How to tell Keras stop training based on loss value?

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)

The keras.callbacks.EarlyStopping callback does have a min_delta argument. From Keras documentation:

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.

One solution is to call model.fit(nb_epoch=1, ...) inside a for loop, then you can put a break statement inside the for loop and do whatever other custom control flow you want.