How do you handle overfitting in a Keras model?

Overfitting is a common problem in machine learning, and it can be especially problematic in deep learning models. To handle overfitting in a Keras model, there are several techniques that can be used.

The first technique is to use regularization. Regularization is a technique that adds a penalty to the model for having too many parameters. This penalty helps to reduce the complexity of the model and prevent it from overfitting. Common regularization techniques used in Keras models include L1 and L2 regularization, dropout, and early stopping.

The second technique is to use data augmentation. Data augmentation is a technique that creates new data points from existing data points. This helps to reduce overfitting by providing the model with more data points to learn from. Common data augmentation techniques used in Keras models include image flipping, rotation, and scaling.

The third technique is to use cross-validation. Cross-validation is a technique that splits the data into training and validation sets. The model is then trained on the training set and evaluated on the validation set. This helps to prevent overfitting by providing a more accurate evaluation of the model’s performance.

Finally, the fourth technique is to use hyperparameter tuning. Hyperparameter tuning is a technique that adjusts the model’s hyperparameters to optimize its performance. This helps to reduce overfitting by finding the optimal combination of hyperparameters for the model. Common hyperparameter tuning techniques used in Keras models include grid search and random search.