Keras reduces the learning rate using callbacks such as ReduceLROnPlateau, which monitors a metric (e.g., validation loss) during training. If the metric stops improving for a specified number of epochs, the learning rate is reduced by a factor.
This dynamic adjustment helps the model converge more efficiently by taking smaller steps in later stages of training, preventing overshooting the optimal solution.
Developers can customize parameters like the reduction factor, patience, and minimum learning rate to fine-tune the training process for their specific use case.