Return number of epochs for EarlyStopping callback in Keras

Question

Is there any way to return the number of epochs after which the training was stopped in Keras when using the EarlyStopping callback?

I can get the log of the training and validation loss and compute the number of epochs myself using the patience parameter, but is there a more direct way?

Answer 1

Use EarlyStopping.stopped_epoch attribute: remember the callback in a separate variable, say callback, and check callback.stopped_epoch after the training stopped.

Answer 2

You can also leverage History() call back to find out the number of epochs it was ran for. Ex:

from keras.callbacks import History, EarlyStopping

history = History()
callback = [history, EarlyStopping(monitor='val_loss', patience=5, verbose=1, min_delta=1e-4)]

history = model.fit_generator(...., callbacks=callbacks)
number_of_epochs_it_ran = len(history.history['loss'])

Answer 3

Subtracting the patience value from the total number of epochs - as suggested in this comment - might not work in some situations. For instance, if you set epochs=100 and patience=20, if the best accuracy/loss value is found at epoch 90, the training will stop at epoch 100. So with this approach you would get a wrong number (100-20 = 80).

Moreover, as noted in this comment, using EarlyStopping.stopped_epoch only gives you the epoch when the training has been stopped, but NOT the epoch when the best weights are saved. This is particularly useful when you set save_best_weights=True or rely on ModelCheckpoint to save the best model before stopping the training.

Therefore my solution is to get the index of model history array, with the best value. Assuming that the metric used is the validation accuracy, relying on numpy, here is some code:

import numpy as np

model.fit(...)
hist = model.history.history['val_acc']
n_epochs_best = np.argmax(hist)