python-kerasHow can I implement early stopping using Python and Keras?

Early stopping is a popular technique used to prevent overfitting in deep learning models. It can be implemented using Python and Keras by using the EarlyStopping callback. This callback monitors the model's performance, and when a chosen metric (e.g. validation loss) no longer improves, the training process is stopped.

Example code

from keras.callbacks import EarlyStopping

es = EarlyStopping(monitor='val_loss', patience=3)
model.fit(X_train, y_train, callbacks=[es], epochs=100)

The code above uses the EarlyStopping callback to monitor the validation loss of the model. The patience argument specifies how many epochs to wait before stopping training when the validation loss has not improved.

Code explanation

• EarlyStopping: callback used to monitor the model's performance and stop training when a chosen metric no longer improves
• monitor: the metric to monitor (e.g. validation loss)
• patience: the number of epochs to wait before stopping training when the validation loss has not improved

Helpful links

• Keras EarlyStopping documentation

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