization is a technique used to prevent overfitting by adding a penalty term to the loss function. There are different types of weight regularization, but one of the most common is L2 regularization, also known as weight decay. L2 regularization adds a penalty term to the loss function that is proportional to the square of the magnitude of the weights. This helps to prevent the weights from becoming too large, which can lead to overfitting.

Keras is a popular deep learning library that makes it easy to build and train neural networks. To add L2 weight regularization to a model in Keras, you first need to import the library, then create a new model using the Sequential() function. Next, you can add the L2 regularization to the model by using the kernel_regularizer argument when creating a dense layer.

The kernel_regularizer argument takes an instance of a regularizer class, such as l2 from keras.regularizers. The regularizer class takes a single argument, which is the strength of the regularization, often denoted by lambda. A smaller lambda value corresponds to a weaker regularization and a larger lambda value corresponds to a stronger regularization.

It is important to note that L2 regularization is applied to the weight matrix of the layer, it does not apply to the bias.

Once you have added the L2 regularization to your model, you can then compile and train the model as usual.

In summary, to add L2 weight regularization to a deep learning model in Keras, you need to import the library, create a new model using the Sequential() function, add the L2 regularization using the kernel_regularizer argument when creating a dense layer, and then compile and train the model as usual. The kernel_regularizer argument takes an instance of regularizer class and lambda value to define the strength of regularization.

 

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How to add a Weight Regularization (l2) to a Deep Learning Model in Keras:



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