Which parameters should be used for early stopping?
I'm training a neural network for my project using Keras. Keras has provided a function for early stopping. May I know what parameters should be observed to avoid my neural network from overfitting by using early stopping?
https://i.stack.imgur.com/wkK5f.gif
Early stopping is basically stopping the training once your loss starts to increase (or in other words validation accuracy starts to decrease). According to documents it is used as follows;
keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=0,
patience=0,
verbose=0, mode='auto')
Values depends on your implementation (problem, batch size etc...) but generally to prevent overfitting I would use;
Monitor the validation loss (need to use cross validation or at least train/test sets) by setting the monitor argument to 'val_loss'. min_delta is a threshold to whether quantify a loss at some epoch as improvement or not. If the difference of loss is below min_delta, it is quantified as no improvement. Better to leave it as 0 since we're interested in when loss becomes worse. patience argument represents the number of epochs before stopping once your loss starts to increase (stops improving). This depends on your implementation, if you use very small batches or a large learning rate your loss zig-zag (accuracy will be more noisy) so better set a large patience argument. If you use large batches and a small learning rate your loss will be smoother so you can use a smaller patience argument. Either way I'll leave it as 2 so I would give the model more chance. verbose decides what to print, leave it at default (0). mode argument depends on what direction your monitored quantity has (is it supposed to be decreasing or increasing), since we monitor the loss, we can use min. But let's leave keras handle that for us and set that to auto
So I would use something like this and experiment by plotting the error loss with and without early stopping.
keras.callbacks.EarlyStopping(monitor='val_loss',
min_delta=0,
patience=2,
verbose=0, mode='auto')
For possible ambiguity on how callbacks work, I'll try to explain more. Once you call fit(... callbacks=[es]) on your model, Keras calls given callback objects predetermined functions. These functions can be called on_train_begin, on_train_end, on_epoch_begin, on_epoch_end and on_batch_begin, on_batch_end. Early stopping callback is called on every epoch end, compares the best monitored value with the current one and stops if conditions are met (how many epochs have past since the observation of the best monitored value and is it more than patience argument, the difference between last value is bigger than min_delta etc..).
As pointed by @BrentFaust in comments, model's training will continue until either Early Stopping conditions are met or epochs parameter (default=10) in fit() is satisfied. Setting an Early Stopping callback will not make the model to train beyond its epochs parameter. So calling fit() function with a larger epochs value would benefit more from Early Stopping callback.
Here's an example of EarlyStopping from another project, AutoKeras (https://autokeras.com/), an automated machine learning (AutoML) library. The library sets two EarlyStopping parameters: patience=10 and min_delta=1e-4
the default quantity to monitor for both AutoKeras and Keras is the val_loss:
https://github.com/keras-team/keras/blob/cb306b4cc446675271e5b15b4a7197efd3b60c34/keras/callbacks.py#L1748 https://autokeras.com/image_classifier/
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min_deltais a threshold to whether quantify the change in monitored value as an improvement or not. So yes, if we givemonitor = 'val_loss'then it would refer to the difference between current validation loss and the previous validation loss. In practice, if you givemin_delta=0.1a decrease in validation loss (current - previous) smaller than 0.1 would not quantify, thus would stop the training (if you havepatience = 0).callbacks=[EarlyStopping(patience=2)]has no effect, unless epochs is given tomodel.fit(..., epochs=max_epochs).epoch=1in a for loop (for various use cases) in which this callback would fail. If there is ambiguity in my answer, I will try to put it in a better way.restore_best_weightsargument (not on the documentation yet), which loads the model with best weights after training. But, for your purposes I would useModelCheckpointcallback withsave_best_onlyargument. You can check the documentation, it is straight forward to use but you need to manually load the best weights after training.