Overfitting and underfitting are two issues that can occur in deep learning models, which can lead to poor performance.
Underfitting occurs when the model is too simple to capture the complexity of the data and it fails to learn the underlying patterns in the data. This results in poor performance on both the training and test data. In other words, the model doesn’t generalize well to unseen data. This can often be seen in cases where the training data is not representative of the entire dataset, or when the model is not complex enough.
Overfitting, on the other hand, occurs when the model is too complex and starts to fit the noise in the training data rather than the underlying patterns. As a result, the model performs very well on the training data but performs poorly on the test data. In other words, the model doesn’t generalize well to unseen data. Overfitting often occurs when the model has too many parameters or when the training dataset is too small to represent the entire dataset.
To prevent underfitting, it is often necessary to increase the complexity of the model. This can be done by adding additional layers or by increasing the number of nodes in the layers. Additionally, increasing the number of epochs or increasing the size of the training dataset can also help to prevent underfitting.
To prevent overfitting, there are several techniques that can be used. One popular method is to use regularization techniques, such as L1 or L2 regularization, which help to prevent the model from fitting the noise in the training data. Dropout is another technique that can be used to prevent overfitting by randomly dropping out units during training, which forces the model to learn more robust features. Another technique is early stopping, which stops training the model as soon as the validation loss starts to increase.
In Keras, regularization techniques can be added to the layers using the ‘kernel_regularizer‘ and ‘bias_regularizer‘ parameters. Dropout can be added using the ‘Dropout‘ layer. Early stopping can be implemented using the ‘EarlyStopping‘ callback. By applying these techniques, the model can be trained to generalize well to unseen data and prevent underfitting and overfitting.