Additional regularization technique – Dropout
Regularization is built into the Early Stopping monitor because a validation test is used during each epoch to score against the training set. The idea is that even if the training set continues to improve, the model will stop building after the validation ceases to improve within the callback patience.
It’s important to examine additional regularization techniques so that you can build even larger neural networks without overfitting the data.
Another very popular regularization technique widely used in neural networks is called the Dropout. Given multiple nodes in multiple layers result in thousands or millions of weights, neural networks can easily overfit the training set.
The idea behind Dropout is to randomly drop some nodes altogether. In densely connected networks, since all nodes in one layer are connected to all nodes in the next layer, any node may be eliminated except the last.
Dropout works in code...