Early stopping is a technique used to stop training a deep learning model when the performance of the model on a validation dataset stops improving. The idea behind early stopping is to prevent overfitting by stopping the training process before the model becomes too complex and starts memorizing the training data instead of generalizing to new data.

In Keras, you can use the EarlyStopping callback to set up early stopping for your model. A callback is a function that is called at certain points during the training process. The EarlyStopping callback allows you to specify a metric to monitor, such as accuracy or loss, and a patience value, which is the number of epochs to wait for the metric to improve before stopping the training.

When you set up early stopping, you also need to provide a validation dataset for the model to evaluate its performance. The validation dataset is used to calculate the metric that is being monitored for early stopping.

To set up early stopping in a deep learning model in Keras, you first need to import the library, then create a new model using the Sequential() function. Next, you can compile the model using the compile() function as usual. To set up early stopping, you need to create an instance of the EarlyStopping callback and pass it to the fit() function when training the model.

The EarlyStopping callback takes several arguments, such as:

• monitor: the metric to monitor for early stopping.
• patience: the number of epochs to wait for the metric to improve before stopping the training.
• mode: the mode of the EarlyStopping, “auto” or “min” or “max”
• restore_best_weights: if set to true, the best weight will be restored

Once you have set up early stopping, you can then train the model using the fit() function, which takes the input data, corresponding output labels and validation data as arguments. During the training, the model will evaluate its performance on the validation dataset, and if the performance of the metric stops improving for the number of epochs specified by the patience value, the training process will be stopped.

In summary, to set up early stopping in a deep learning model in Keras, you need to import the library, create a new model using the Sequential() function, compile the model using the compile() function, and set up the EarlyStopping callback by passing it to the fit() function when training the model. The EarlyStopping callback takes several arguments, such as monitor, patience, mode and restore_best_weights, which are used to configure the early stopping.