# How to use l1_l2 regularization to a Deep Learning Model in Keras

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 L1 regularization and L2 regularization. L1 regularization adds a penalty term to the loss function that is proportional to the absolute value of the weights, while L2 regularization adds a penalty term that is proportional to the square of the magnitude of the weights. Combining both L1 and L2 regularization is called L1-L2 regularization.

Keras is a popular deep learning library that makes it easy to build and train neural networks. To use L1-L2 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 L1-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 l1_l2 from keras.regularizers. The regularizer class takes two arguments, l1 and l2, which are the strengths of L1 and L2 regularization respectively. 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 L1-L2 regularization is applied to the weight matrix of the layer, it does not apply to the bias.

Once you have added the L1-L2 regularization to your model, you can then compile and train the model as usual.

In summary, to use L1-L2 regularization to a deep learning model in Keras, you need to import the library, create a new model using the Sequential() function, add the L1-L2 regularization using the kernel_regularizer argument when creating a dense layer. The kernel_regularizer argument takes an instance of regularizer class with two arguments, l1 and l2, to define the strengths of L1 and L2 regularization respectively, and then compile and train the model as usual.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to use l1_l2 regularization to a Deep Learning Model in Keras.

### What should I learn from this recipe?

You will learn:

- How to code a keras and tensorflow model in Python.
- How to setup a sequential deep learning model in Python.
- How to add weight regularization in a deep learning model.
- How to create simulated data using scikit-learn.
- How to create training and testing dataset using scikit-learn.
- How to train a tensorflow and keras model.
- How to report confusion matrix.

How to use l1_l2 regularization to a Deep Learning Model in Keras:

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