Keras Callbacks Guide

Keras callbacks are a set of tools that allow you to customize the training process. They are particularly useful for tasks like early stopping, model checkpointing, learning rate adjustments, and more. Below is an overview of the most common Keras callbacks and how they can help you in your deep learning projects.

Common Callbacks

• EarlyStopping: Prevents overfitting by stopping training when the model’s performance on a validation set stops improving.
• ModelCheckpoint: Saves the model at the end of each epoch, or when a new best performance is achieved.
• ReduceLROnPlateau: Reduces the learning rate when a metric has stopped improving.
• LearningRateScheduler: Adjusts the learning rate at specified intervals.

EarlyStopping

EarlyStopping is a great way to prevent overfitting. It works by monitoring the validation loss and stopping the training process if there is no improvement for a specified number of epochs.

from keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', patience=3)

ModelCheckpoint

ModelCheckpoint is useful for saving the best model during training. You can specify the frequency at which the model should be saved and the filename pattern.

from keras.callbacks import ModelCheckpoint

checkpoint = ModelCheckpoint('best_model.h5', monitor='val_loss', save_best_only=True)

ReduceLROnPlateau

ReduceLROnPlateau adjusts the learning rate when the training loss on the validation set does not improve for a certain number of epochs.

from keras.callbacks import ReduceLROnPlateau

reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=2, min_lr=0.001)

LearningRateScheduler

LearningRateScheduler allows you to adjust the learning rate at specific intervals using a schedule.

from keras.callbacks import LearningRateScheduler
import numpy as np

def scheduler(epoch, lr):
    if epoch < 10:
        return lr
    else:
        return lr * np.exp(-0.1)

lr_scheduler = LearningRateScheduler(scheduler)

For more detailed information on each callback, check out the official Keras documentation.

If you're interested in diving deeper into Keras callbacks and other deep learning topics, don't forget to explore our Deep Learning Resources.

Deep Learning with Keras