Early Stopping is to stop the Training of Neural Networks at the Right Time or Stop training when a monitored quantity has stopped improving.
A major concern with training neural networks is in the choice of the number of training epochs to use.
Too many epochs can lead to overfitting of the training dataset, whereas too few may result in an underfit model. Early stopping is a method that allows you to specify an arbitrary large number of training epochs and stop training once the model performance stops improving on a hold out validation dataset.
EarlyStopping:
keras.callbacks.EarlyStopping(monitor=’val_loss’, min_delta=0, patience=0, verbose=0, mode=’auto’, baseline=None, restore_best_weights=False)
Stop training when a monitored quantity has stopped improving.
Arguments:
monitor: quantity to be monitored.
min_delta: minimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than min_delta, will count as no improvement.
patience: number of epochs with no improvement after which training will be stopped.
verbose: verbosity mode.
mode: one of {auto, min, max}. In min mode, training will stop when the quantity monitored has stopped decreasing; in max mode it will stop when the quantity monitored has stopped increasing; in auto mode, the direction is automatically inferred from the name of the monitored quantity.
baseline: Baseline value for the monitored quantity to reach. Training will stop if the model doesn’t show improvement over the baseline.
restore_best_weights: whether to restore model weights from the epoch with the best value of the monitored quantity. If False, the model weights obtained at the last step of training are used.
The complete example is listed below.
from sklearn.datasets import make_moons
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping
from matplotlib import pyplot
generate 2d classification dataset
X, y = make_moons(n_samples=100, noise=0.2, random_state=1)
split into train and test
n_train = 30
trainX, testX = X[:n_train, :], X[n_train:, :]
trainy, testy = y[:n_train], y[n_train:]
define model
model = Sequential()
model.add(Dense(500, input_dim=2, activation=’relu’))
model.add(Dense(1, activation=’sigmoid’))
model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])
patient early stopping
es = EarlyStopping(monitor=’val_loss’, mode=’min’, verbose=1, patience=200)
fit model
history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=4000, verbose=0, callbacks=[es])
evaluate the model
_, train_acc = model.evaluate(trainX, trainy, verbose=0) _, test_acc = model.evaluate(testX, testy, verbose=0)
print(‘Train: %.3f, Test: %.3f’ % (train_acc, test_acc))
plot training history
pyplot.plot(history.history[‘loss’], label=’train’)
pyplot.plot(history.history[‘val_loss’], label=’test’)
pyplot.legend()
pyplot.show()
Example Reference: https://machinelearningmastery.com/
llamasearch 