我找到了答案。我调查了Keras的资源，并找到了EarlyStopping的代码。我基于此进行了自己的回调：
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和用法：
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)
