Learn By Example | How to add a Weight Regularization (l2) to a Deep Learning Model in Keras?
Weight regularization is a technique used in deep learning to prevent overfitting, which occurs when a model is too complex and is able to memorize the training data instead of learning from it. One common type of weight regularization is called L2 regularization, which adds a penalty term to the model’s cost function that is proportional to the sum of the squares of the weights.
In Keras, a deep learning library for Python, adding L2 regularization to a model is quite easy. First, you will need to import the necessary modules from Keras. Next, you will need to create your model using the Sequential class, which allows you to add layers to your model one by one.
After creating your model, you will need to add a dense layer, which is a type of layer that is fully connected to the previous layer. You can specify the number of neurons in this layer and the activation function to use. The activation function is a mathematical function that is applied to the output of the layer to introduce non-linearity into the model.
To add L2 regularization to your model, you will need to create an instance of the regularizers class and specify the value of the L2 regularization parameter, also known as the lambda parameter. This value determines how much the model will be penalized for having large weights.
Finally, you will need to compile your model by specifying the optimizer, loss function, and metrics to use. The optimizer is the algorithm used to update the weights of the model based on the cost function. The loss function is the function that measures the difference between the predicted and actual values. The metrics are used to evaluate the performance of the model.
In summary, adding L2 regularization to a deep learning model in Keras is a simple process that involves importing the necessary modules, creating the model, adding a dense layer, creating an instance of the regularizers class, and specifying the L2 regularization parameter. Finally, the model needs to be compiled by specifying the optimizer, loss function, and metrics.
In this Applied Machine Learning & Data Science Recipe, the reader will find the practical use of applied machine learning and data science in Python & R programming: Learn By Example | How to add a Weight Regularization (l2) to a Deep Learning Model in Keras?
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