How to prevent overfitting in Keras?

5 months ago

2 minutes

In Keras, there are several methods that can be used to prevent overfitting.

Utilizing regularization by adding regularization terms such as L1 or L2 regularization in the layers of a neural network can reduce model complexity, leading to a lower risk of overfitting.
Utilizing Dropout: Incorporating a Dropout layer in a neural network involves randomly setting a portion of neuron outputs to zero, reducing dependencies between neurons to prevent overfitting.
Early Stopping: monitor the performance of the validation set during training and stop the training when the validation set performance no longer improves, in order to prevent the model from overfitting on the training set.
Data Augmentation involves making certain transformations to training data, such as rotation, translation, and flipping, in order to increase the diversity of the training data and reduce overfitting.
Cross validation involves dividing the dataset into multiple subsets, training and validating repeatedly, and averaging the results to accurately assess the model’s performance and prevent overfitting.

By combining the above methods, it is possible to effectively prevent overfitting of the model during the training process.