How to tell Keras stop training based on loss value?

by

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)

Leave a Comment