Currently I use the following code:
callbacks = [
EarlyStopping(monitor='val_loss', patience=2, verbose=0),
ModelCheckpoint(kfold_weights_path, monitor='val_loss', save_best_only=True, verbose=0),
]
model.fit(X_train.astype('float32'), Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
shuffle=True, verbose=1, validation_data=(X_valid, Y_valid),
callbacks=callbacks)
It tells Keras to stop training when loss didn't improve for 2 epochs. But I want to stop training after loss became smaller than some constant "THR":
if val_loss < THR:
break
I've seen in documentation there are possibility to make your own callback: http://keras.io/callbacks/ But nothing found how to stop training process. I need an advice.
I found the answer. I looked into Keras sources and find out code for EarlyStopping. I made my own callback, based on it:
class EarlyStoppingByLossVal(Callback):
def __init__(self, monitor='val_loss', value=0.00001, verbose=0):
super(Callback, self).__init__()
self.monitor = monitor
self.value = value
self.verbose = verbose
def on_epoch_end(self, epoch, logs={}):
current = logs.get(self.monitor)
if current is None:
warnings.warn("Early stopping requires %s available!" % self.monitor, RuntimeWarning)
if current < self.value:
if self.verbose > 0:
print("Epoch %05d: early stopping THR" % epoch)
self.model.stop_training = True
And usage:
callbacks = [
EarlyStoppingByLossVal(monitor='val_loss', value=0.00001, verbose=1),
# EarlyStopping(monitor='val_loss', patience=2, verbose=0),
ModelCheckpoint(kfold_weights_path, monitor='val_loss', save_best_only=True, verbose=0),
]
model.fit(X_train.astype('float32'), Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
shuffle=True, verbose=1, validation_data=(X_valid, Y_valid),
callbacks=callbacks)